diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..495dd55cba83598865d0f4fa4657e0a22299335f 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +diffsynth/tokenizer_configs/kolors/tokenizer/vocab.txt filter=lfs diff=lfs merge=lfs -text diff --git a/DiffSynth_Studio.py b/DiffSynth_Studio.py new file mode 100644 index 0000000000000000000000000000000000000000..855e5a5b0f5a64d513861fff8a86a4c11837fe7b --- /dev/null +++ b/DiffSynth_Studio.py @@ -0,0 +1,15 @@ +# Set web page format +import streamlit as st +st.set_page_config(layout="wide") +# Diasble virtual VRAM on windows system +import torch +torch.cuda.set_per_process_memory_fraction(0.999, 0) + + +st.markdown(""" +# DiffSynth Studio + +[Source Code](https://github.com/Artiprocher/DiffSynth-Studio) + +Welcome to DiffSynth Studio. +""") diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..0e5a49f5f70af9e9d37278f72315d1b1afd34895 --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [2023] [Zhongjie Duan] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md index 47b7580e42d5aedf6c90a12f2d85143e13c13d2b..fbe42e13bfd6b39f86eb2aac51a3eb3aff84c7bb 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,117 @@ ---- -title: Diffutoon -emoji: 🏃 -colorFrom: green -colorTo: yellow -sdk: gradio -sdk_version: 4.39.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +# DiffSynth Studio + + +## Introduction + +DiffSynth Studio is a Diffusion engine. We have restructured architectures including Text Encoder, UNet, VAE, among others, maintaining compatibility with models from the open-source community while enhancing computational performance. We provide many interesting features. Enjoy the magic of Diffusion models! + +Until now, DiffSynth Studio has supported the following models: + +* [ExVideo](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1) +* [Kolors](https://huggingface.co/Kwai-Kolors/Kolors) +* [Stable Diffusion 3](https://huggingface.co/stabilityai/stable-diffusion-3-medium) +* [Stable Video Diffusion](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt) +* [Hunyuan-DiT](https://github.com/Tencent/HunyuanDiT) +* [RIFE](https://github.com/hzwer/ECCV2022-RIFE) +* [ESRGAN](https://github.com/xinntao/ESRGAN) +* [Ip-Adapter](https://github.com/tencent-ailab/IP-Adapter) +* [AnimateDiff](https://github.com/guoyww/animatediff/) +* [ControlNet](https://github.com/lllyasviel/ControlNet) +* [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) +* [Stable Diffusion](https://huggingface.co/runwayml/stable-diffusion-v1-5) + +## News + + +- **June 21, 2024.** 🔥🔥🔥 We propose ExVideo, a post-tuning technique aimed at enhancing the capability of video generation models. We have extended Stable Video Diffusion to achieve the generation of long videos up to 128 frames. + - [Project Page](https://ecnu-cilab.github.io/ExVideoProjectPage/) + - Source code is released in this repo. See [`examples/ExVideo`](./examples/ExVideo/). + - Models are released on [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1) and [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1). + - Technical report is released on [arXiv](https://arxiv.org/abs/2406.14130). + - You can try ExVideo in this [Demo](https://huggingface.co/spaces/modelscope/ExVideo-SVD-128f-v1)! + +- **June 13, 2024.** DiffSynth Studio is transferred to ModelScope. The developers have transitioned from "I" to "we". Of course, I will still participate in development and maintenance. + +- **Jan 29, 2024.** We propose Diffutoon, a fantastic solution for toon shading. + - [Project Page](https://ecnu-cilab.github.io/DiffutoonProjectPage/) + - The source codes are released in this project. + - The technical report (IJCAI 2024) is released on [arXiv](https://arxiv.org/abs/2401.16224). + +- **Dec 8, 2023.** We decide to develop a new Project, aiming to release the potential of diffusion models, especially in video synthesis. The development of this project is started. + +- **Nov 15, 2023.** We propose FastBlend, a powerful video deflickering algorithm. + - The sd-webui extension is released on [GitHub](https://github.com/Artiprocher/sd-webui-fastblend). + - Demo videos are shown on Bilibili, including three tasks. + - [Video deflickering](https://www.bilibili.com/video/BV1d94y1W7PE) + - [Video interpolation](https://www.bilibili.com/video/BV1Lw411m71p) + - [Image-driven video rendering](https://www.bilibili.com/video/BV1RB4y1Z7LF) + - The technical report is released on [arXiv](https://arxiv.org/abs/2311.09265). + - An unofficial ComfyUI extension developed by other users is released on [GitHub](https://github.com/AInseven/ComfyUI-fastblend). + +- **Oct 1, 2023.** We release an early version of this project, namely FastSDXL. A try for building a diffusion engine. + - The source codes are released on [GitHub](https://github.com/Artiprocher/FastSDXL). + - FastSDXL includes a trainable OLSS scheduler for efficiency improvement. + - The original repo of OLSS is [here](https://github.com/alibaba/EasyNLP/tree/master/diffusion/olss_scheduler). + - The technical report (CIKM 2023) is released on [arXiv](https://arxiv.org/abs/2305.14677). + - A demo video is shown on [Bilibili](https://www.bilibili.com/video/BV1w8411y7uj). + - Since OLSS requires additional training, we don't implement it in this project. + +- **Aug 29, 2023.** We propose DiffSynth, a video synthesis framework. + - [Project Page](https://ecnu-cilab.github.io/DiffSynth.github.io/). + - The source codes are released in [EasyNLP](https://github.com/alibaba/EasyNLP/tree/master/diffusion/DiffSynth). + - The technical report (ECML PKDD 2024) is released on [arXiv](https://arxiv.org/abs/2308.03463). + + +## Installation + +``` +git clone https://github.com/modelscope/DiffSynth-Studio.git +cd DiffSynth-Studio +pip install -e . +``` + +## Usage (in Python code) + +The Python examples are in [`examples`](./examples/). We provide an overview here. + +### Long Video Synthesis + +We trained an extended video synthesis model, which can generate 128 frames. [`examples/ExVideo`](./examples/ExVideo/) + +https://github.com/modelscope/DiffSynth-Studio/assets/35051019/d97f6aa9-8064-4b5b-9d49-ed6001bb9acc + +### Image Synthesis + +Generate high-resolution images, by breaking the limitation of diffusion models! [`examples/image_synthesis`](./examples/image_synthesis/). + +LoRA fine-tuning is supported in [`examples/train`](./examples/train/). + +|Model|Example| +|-|-| +|Stable Diffusion|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/6fc84611-8da6-4a1f-8fee-9a34eba3b4a5)| +|Stable Diffusion XL|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/67687748-e738-438c-aee5-96096f09ac90)| +|Stable Diffusion 3|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/4df346db-6f91-420a-b4c1-26e205376098)| +|Kolors|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/53ef6f41-da11-4701-8665-9f64392607bf)| +|Hunyuan-DiT|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/60b022c8-df3f-4541-95ab-bf39f2fa8bb5)| + +### Toon Shading + +Render realistic videos in a flatten style and enable video editing features. [`examples/Diffutoon`](./examples/Diffutoon/) + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-4709-be5e-b39af82404dd + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/20528af5-5100-474a-8cdc-440b9efdd86c + +### Video Stylization + +Video stylization without video models. [`examples/diffsynth`](./examples/diffsynth/) + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea + +## Usage (in WebUI) + +``` +python -m streamlit run DiffSynth_Studio.py +``` + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/93085557-73f3-4eee-a205-9829591ef954 diff --git a/diffsynth/__init__.py b/diffsynth/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0f2351777ad6d3d5734a6bbf99d8c7685158ffd5 --- /dev/null +++ b/diffsynth/__init__.py @@ -0,0 +1,6 @@ +from .data import * +from .models import * +from .prompts import * +from .schedulers import * +from .pipelines import * +from .controlnets import * diff --git a/diffsynth/controlnets/__init__.py b/diffsynth/controlnets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b08ba4cbdac2e97f79285a5d5c772dae3b0e25ed --- /dev/null +++ b/diffsynth/controlnets/__init__.py @@ -0,0 +1,2 @@ +from .controlnet_unit import ControlNetConfigUnit, ControlNetUnit, MultiControlNetManager +from .processors import Annotator diff --git a/diffsynth/controlnets/controlnet_unit.py b/diffsynth/controlnets/controlnet_unit.py new file mode 100644 index 0000000000000000000000000000000000000000..9754baed90b55dc3518c1cf33ea5863303a49db6 --- /dev/null +++ b/diffsynth/controlnets/controlnet_unit.py @@ -0,0 +1,53 @@ +import torch +import numpy as np +from .processors import Processor_id + + +class ControlNetConfigUnit: + def __init__(self, processor_id: Processor_id, model_path, scale=1.0): + self.processor_id = processor_id + self.model_path = model_path + self.scale = scale + + +class ControlNetUnit: + def __init__(self, processor, model, scale=1.0): + self.processor = processor + self.model = model + self.scale = scale + + +class MultiControlNetManager: + def __init__(self, controlnet_units=[]): + self.processors = [unit.processor for unit in controlnet_units] + self.models = [unit.model for unit in controlnet_units] + self.scales = [unit.scale for unit in controlnet_units] + + def process_image(self, image, processor_id=None): + if processor_id is None: + processed_image = [processor(image) for processor in self.processors] + else: + processed_image = [self.processors[processor_id](image)] + processed_image = torch.concat([ + torch.Tensor(np.array(image_, dtype=np.float32) / 255).permute(2, 0, 1).unsqueeze(0) + for image_ in processed_image + ], dim=0) + return processed_image + + def __call__( + self, + sample, timestep, encoder_hidden_states, conditionings, + tiled=False, tile_size=64, tile_stride=32 + ): + res_stack = None + for conditioning, model, scale in zip(conditionings, self.models, self.scales): + res_stack_ = model( + sample, timestep, encoder_hidden_states, conditioning, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride + ) + res_stack_ = [res * scale for res in res_stack_] + if res_stack is None: + res_stack = res_stack_ + else: + res_stack = [i + j for i, j in zip(res_stack, res_stack_)] + return res_stack diff --git a/diffsynth/controlnets/processors.py b/diffsynth/controlnets/processors.py new file mode 100644 index 0000000000000000000000000000000000000000..1d23c730e7c6ccc0332fad24f43c92609885fa63 --- /dev/null +++ b/diffsynth/controlnets/processors.py @@ -0,0 +1,51 @@ +from typing_extensions import Literal, TypeAlias +import warnings +with warnings.catch_warnings(): + warnings.simplefilter("ignore") + from controlnet_aux.processor import ( + CannyDetector, MidasDetector, HEDdetector, LineartDetector, LineartAnimeDetector, OpenposeDetector + ) + + +Processor_id: TypeAlias = Literal[ + "canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "tile" +] + +class Annotator: + def __init__(self, processor_id: Processor_id, model_path="models/Annotators", detect_resolution=None, device='cuda'): + if processor_id == "canny": + self.processor = CannyDetector() + elif processor_id == "depth": + self.processor = MidasDetector.from_pretrained(model_path).to(device) + elif processor_id == "softedge": + self.processor = HEDdetector.from_pretrained(model_path).to(device) + elif processor_id == "lineart": + self.processor = LineartDetector.from_pretrained(model_path).to(device) + elif processor_id == "lineart_anime": + self.processor = LineartAnimeDetector.from_pretrained(model_path).to(device) + elif processor_id == "openpose": + self.processor = OpenposeDetector.from_pretrained(model_path).to(device) + elif processor_id == "tile": + self.processor = None + else: + raise ValueError(f"Unsupported processor_id: {processor_id}") + + self.processor_id = processor_id + self.detect_resolution = detect_resolution + + def __call__(self, image): + width, height = image.size + if self.processor_id == "openpose": + kwargs = { + "include_body": True, + "include_hand": True, + "include_face": True + } + else: + kwargs = {} + if self.processor is not None: + detect_resolution = self.detect_resolution if self.detect_resolution is not None else min(width, height) + image = self.processor(image, detect_resolution=detect_resolution, image_resolution=min(width, height), **kwargs) + image = image.resize((width, height)) + return image + diff --git a/diffsynth/data/__init__.py b/diffsynth/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..de09a29905d289673e40e53278a0a3181232640d --- /dev/null +++ b/diffsynth/data/__init__.py @@ -0,0 +1 @@ +from .video import VideoData, save_video, save_frames diff --git a/diffsynth/data/video.py b/diffsynth/data/video.py new file mode 100644 index 0000000000000000000000000000000000000000..16e19186ac3b803dd74a1a56b3006b06427bbc51 --- /dev/null +++ b/diffsynth/data/video.py @@ -0,0 +1,148 @@ +import imageio, os +import numpy as np +from PIL import Image +from tqdm import tqdm + + +class LowMemoryVideo: + def __init__(self, file_name): + self.reader = imageio.get_reader(file_name) + + def __len__(self): + return self.reader.count_frames() + + def __getitem__(self, item): + return Image.fromarray(np.array(self.reader.get_data(item))).convert("RGB") + + def __del__(self): + self.reader.close() + + +def split_file_name(file_name): + result = [] + number = -1 + for i in file_name: + if ord(i)>=ord("0") and ord(i)<=ord("9"): + if number == -1: + number = 0 + number = number*10 + ord(i) - ord("0") + else: + if number != -1: + result.append(number) + number = -1 + result.append(i) + if number != -1: + result.append(number) + result = tuple(result) + return result + + +def search_for_images(folder): + file_list = [i for i in os.listdir(folder) if i.endswith(".jpg") or i.endswith(".png")] + file_list = [(split_file_name(file_name), file_name) for file_name in file_list] + file_list = [i[1] for i in sorted(file_list)] + file_list = [os.path.join(folder, i) for i in file_list] + return file_list + + +class LowMemoryImageFolder: + def __init__(self, folder, file_list=None): + if file_list is None: + self.file_list = search_for_images(folder) + else: + self.file_list = [os.path.join(folder, file_name) for file_name in file_list] + + def __len__(self): + return len(self.file_list) + + def __getitem__(self, item): + return Image.open(self.file_list[item]).convert("RGB") + + def __del__(self): + pass + + +def crop_and_resize(image, height, width): + image = np.array(image) + image_height, image_width, _ = image.shape + if image_height / image_width < height / width: + croped_width = int(image_height / height * width) + left = (image_width - croped_width) // 2 + image = image[:, left: left+croped_width] + image = Image.fromarray(image).resize((width, height)) + else: + croped_height = int(image_width / width * height) + left = (image_height - croped_height) // 2 + image = image[left: left+croped_height, :] + image = Image.fromarray(image).resize((width, height)) + return image + + +class VideoData: + def __init__(self, video_file=None, image_folder=None, height=None, width=None, **kwargs): + if video_file is not None: + self.data_type = "video" + self.data = LowMemoryVideo(video_file, **kwargs) + elif image_folder is not None: + self.data_type = "images" + self.data = LowMemoryImageFolder(image_folder, **kwargs) + else: + raise ValueError("Cannot open video or image folder") + self.length = None + self.set_shape(height, width) + + def raw_data(self): + frames = [] + for i in range(self.__len__()): + frames.append(self.__getitem__(i)) + return frames + + def set_length(self, length): + self.length = length + + def set_shape(self, height, width): + self.height = height + self.width = width + + def __len__(self): + if self.length is None: + return len(self.data) + else: + return self.length + + def shape(self): + if self.height is not None and self.width is not None: + return self.height, self.width + else: + height, width, _ = self.__getitem__(0).shape + return height, width + + def __getitem__(self, item): + frame = self.data.__getitem__(item) + width, height = frame.size + if self.height is not None and self.width is not None: + if self.height != height or self.width != width: + frame = crop_and_resize(frame, self.height, self.width) + return frame + + def __del__(self): + pass + + def save_images(self, folder): + os.makedirs(folder, exist_ok=True) + for i in tqdm(range(self.__len__()), desc="Saving images"): + frame = self.__getitem__(i) + frame.save(os.path.join(folder, f"{i}.png")) + + +def save_video(frames, save_path, fps, quality=9): + writer = imageio.get_writer(save_path, fps=fps, quality=quality) + for frame in tqdm(frames, desc="Saving video"): + frame = np.array(frame) + writer.append_data(frame) + writer.close() + +def save_frames(frames, save_path): + os.makedirs(save_path, exist_ok=True) + for i, frame in enumerate(tqdm(frames, desc="Saving images")): + frame.save(os.path.join(save_path, f"{i}.png")) diff --git a/diffsynth/extensions/ESRGAN/__init__.py b/diffsynth/extensions/ESRGAN/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e71cd3f24f1f81c7fc224421aea08aa944707493 --- /dev/null +++ b/diffsynth/extensions/ESRGAN/__init__.py @@ -0,0 +1,118 @@ +import torch +from einops import repeat +from PIL import Image +import numpy as np + + +class ResidualDenseBlock(torch.nn.Module): + + def __init__(self, num_feat=64, num_grow_ch=32): + super(ResidualDenseBlock, self).__init__() + self.conv1 = torch.nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1) + self.conv2 = torch.nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1) + self.conv3 = torch.nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1) + self.conv4 = torch.nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1) + self.conv5 = torch.nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1) + self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True) + + def forward(self, x): + x1 = self.lrelu(self.conv1(x)) + x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) + x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) + x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) + x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) + return x5 * 0.2 + x + + +class RRDB(torch.nn.Module): + + def __init__(self, num_feat, num_grow_ch=32): + super(RRDB, self).__init__() + self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch) + self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch) + self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch) + + def forward(self, x): + out = self.rdb1(x) + out = self.rdb2(out) + out = self.rdb3(out) + return out * 0.2 + x + + +class RRDBNet(torch.nn.Module): + + def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32): + super(RRDBNet, self).__init__() + self.conv_first = torch.nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) + self.body = torch.torch.nn.Sequential(*[RRDB(num_feat=num_feat, num_grow_ch=num_grow_ch) for _ in range(num_block)]) + self.conv_body = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) + # upsample + self.conv_up1 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_up2 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_hr = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_last = torch.nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True) + + def forward(self, x): + feat = x + feat = self.conv_first(feat) + body_feat = self.conv_body(self.body(feat)) + feat = feat + body_feat + # upsample + feat = repeat(feat, "B C H W -> B C (H 2) (W 2)") + feat = self.lrelu(self.conv_up1(feat)) + feat = repeat(feat, "B C H W -> B C (H 2) (W 2)") + feat = self.lrelu(self.conv_up2(feat)) + out = self.conv_last(self.lrelu(self.conv_hr(feat))) + return out + + +class ESRGAN(torch.nn.Module): + def __init__(self, model): + super().__init__() + self.model = model + + @staticmethod + def from_pretrained(model_path): + model = RRDBNet() + state_dict = torch.load(model_path, map_location="cpu")["params_ema"] + model.load_state_dict(state_dict) + model.eval() + return ESRGAN(model) + + def process_image(self, image): + image = torch.Tensor(np.array(image, dtype=np.float32) / 255).permute(2, 0, 1) + return image + + def process_images(self, images): + images = [self.process_image(image) for image in images] + images = torch.stack(images) + return images + + def decode_images(self, images): + images = (images.permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8) + images = [Image.fromarray(image) for image in images] + return images + + @torch.no_grad() + def upscale(self, images, batch_size=4, progress_bar=lambda x:x): + # Preprocess + input_tensor = self.process_images(images) + + # Interpolate + output_tensor = [] + for batch_id in progress_bar(range(0, input_tensor.shape[0], batch_size)): + batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) + batch_input_tensor = input_tensor[batch_id: batch_id_] + batch_input_tensor = batch_input_tensor.to( + device=self.model.conv_first.weight.device, + dtype=self.model.conv_first.weight.dtype) + batch_output_tensor = self.model(batch_input_tensor) + output_tensor.append(batch_output_tensor.cpu()) + + # Output + output_tensor = torch.concat(output_tensor, dim=0) + + # To images + output_images = self.decode_images(output_tensor) + return output_images diff --git a/diffsynth/extensions/FastBlend/__init__.py b/diffsynth/extensions/FastBlend/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2bf812c2085082bfa82658dd249ebca89e9fb465 --- /dev/null +++ b/diffsynth/extensions/FastBlend/__init__.py @@ -0,0 +1,63 @@ +from .runners.fast import TableManager, PyramidPatchMatcher +from PIL import Image +import numpy as np +import cupy as cp + + +class FastBlendSmoother: + def __init__(self): + self.batch_size = 8 + self.window_size = 64 + self.ebsynth_config = { + "minimum_patch_size": 5, + "threads_per_block": 8, + "num_iter": 5, + "gpu_id": 0, + "guide_weight": 10.0, + "initialize": "identity", + "tracking_window_size": 0, + } + + @staticmethod + def from_model_manager(model_manager): + # TODO: fetch GPU ID from model_manager + return FastBlendSmoother() + + def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config): + frames_guide = [np.array(frame) for frame in frames_guide] + frames_style = [np.array(frame) for frame in frames_style] + table_manager = TableManager() + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + **ebsynth_config + ) + # left part + table_l = table_manager.build_remapping_table(frames_guide, frames_style, patch_match_engine, batch_size, desc="FastBlend Step 1/4") + table_l = table_manager.remapping_table_to_blending_table(table_l) + table_l = table_manager.process_window_sum(frames_guide, table_l, patch_match_engine, window_size, batch_size, desc="FastBlend Step 2/4") + # right part + table_r = table_manager.build_remapping_table(frames_guide[::-1], frames_style[::-1], patch_match_engine, batch_size, desc="FastBlend Step 3/4") + table_r = table_manager.remapping_table_to_blending_table(table_r) + table_r = table_manager.process_window_sum(frames_guide[::-1], table_r, patch_match_engine, window_size, batch_size, desc="FastBlend Step 4/4")[::-1] + # merge + frames = [] + for (frame_l, weight_l), frame_m, (frame_r, weight_r) in zip(table_l, frames_style, table_r): + weight_m = -1 + weight = weight_l + weight_m + weight_r + frame = frame_l * (weight_l / weight) + frame_m * (weight_m / weight) + frame_r * (weight_r / weight) + frames.append(frame) + frames = [Image.fromarray(frame.clip(0, 255).astype("uint8")) for frame in frames] + return frames + + def __call__(self, rendered_frames, original_frames=None, **kwargs): + frames = self.run( + original_frames, rendered_frames, + self.batch_size, self.window_size, self.ebsynth_config + ) + mempool = cp.get_default_memory_pool() + pinned_mempool = cp.get_default_pinned_memory_pool() + mempool.free_all_blocks() + pinned_mempool.free_all_blocks() + return frames \ No newline at end of file diff --git a/diffsynth/extensions/FastBlend/api.py b/diffsynth/extensions/FastBlend/api.py new file mode 100644 index 0000000000000000000000000000000000000000..2db24330e375ed62065af54613b6ab956c9c64cf --- /dev/null +++ b/diffsynth/extensions/FastBlend/api.py @@ -0,0 +1,397 @@ +from .runners import AccurateModeRunner, FastModeRunner, BalancedModeRunner, InterpolationModeRunner, InterpolationModeSingleFrameRunner +from .data import VideoData, get_video_fps, save_video, search_for_images +import os +import gradio as gr + + +def check_input_for_blending(video_guide, video_guide_folder, video_style, video_style_folder): + frames_guide = VideoData(video_guide, video_guide_folder) + frames_style = VideoData(video_style, video_style_folder) + message = "" + if len(frames_guide) < len(frames_style): + message += f"The number of frames mismatches. Only the first {len(frames_guide)} frames of style video will be used.\n" + frames_style.set_length(len(frames_guide)) + elif len(frames_guide) > len(frames_style): + message += f"The number of frames mismatches. Only the first {len(frames_style)} frames of guide video will be used.\n" + frames_guide.set_length(len(frames_style)) + height_guide, width_guide = frames_guide.shape() + height_style, width_style = frames_style.shape() + if height_guide != height_style or width_guide != width_style: + message += f"The shape of frames mismatches. The frames in style video will be resized to (height: {height_guide}, width: {width_guide})\n" + frames_style.set_shape(height_guide, width_guide) + return frames_guide, frames_style, message + + +def smooth_video( + video_guide, + video_guide_folder, + video_style, + video_style_folder, + mode, + window_size, + batch_size, + tracking_window_size, + output_path, + fps, + minimum_patch_size, + num_iter, + guide_weight, + initialize, + progress = None, +): + # input + frames_guide, frames_style, message = check_input_for_blending(video_guide, video_guide_folder, video_style, video_style_folder) + if len(message) > 0: + print(message) + # output + if output_path == "": + if video_style is None: + output_path = os.path.join(video_style_folder, "output") + else: + output_path = os.path.join(os.path.split(video_style)[0], "output") + os.makedirs(output_path, exist_ok=True) + print("No valid output_path. Your video will be saved here:", output_path) + elif not os.path.exists(output_path): + os.makedirs(output_path, exist_ok=True) + print("Your video will be saved here:", output_path) + frames_path = os.path.join(output_path, "frames") + video_path = os.path.join(output_path, "video.mp4") + os.makedirs(frames_path, exist_ok=True) + # process + if mode == "Fast" or mode == "Balanced": + tracking_window_size = 0 + ebsynth_config = { + "minimum_patch_size": minimum_patch_size, + "threads_per_block": 8, + "num_iter": num_iter, + "gpu_id": 0, + "guide_weight": guide_weight, + "initialize": initialize, + "tracking_window_size": tracking_window_size, + } + if mode == "Fast": + FastModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path) + elif mode == "Balanced": + BalancedModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path) + elif mode == "Accurate": + AccurateModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path) + # output + try: + fps = int(fps) + except: + fps = get_video_fps(video_style) if video_style is not None else 30 + print("Fps:", fps) + print("Saving video...") + video_path = save_video(frames_path, video_path, num_frames=len(frames_style), fps=fps) + print("Success!") + print("Your frames are here:", frames_path) + print("Your video is here:", video_path) + return output_path, fps, video_path + + +class KeyFrameMatcher: + def __init__(self): + pass + + def extract_number_from_filename(self, file_name): + result = [] + number = -1 + for i in file_name: + if ord(i)>=ord("0") and ord(i)<=ord("9"): + if number == -1: + number = 0 + number = number*10 + ord(i) - ord("0") + else: + if number != -1: + result.append(number) + number = -1 + if number != -1: + result.append(number) + result = tuple(result) + return result + + def extract_number_from_filenames(self, file_names): + numbers = [self.extract_number_from_filename(file_name) for file_name in file_names] + min_length = min(len(i) for i in numbers) + for i in range(min_length-1, -1, -1): + if len(set(number[i] for number in numbers))==len(file_names): + return [number[i] for number in numbers] + return list(range(len(file_names))) + + def match_using_filename(self, file_names_a, file_names_b): + file_names_b_set = set(file_names_b) + matched_file_name = [] + for file_name in file_names_a: + if file_name not in file_names_b_set: + matched_file_name.append(None) + else: + matched_file_name.append(file_name) + return matched_file_name + + def match_using_numbers(self, file_names_a, file_names_b): + numbers_a = self.extract_number_from_filenames(file_names_a) + numbers_b = self.extract_number_from_filenames(file_names_b) + numbers_b_dict = {number: file_name for number, file_name in zip(numbers_b, file_names_b)} + matched_file_name = [] + for number in numbers_a: + if number in numbers_b_dict: + matched_file_name.append(numbers_b_dict[number]) + else: + matched_file_name.append(None) + return matched_file_name + + def match_filenames(self, file_names_a, file_names_b): + matched_file_name = self.match_using_filename(file_names_a, file_names_b) + if sum([i is not None for i in matched_file_name]) > 0: + return matched_file_name + matched_file_name = self.match_using_numbers(file_names_a, file_names_b) + return matched_file_name + + +def detect_frames(frames_path, keyframes_path): + if not os.path.exists(frames_path) and not os.path.exists(keyframes_path): + return "Please input the directory of guide video and rendered frames" + elif not os.path.exists(frames_path): + return "Please input the directory of guide video" + elif not os.path.exists(keyframes_path): + return "Please input the directory of rendered frames" + frames = [os.path.split(i)[-1] for i in search_for_images(frames_path)] + keyframes = [os.path.split(i)[-1] for i in search_for_images(keyframes_path)] + if len(frames)==0: + return f"No images detected in {frames_path}" + if len(keyframes)==0: + return f"No images detected in {keyframes_path}" + matched_keyframes = KeyFrameMatcher().match_filenames(frames, keyframes) + max_filename_length = max([len(i) for i in frames]) + if sum([i is not None for i in matched_keyframes])==0: + message = "" + for frame, matched_keyframe in zip(frames, matched_keyframes): + message += frame + " " * (max_filename_length - len(frame) + 1) + message += "--> No matched keyframes\n" + else: + message = "" + for frame, matched_keyframe in zip(frames, matched_keyframes): + message += frame + " " * (max_filename_length - len(frame) + 1) + if matched_keyframe is None: + message += "--> [to be rendered]\n" + else: + message += f"--> {matched_keyframe}\n" + return message + + +def check_input_for_interpolating(frames_path, keyframes_path): + # search for images + frames = [os.path.split(i)[-1] for i in search_for_images(frames_path)] + keyframes = [os.path.split(i)[-1] for i in search_for_images(keyframes_path)] + # match frames + matched_keyframes = KeyFrameMatcher().match_filenames(frames, keyframes) + file_list = [file_name for file_name in matched_keyframes if file_name is not None] + index_style = [i for i, file_name in enumerate(matched_keyframes) if file_name is not None] + frames_guide = VideoData(None, frames_path) + frames_style = VideoData(None, keyframes_path, file_list=file_list) + # match shape + message = "" + height_guide, width_guide = frames_guide.shape() + height_style, width_style = frames_style.shape() + if height_guide != height_style or width_guide != width_style: + message += f"The shape of frames mismatches. The rendered keyframes will be resized to (height: {height_guide}, width: {width_guide})\n" + frames_style.set_shape(height_guide, width_guide) + return frames_guide, frames_style, index_style, message + + +def interpolate_video( + frames_path, + keyframes_path, + output_path, + fps, + batch_size, + tracking_window_size, + minimum_patch_size, + num_iter, + guide_weight, + initialize, + progress = None, +): + # input + frames_guide, frames_style, index_style, message = check_input_for_interpolating(frames_path, keyframes_path) + if len(message) > 0: + print(message) + # output + if output_path == "": + output_path = os.path.join(keyframes_path, "output") + os.makedirs(output_path, exist_ok=True) + print("No valid output_path. Your video will be saved here:", output_path) + elif not os.path.exists(output_path): + os.makedirs(output_path, exist_ok=True) + print("Your video will be saved here:", output_path) + output_frames_path = os.path.join(output_path, "frames") + output_video_path = os.path.join(output_path, "video.mp4") + os.makedirs(output_frames_path, exist_ok=True) + # process + ebsynth_config = { + "minimum_patch_size": minimum_patch_size, + "threads_per_block": 8, + "num_iter": num_iter, + "gpu_id": 0, + "guide_weight": guide_weight, + "initialize": initialize, + "tracking_window_size": tracking_window_size + } + if len(index_style)==1: + InterpolationModeSingleFrameRunner().run(frames_guide, frames_style, index_style, batch_size=batch_size, ebsynth_config=ebsynth_config, save_path=output_frames_path) + else: + InterpolationModeRunner().run(frames_guide, frames_style, index_style, batch_size=batch_size, ebsynth_config=ebsynth_config, save_path=output_frames_path) + try: + fps = int(fps) + except: + fps = 30 + print("Fps:", fps) + print("Saving video...") + video_path = save_video(output_frames_path, output_video_path, num_frames=len(frames_guide), fps=fps) + print("Success!") + print("Your frames are here:", output_frames_path) + print("Your video is here:", video_path) + return output_path, fps, video_path + + +def on_ui_tabs(): + with gr.Blocks(analytics_enabled=False) as ui_component: + with gr.Tab("Blend"): + gr.Markdown(""" +# Blend + +Given a guide video and a style video, this algorithm will make the style video fluent according to the motion features of the guide video. Click [here](https://github.com/Artiprocher/sd-webui-fastblend/assets/35051019/208d902d-6aba-48d7-b7d5-cd120ebd306d) to see the example. Note that this extension doesn't support long videos. Please use short videos (e.g., several seconds). The algorithm is mainly designed for 512*512 resolution. Please use a larger `Minimum patch size` for higher resolution. + """) + with gr.Row(): + with gr.Column(): + with gr.Tab("Guide video"): + video_guide = gr.Video(label="Guide video") + with gr.Tab("Guide video (images format)"): + video_guide_folder = gr.Textbox(label="Guide video (images format)", value="") + with gr.Column(): + with gr.Tab("Style video"): + video_style = gr.Video(label="Style video") + with gr.Tab("Style video (images format)"): + video_style_folder = gr.Textbox(label="Style video (images format)", value="") + with gr.Column(): + output_path = gr.Textbox(label="Output directory", value="", placeholder="Leave empty to use the directory of style video") + fps = gr.Textbox(label="Fps", value="", placeholder="Leave empty to use the default fps") + video_output = gr.Video(label="Output video", interactive=False, show_share_button=True) + btn = gr.Button(value="Blend") + with gr.Row(): + with gr.Column(): + gr.Markdown("# Settings") + mode = gr.Radio(["Fast", "Balanced", "Accurate"], label="Inference mode", value="Fast", interactive=True) + window_size = gr.Slider(label="Sliding window size", value=15, minimum=1, maximum=1000, step=1, interactive=True) + batch_size = gr.Slider(label="Batch size", value=8, minimum=1, maximum=128, step=1, interactive=True) + tracking_window_size = gr.Slider(label="Tracking window size (only for accurate mode)", value=0, minimum=0, maximum=10, step=1, interactive=True) + gr.Markdown("## Advanced Settings") + minimum_patch_size = gr.Slider(label="Minimum patch size (odd number)", value=5, minimum=5, maximum=99, step=2, interactive=True) + num_iter = gr.Slider(label="Number of iterations", value=5, minimum=1, maximum=10, step=1, interactive=True) + guide_weight = gr.Slider(label="Guide weight", value=10.0, minimum=0.0, maximum=100.0, step=0.1, interactive=True) + initialize = gr.Radio(["identity", "random"], label="NNF initialization", value="identity", interactive=True) + with gr.Column(): + gr.Markdown(""" +# Reference + +* Output directory: the directory to save the video. +* Inference mode + +|Mode|Time|Memory|Quality|Frame by frame output|Description| +|-|-|-|-|-|-| +|Fast|■|■■■|■■|No|Blend the frames using a tree-like data structure, which requires much RAM but is fast.| +|Balanced|■■|■|■■|Yes|Blend the frames naively.| +|Accurate|■■■|■|■■■|Yes|Blend the frames and align them together for higher video quality. When [batch size] >= [sliding window size] * 2 + 1, the performance is the best.| + +* Sliding window size: our algorithm will blend the frames in a sliding windows. If the size is n, each frame will be blended with the last n frames and the next n frames. A large sliding window can make the video fluent but sometimes smoggy. +* Batch size: a larger batch size makes the program faster but requires more VRAM. +* Tracking window size (only for accurate mode): The size of window in which our algorithm tracks moving objects. Empirically, 1 is enough. +* Advanced settings + * Minimum patch size (odd number): the minimum patch size used for patch matching. (Default: 5) + * Number of iterations: the number of iterations of patch matching. (Default: 5) + * Guide weight: a parameter that determines how much motion feature applied to the style video. (Default: 10) + * NNF initialization: how to initialize the NNF (Nearest Neighbor Field). (Default: identity) + """) + btn.click( + smooth_video, + inputs=[ + video_guide, + video_guide_folder, + video_style, + video_style_folder, + mode, + window_size, + batch_size, + tracking_window_size, + output_path, + fps, + minimum_patch_size, + num_iter, + guide_weight, + initialize + ], + outputs=[output_path, fps, video_output] + ) + with gr.Tab("Interpolate"): + gr.Markdown(""" +# Interpolate + +Given a guide video and some rendered keyframes, this algorithm will render the remaining frames. Click [here](https://github.com/Artiprocher/sd-webui-fastblend/assets/35051019/3490c5b4-8f67-478f-86de-f9adc2ace16a) to see the example. The algorithm is experimental and is only tested for 512*512 resolution. + """) + with gr.Row(): + with gr.Column(): + with gr.Row(): + with gr.Column(): + video_guide_folder_ = gr.Textbox(label="Guide video (images format)", value="") + with gr.Column(): + rendered_keyframes_ = gr.Textbox(label="Rendered keyframes (images format)", value="") + with gr.Row(): + detected_frames = gr.Textbox(label="Detected frames", value="Please input the directory of guide video and rendered frames", lines=9, max_lines=9, interactive=False) + video_guide_folder_.change(detect_frames, inputs=[video_guide_folder_, rendered_keyframes_], outputs=detected_frames) + rendered_keyframes_.change(detect_frames, inputs=[video_guide_folder_, rendered_keyframes_], outputs=detected_frames) + with gr.Column(): + output_path_ = gr.Textbox(label="Output directory", value="", placeholder="Leave empty to use the directory of rendered keyframes") + fps_ = gr.Textbox(label="Fps", value="", placeholder="Leave empty to use the default fps") + video_output_ = gr.Video(label="Output video", interactive=False, show_share_button=True) + btn_ = gr.Button(value="Interpolate") + with gr.Row(): + with gr.Column(): + gr.Markdown("# Settings") + batch_size_ = gr.Slider(label="Batch size", value=8, minimum=1, maximum=128, step=1, interactive=True) + tracking_window_size_ = gr.Slider(label="Tracking window size", value=0, minimum=0, maximum=10, step=1, interactive=True) + gr.Markdown("## Advanced Settings") + minimum_patch_size_ = gr.Slider(label="Minimum patch size (odd number, larger is better)", value=15, minimum=5, maximum=99, step=2, interactive=True) + num_iter_ = gr.Slider(label="Number of iterations", value=5, minimum=1, maximum=10, step=1, interactive=True) + guide_weight_ = gr.Slider(label="Guide weight", value=10.0, minimum=0.0, maximum=100.0, step=0.1, interactive=True) + initialize_ = gr.Radio(["identity", "random"], label="NNF initialization", value="identity", interactive=True) + with gr.Column(): + gr.Markdown(""" +# Reference + +* Output directory: the directory to save the video. +* Batch size: a larger batch size makes the program faster but requires more VRAM. +* Tracking window size (only for accurate mode): The size of window in which our algorithm tracks moving objects. Empirically, 1 is enough. +* Advanced settings + * Minimum patch size (odd number): the minimum patch size used for patch matching. **This parameter should be larger than that in blending. (Default: 15)** + * Number of iterations: the number of iterations of patch matching. (Default: 5) + * Guide weight: a parameter that determines how much motion feature applied to the style video. (Default: 10) + * NNF initialization: how to initialize the NNF (Nearest Neighbor Field). (Default: identity) + """) + btn_.click( + interpolate_video, + inputs=[ + video_guide_folder_, + rendered_keyframes_, + output_path_, + fps_, + batch_size_, + tracking_window_size_, + minimum_patch_size_, + num_iter_, + guide_weight_, + initialize_, + ], + outputs=[output_path_, fps_, video_output_] + ) + + return [(ui_component, "FastBlend", "FastBlend_ui")] diff --git a/diffsynth/extensions/FastBlend/cupy_kernels.py b/diffsynth/extensions/FastBlend/cupy_kernels.py new file mode 100644 index 0000000000000000000000000000000000000000..70e2790a2c67a2dd537f4188b38ebfc785f1fb34 --- /dev/null +++ b/diffsynth/extensions/FastBlend/cupy_kernels.py @@ -0,0 +1,119 @@ +import cupy as cp + +remapping_kernel = cp.RawKernel(r''' +extern "C" __global__ +void remap( + const int height, + const int width, + const int channel, + const int patch_size, + const int pad_size, + const float* source_style, + const int* nnf, + float* target_style +) { + const int r = (patch_size - 1) / 2; + const int x = blockDim.x * blockIdx.x + threadIdx.x; + const int y = blockDim.y * blockIdx.y + threadIdx.y; + if (x >= height or y >= width) return; + const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel; + const int pid = (x + pad_size) * (width + pad_size * 2) + (y + pad_size); + const int min_px = x < r ? -x : -r; + const int max_px = x + r > height - 1 ? height - 1 - x : r; + const int min_py = y < r ? -y : -r; + const int max_py = y + r > width - 1 ? width - 1 - y : r; + int num = 0; + for (int px = min_px; px <= max_px; px++){ + for (int py = min_py; py <= max_py; py++){ + const int nid = (x + px) * width + y + py; + const int x_ = nnf[blockIdx.z * height * width * 2 + nid*2 + 0] - px; + const int y_ = nnf[blockIdx.z * height * width * 2 + nid*2 + 1] - py; + if (x_ < 0 or y_ < 0 or x_ >= height or y_ >= width)continue; + const int pid_ = (x_ + pad_size) * (width + pad_size * 2) + (y_ + pad_size); + num++; + for (int c = 0; c < channel; c++){ + target_style[z + pid * channel + c] += source_style[z + pid_ * channel + c]; + } + } + } + for (int c = 0; c < channel; c++){ + target_style[z + pid * channel + c] /= num; + } +} +''', 'remap') + + +patch_error_kernel = cp.RawKernel(r''' +extern "C" __global__ +void patch_error( + const int height, + const int width, + const int channel, + const int patch_size, + const int pad_size, + const float* source, + const int* nnf, + const float* target, + float* error +) { + const int r = (patch_size - 1) / 2; + const int x = blockDim.x * blockIdx.x + threadIdx.x; + const int y = blockDim.y * blockIdx.y + threadIdx.y; + const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel; + if (x >= height or y >= width) return; + const int x_ = nnf[blockIdx.z * height * width * 2 + (x * width + y)*2 + 0]; + const int y_ = nnf[blockIdx.z * height * width * 2 + (x * width + y)*2 + 1]; + float e = 0; + for (int px = -r; px <= r; px++){ + for (int py = -r; py <= r; py++){ + const int pid = (x + pad_size + px) * (width + pad_size * 2) + y + pad_size + py; + const int pid_ = (x_ + pad_size + px) * (width + pad_size * 2) + y_ + pad_size + py; + for (int c = 0; c < channel; c++){ + const float diff = target[z + pid * channel + c] - source[z + pid_ * channel + c]; + e += diff * diff; + } + } + } + error[blockIdx.z * height * width + x * width + y] = e; +} +''', 'patch_error') + + +pairwise_patch_error_kernel = cp.RawKernel(r''' +extern "C" __global__ +void pairwise_patch_error( + const int height, + const int width, + const int channel, + const int patch_size, + const int pad_size, + const float* source_a, + const int* nnf_a, + const float* source_b, + const int* nnf_b, + float* error +) { + const int r = (patch_size - 1) / 2; + const int x = blockDim.x * blockIdx.x + threadIdx.x; + const int y = blockDim.y * blockIdx.y + threadIdx.y; + const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel; + if (x >= height or y >= width) return; + const int z_nnf = blockIdx.z * height * width * 2 + (x * width + y) * 2; + const int x_a = nnf_a[z_nnf + 0]; + const int y_a = nnf_a[z_nnf + 1]; + const int x_b = nnf_b[z_nnf + 0]; + const int y_b = nnf_b[z_nnf + 1]; + float e = 0; + for (int px = -r; px <= r; px++){ + for (int py = -r; py <= r; py++){ + const int pid_a = (x_a + pad_size + px) * (width + pad_size * 2) + y_a + pad_size + py; + const int pid_b = (x_b + pad_size + px) * (width + pad_size * 2) + y_b + pad_size + py; + for (int c = 0; c < channel; c++){ + const float diff = source_a[z + pid_a * channel + c] - source_b[z + pid_b * channel + c]; + e += diff * diff; + } + } + } + error[blockIdx.z * height * width + x * width + y] = e; +} +''', 'pairwise_patch_error') diff --git a/diffsynth/extensions/FastBlend/data.py b/diffsynth/extensions/FastBlend/data.py new file mode 100644 index 0000000000000000000000000000000000000000..dcaddd77de9eaf208cd083dd522e5eaa6b58f783 --- /dev/null +++ b/diffsynth/extensions/FastBlend/data.py @@ -0,0 +1,146 @@ +import imageio, os +import numpy as np +from PIL import Image + + +def read_video(file_name): + reader = imageio.get_reader(file_name) + video = [] + for frame in reader: + frame = np.array(frame) + video.append(frame) + reader.close() + return video + + +def get_video_fps(file_name): + reader = imageio.get_reader(file_name) + fps = reader.get_meta_data()["fps"] + reader.close() + return fps + + +def save_video(frames_path, video_path, num_frames, fps): + writer = imageio.get_writer(video_path, fps=fps, quality=9) + for i in range(num_frames): + frame = np.array(Image.open(os.path.join(frames_path, "%05d.png" % i))) + writer.append_data(frame) + writer.close() + return video_path + + +class LowMemoryVideo: + def __init__(self, file_name): + self.reader = imageio.get_reader(file_name) + + def __len__(self): + return self.reader.count_frames() + + def __getitem__(self, item): + return np.array(self.reader.get_data(item)) + + def __del__(self): + self.reader.close() + + +def split_file_name(file_name): + result = [] + number = -1 + for i in file_name: + if ord(i)>=ord("0") and ord(i)<=ord("9"): + if number == -1: + number = 0 + number = number*10 + ord(i) - ord("0") + else: + if number != -1: + result.append(number) + number = -1 + result.append(i) + if number != -1: + result.append(number) + result = tuple(result) + return result + + +def search_for_images(folder): + file_list = [i for i in os.listdir(folder) if i.endswith(".jpg") or i.endswith(".png")] + file_list = [(split_file_name(file_name), file_name) for file_name in file_list] + file_list = [i[1] for i in sorted(file_list)] + file_list = [os.path.join(folder, i) for i in file_list] + return file_list + + +def read_images(folder): + file_list = search_for_images(folder) + frames = [np.array(Image.open(i)) for i in file_list] + return frames + + +class LowMemoryImageFolder: + def __init__(self, folder, file_list=None): + if file_list is None: + self.file_list = search_for_images(folder) + else: + self.file_list = [os.path.join(folder, file_name) for file_name in file_list] + + def __len__(self): + return len(self.file_list) + + def __getitem__(self, item): + return np.array(Image.open(self.file_list[item])) + + def __del__(self): + pass + + +class VideoData: + def __init__(self, video_file, image_folder, **kwargs): + if video_file is not None: + self.data_type = "video" + self.data = LowMemoryVideo(video_file, **kwargs) + elif image_folder is not None: + self.data_type = "images" + self.data = LowMemoryImageFolder(image_folder, **kwargs) + else: + raise ValueError("Cannot open video or image folder") + self.length = None + self.height = None + self.width = None + + def raw_data(self): + frames = [] + for i in range(self.__len__()): + frames.append(self.__getitem__(i)) + return frames + + def set_length(self, length): + self.length = length + + def set_shape(self, height, width): + self.height = height + self.width = width + + def __len__(self): + if self.length is None: + return len(self.data) + else: + return self.length + + def shape(self): + if self.height is not None and self.width is not None: + return self.height, self.width + else: + height, width, _ = self.__getitem__(0).shape + return height, width + + def __getitem__(self, item): + frame = self.data.__getitem__(item) + height, width, _ = frame.shape + if self.height is not None and self.width is not None: + if self.height != height or self.width != width: + frame = Image.fromarray(frame).resize((self.width, self.height)) + frame = np.array(frame) + return frame + + def __del__(self): + pass diff --git a/diffsynth/extensions/FastBlend/patch_match.py b/diffsynth/extensions/FastBlend/patch_match.py new file mode 100644 index 0000000000000000000000000000000000000000..aeb1f7f9e31b4b2ad77ec58ba8d32361315e0390 --- /dev/null +++ b/diffsynth/extensions/FastBlend/patch_match.py @@ -0,0 +1,298 @@ +from .cupy_kernels import remapping_kernel, patch_error_kernel, pairwise_patch_error_kernel +import numpy as np +import cupy as cp +import cv2 + + +class PatchMatcher: + def __init__( + self, height, width, channel, minimum_patch_size, + threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0, + random_search_steps=3, random_search_range=4, + use_mean_target_style=False, use_pairwise_patch_error=False, + tracking_window_size=0 + ): + self.height = height + self.width = width + self.channel = channel + self.minimum_patch_size = minimum_patch_size + self.threads_per_block = threads_per_block + self.num_iter = num_iter + self.gpu_id = gpu_id + self.guide_weight = guide_weight + self.random_search_steps = random_search_steps + self.random_search_range = random_search_range + self.use_mean_target_style = use_mean_target_style + self.use_pairwise_patch_error = use_pairwise_patch_error + self.tracking_window_size = tracking_window_size + + self.patch_size_list = [minimum_patch_size + i*2 for i in range(num_iter)][::-1] + self.pad_size = self.patch_size_list[0] // 2 + self.grid = ( + (height + threads_per_block - 1) // threads_per_block, + (width + threads_per_block - 1) // threads_per_block + ) + self.block = (threads_per_block, threads_per_block) + + def pad_image(self, image): + return cp.pad(image, ((0, 0), (self.pad_size, self.pad_size), (self.pad_size, self.pad_size), (0, 0))) + + def unpad_image(self, image): + return image[:, self.pad_size: -self.pad_size, self.pad_size: -self.pad_size, :] + + def apply_nnf_to_image(self, nnf, source): + batch_size = source.shape[0] + target = cp.zeros((batch_size, self.height + self.pad_size * 2, self.width + self.pad_size * 2, self.channel), dtype=cp.float32) + remapping_kernel( + self.grid + (batch_size,), + self.block, + (self.height, self.width, self.channel, self.patch_size, self.pad_size, source, nnf, target) + ) + return target + + def get_patch_error(self, source, nnf, target): + batch_size = source.shape[0] + error = cp.zeros((batch_size, self.height, self.width), dtype=cp.float32) + patch_error_kernel( + self.grid + (batch_size,), + self.block, + (self.height, self.width, self.channel, self.patch_size, self.pad_size, source, nnf, target, error) + ) + return error + + def get_pairwise_patch_error(self, source, nnf): + batch_size = source.shape[0]//2 + error = cp.zeros((batch_size, self.height, self.width), dtype=cp.float32) + source_a, nnf_a = source[0::2].copy(), nnf[0::2].copy() + source_b, nnf_b = source[1::2].copy(), nnf[1::2].copy() + pairwise_patch_error_kernel( + self.grid + (batch_size,), + self.block, + (self.height, self.width, self.channel, self.patch_size, self.pad_size, source_a, nnf_a, source_b, nnf_b, error) + ) + error = error.repeat(2, axis=0) + return error + + def get_error(self, source_guide, target_guide, source_style, target_style, nnf): + error_guide = self.get_patch_error(source_guide, nnf, target_guide) + if self.use_mean_target_style: + target_style = self.apply_nnf_to_image(nnf, source_style) + target_style = target_style.mean(axis=0, keepdims=True) + target_style = target_style.repeat(source_guide.shape[0], axis=0) + if self.use_pairwise_patch_error: + error_style = self.get_pairwise_patch_error(source_style, nnf) + else: + error_style = self.get_patch_error(source_style, nnf, target_style) + error = error_guide * self.guide_weight + error_style + return error + + def clamp_bound(self, nnf): + nnf[:,:,:,0] = cp.clip(nnf[:,:,:,0], 0, self.height-1) + nnf[:,:,:,1] = cp.clip(nnf[:,:,:,1], 0, self.width-1) + return nnf + + def random_step(self, nnf, r): + batch_size = nnf.shape[0] + step = cp.random.randint(-r, r+1, size=(batch_size, self.height, self.width, 2), dtype=cp.int32) + upd_nnf = self.clamp_bound(nnf + step) + return upd_nnf + + def neighboor_step(self, nnf, d): + if d==0: + upd_nnf = cp.concatenate([nnf[:, :1, :], nnf[:, :-1, :]], axis=1) + upd_nnf[:, :, :, 0] += 1 + elif d==1: + upd_nnf = cp.concatenate([nnf[:, :, :1], nnf[:, :, :-1]], axis=2) + upd_nnf[:, :, :, 1] += 1 + elif d==2: + upd_nnf = cp.concatenate([nnf[:, 1:, :], nnf[:, -1:, :]], axis=1) + upd_nnf[:, :, :, 0] -= 1 + elif d==3: + upd_nnf = cp.concatenate([nnf[:, :, 1:], nnf[:, :, -1:]], axis=2) + upd_nnf[:, :, :, 1] -= 1 + upd_nnf = self.clamp_bound(upd_nnf) + return upd_nnf + + def shift_nnf(self, nnf, d): + if d>0: + d = min(nnf.shape[0], d) + upd_nnf = cp.concatenate([nnf[d:]] + [nnf[-1:]] * d, axis=0) + else: + d = max(-nnf.shape[0], d) + upd_nnf = cp.concatenate([nnf[:1]] * (-d) + [nnf[:d]], axis=0) + return upd_nnf + + def track_step(self, nnf, d): + if self.use_pairwise_patch_error: + upd_nnf = cp.zeros_like(nnf) + upd_nnf[0::2] = self.shift_nnf(nnf[0::2], d) + upd_nnf[1::2] = self.shift_nnf(nnf[1::2], d) + else: + upd_nnf = self.shift_nnf(nnf, d) + return upd_nnf + + def C(self, n, m): + # not used + c = 1 + for i in range(1, n+1): + c *= i + for i in range(1, m+1): + c //= i + for i in range(1, n-m+1): + c //= i + return c + + def bezier_step(self, nnf, r): + # not used + n = r * 2 - 1 + upd_nnf = cp.zeros(shape=nnf.shape, dtype=cp.float32) + for i, d in enumerate(list(range(-r, 0)) + list(range(1, r+1))): + if d>0: + ctl_nnf = cp.concatenate([nnf[d:]] + [nnf[-1:]] * d, axis=0) + elif d<0: + ctl_nnf = cp.concatenate([nnf[:1]] * (-d) + [nnf[:d]], axis=0) + upd_nnf += ctl_nnf * (self.C(n, i) / 2**n) + upd_nnf = self.clamp_bound(upd_nnf).astype(nnf.dtype) + return upd_nnf + + def update(self, source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf): + upd_err = self.get_error(source_guide, target_guide, source_style, target_style, upd_nnf) + upd_idx = (upd_err < err) + nnf[upd_idx] = upd_nnf[upd_idx] + err[upd_idx] = upd_err[upd_idx] + return nnf, err + + def propagation(self, source_guide, target_guide, source_style, target_style, nnf, err): + for d in cp.random.permutation(4): + upd_nnf = self.neighboor_step(nnf, d) + nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf) + return nnf, err + + def random_search(self, source_guide, target_guide, source_style, target_style, nnf, err): + for i in range(self.random_search_steps): + upd_nnf = self.random_step(nnf, self.random_search_range) + nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf) + return nnf, err + + def track(self, source_guide, target_guide, source_style, target_style, nnf, err): + for d in range(1, self.tracking_window_size + 1): + upd_nnf = self.track_step(nnf, d) + nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf) + upd_nnf = self.track_step(nnf, -d) + nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf) + return nnf, err + + def iteration(self, source_guide, target_guide, source_style, target_style, nnf, err): + nnf, err = self.propagation(source_guide, target_guide, source_style, target_style, nnf, err) + nnf, err = self.random_search(source_guide, target_guide, source_style, target_style, nnf, err) + nnf, err = self.track(source_guide, target_guide, source_style, target_style, nnf, err) + return nnf, err + + def estimate_nnf(self, source_guide, target_guide, source_style, nnf): + with cp.cuda.Device(self.gpu_id): + source_guide = self.pad_image(source_guide) + target_guide = self.pad_image(target_guide) + source_style = self.pad_image(source_style) + for it in range(self.num_iter): + self.patch_size = self.patch_size_list[it] + target_style = self.apply_nnf_to_image(nnf, source_style) + err = self.get_error(source_guide, target_guide, source_style, target_style, nnf) + nnf, err = self.iteration(source_guide, target_guide, source_style, target_style, nnf, err) + target_style = self.unpad_image(self.apply_nnf_to_image(nnf, source_style)) + return nnf, target_style + + +class PyramidPatchMatcher: + def __init__( + self, image_height, image_width, channel, minimum_patch_size, + threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0, + use_mean_target_style=False, use_pairwise_patch_error=False, + tracking_window_size=0, + initialize="identity" + ): + maximum_patch_size = minimum_patch_size + (num_iter - 1) * 2 + self.pyramid_level = int(np.log2(min(image_height, image_width) / maximum_patch_size)) + self.pyramid_heights = [] + self.pyramid_widths = [] + self.patch_matchers = [] + self.minimum_patch_size = minimum_patch_size + self.num_iter = num_iter + self.gpu_id = gpu_id + self.initialize = initialize + for level in range(self.pyramid_level): + height = image_height//(2**(self.pyramid_level - 1 - level)) + width = image_width//(2**(self.pyramid_level - 1 - level)) + self.pyramid_heights.append(height) + self.pyramid_widths.append(width) + self.patch_matchers.append(PatchMatcher( + height, width, channel, minimum_patch_size=minimum_patch_size, + threads_per_block=threads_per_block, num_iter=num_iter, gpu_id=gpu_id, guide_weight=guide_weight, + use_mean_target_style=use_mean_target_style, use_pairwise_patch_error=use_pairwise_patch_error, + tracking_window_size=tracking_window_size + )) + + def resample_image(self, images, level): + height, width = self.pyramid_heights[level], self.pyramid_widths[level] + images = images.get() + images_resample = [] + for image in images: + image_resample = cv2.resize(image, (width, height), interpolation=cv2.INTER_AREA) + images_resample.append(image_resample) + images_resample = cp.array(np.stack(images_resample), dtype=cp.float32) + return images_resample + + def initialize_nnf(self, batch_size): + if self.initialize == "random": + height, width = self.pyramid_heights[0], self.pyramid_widths[0] + nnf = cp.stack([ + cp.random.randint(0, height, (batch_size, height, width), dtype=cp.int32), + cp.random.randint(0, width, (batch_size, height, width), dtype=cp.int32) + ], axis=3) + elif self.initialize == "identity": + height, width = self.pyramid_heights[0], self.pyramid_widths[0] + nnf = cp.stack([ + cp.repeat(cp.arange(height), width).reshape(height, width), + cp.tile(cp.arange(width), height).reshape(height, width) + ], axis=2) + nnf = cp.stack([nnf] * batch_size) + else: + raise NotImplementedError() + return nnf + + def update_nnf(self, nnf, level): + # upscale + nnf = nnf.repeat(2, axis=1).repeat(2, axis=2) * 2 + nnf[:,[i for i in range(nnf.shape[0]) if i&1],:,0] += 1 + nnf[:,:,[i for i in range(nnf.shape[0]) if i&1],1] += 1 + # check if scale is 2 + height, width = self.pyramid_heights[level], self.pyramid_widths[level] + if height != nnf.shape[0] * 2 or width != nnf.shape[1] * 2: + nnf = nnf.get().astype(np.float32) + nnf = [cv2.resize(n, (width, height), interpolation=cv2.INTER_LINEAR) for n in nnf] + nnf = cp.array(np.stack(nnf), dtype=cp.int32) + nnf = self.patch_matchers[level].clamp_bound(nnf) + return nnf + + def apply_nnf_to_image(self, nnf, image): + with cp.cuda.Device(self.gpu_id): + image = self.patch_matchers[-1].pad_image(image) + image = self.patch_matchers[-1].apply_nnf_to_image(nnf, image) + return image + + def estimate_nnf(self, source_guide, target_guide, source_style): + with cp.cuda.Device(self.gpu_id): + if not isinstance(source_guide, cp.ndarray): + source_guide = cp.array(source_guide, dtype=cp.float32) + if not isinstance(target_guide, cp.ndarray): + target_guide = cp.array(target_guide, dtype=cp.float32) + if not isinstance(source_style, cp.ndarray): + source_style = cp.array(source_style, dtype=cp.float32) + for level in range(self.pyramid_level): + nnf = self.initialize_nnf(source_guide.shape[0]) if level==0 else self.update_nnf(nnf, level) + source_guide_ = self.resample_image(source_guide, level) + target_guide_ = self.resample_image(target_guide, level) + source_style_ = self.resample_image(source_style, level) + nnf, target_style = self.patch_matchers[level].estimate_nnf( + source_guide_, target_guide_, source_style_, nnf + ) + return nnf.get(), target_style.get() diff --git a/diffsynth/extensions/FastBlend/runners/__init__.py b/diffsynth/extensions/FastBlend/runners/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..078382729690d282436411661693ce22f3dcc033 --- /dev/null +++ b/diffsynth/extensions/FastBlend/runners/__init__.py @@ -0,0 +1,4 @@ +from .accurate import AccurateModeRunner +from .fast import FastModeRunner +from .balanced import BalancedModeRunner +from .interpolation import InterpolationModeRunner, InterpolationModeSingleFrameRunner diff --git a/diffsynth/extensions/FastBlend/runners/accurate.py b/diffsynth/extensions/FastBlend/runners/accurate.py new file mode 100644 index 0000000000000000000000000000000000000000..2e4a47f1981ebc1ec9a034a814dfc1130955c2e1 --- /dev/null +++ b/diffsynth/extensions/FastBlend/runners/accurate.py @@ -0,0 +1,35 @@ +from ..patch_match import PyramidPatchMatcher +import os +import numpy as np +from PIL import Image +from tqdm import tqdm + + +class AccurateModeRunner: + def __init__(self): + pass + + def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config, desc="Accurate Mode", save_path=None): + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + use_mean_target_style=True, + **ebsynth_config + ) + # run + n = len(frames_style) + for target in tqdm(range(n), desc=desc): + l, r = max(target - window_size, 0), min(target + window_size + 1, n) + remapped_frames = [] + for i in range(l, r, batch_size): + j = min(i + batch_size, r) + source_guide = np.stack([frames_guide[source] for source in range(i, j)]) + target_guide = np.stack([frames_guide[target]] * (j - i)) + source_style = np.stack([frames_style[source] for source in range(i, j)]) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + remapped_frames.append(target_style) + frame = np.concatenate(remapped_frames, axis=0).mean(axis=0) + frame = frame.clip(0, 255).astype("uint8") + if save_path is not None: + Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % target)) \ No newline at end of file diff --git a/diffsynth/extensions/FastBlend/runners/balanced.py b/diffsynth/extensions/FastBlend/runners/balanced.py new file mode 100644 index 0000000000000000000000000000000000000000..1c9a2bb7e438b49c89d0786e858ccf03302fab35 --- /dev/null +++ b/diffsynth/extensions/FastBlend/runners/balanced.py @@ -0,0 +1,46 @@ +from ..patch_match import PyramidPatchMatcher +import os +import numpy as np +from PIL import Image +from tqdm import tqdm + + +class BalancedModeRunner: + def __init__(self): + pass + + def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config, desc="Balanced Mode", save_path=None): + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + **ebsynth_config + ) + # tasks + n = len(frames_style) + tasks = [] + for target in range(n): + for source in range(target - window_size, target + window_size + 1): + if source >= 0 and source < n and source != target: + tasks.append((source, target)) + # run + frames = [(None, 1) for i in range(n)] + for batch_id in tqdm(range(0, len(tasks), batch_size), desc=desc): + tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))] + source_guide = np.stack([frames_guide[source] for source, target in tasks_batch]) + target_guide = np.stack([frames_guide[target] for source, target in tasks_batch]) + source_style = np.stack([frames_style[source] for source, target in tasks_batch]) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + for (source, target), result in zip(tasks_batch, target_style): + frame, weight = frames[target] + if frame is None: + frame = frames_style[target] + frames[target] = ( + frame * (weight / (weight + 1)) + result / (weight + 1), + weight + 1 + ) + if weight + 1 == min(n, target + window_size + 1) - max(0, target - window_size): + frame = frame.clip(0, 255).astype("uint8") + if save_path is not None: + Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % target)) + frames[target] = (None, 1) diff --git a/diffsynth/extensions/FastBlend/runners/fast.py b/diffsynth/extensions/FastBlend/runners/fast.py new file mode 100644 index 0000000000000000000000000000000000000000..2ba5731475ab875929b14181e0c22f4fd466c591 --- /dev/null +++ b/diffsynth/extensions/FastBlend/runners/fast.py @@ -0,0 +1,141 @@ +from ..patch_match import PyramidPatchMatcher +import functools, os +import numpy as np +from PIL import Image +from tqdm import tqdm + + +class TableManager: + def __init__(self): + pass + + def task_list(self, n): + tasks = [] + max_level = 1 + while (1<=n: + break + meta_data = { + "source": i, + "target": j, + "level": level + 1 + } + tasks.append(meta_data) + tasks.sort(key=functools.cmp_to_key(lambda u, v: u["level"]-v["level"])) + return tasks + + def build_remapping_table(self, frames_guide, frames_style, patch_match_engine, batch_size, desc=""): + n = len(frames_guide) + tasks = self.task_list(n) + remapping_table = [[(frames_style[i], 1)] for i in range(n)] + for batch_id in tqdm(range(0, len(tasks), batch_size), desc=desc): + tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))] + source_guide = np.stack([frames_guide[task["source"]] for task in tasks_batch]) + target_guide = np.stack([frames_guide[task["target"]] for task in tasks_batch]) + source_style = np.stack([frames_style[task["source"]] for task in tasks_batch]) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + for task, result in zip(tasks_batch, target_style): + target, level = task["target"], task["level"] + if len(remapping_table[target])==level: + remapping_table[target].append((result, 1)) + else: + frame, weight = remapping_table[target][level] + remapping_table[target][level] = ( + frame * (weight / (weight + 1)) + result / (weight + 1), + weight + 1 + ) + return remapping_table + + def remapping_table_to_blending_table(self, table): + for i in range(len(table)): + for j in range(1, len(table[i])): + frame_1, weight_1 = table[i][j-1] + frame_2, weight_2 = table[i][j] + frame = (frame_1 + frame_2) / 2 + weight = weight_1 + weight_2 + table[i][j] = (frame, weight) + return table + + def tree_query(self, leftbound, rightbound): + node_list = [] + node_index = rightbound + while node_index>=leftbound: + node_level = 0 + while (1<=leftbound: + node_level += 1 + node_list.append((node_index, node_level)) + node_index -= 1<0: + tasks = [] + for m in range(index_style[0]): + tasks.append((index_style[0], m, index_style[0])) + task_group.append(tasks) + # middle frames + for l, r in zip(index_style[:-1], index_style[1:]): + tasks = [] + for m in range(l, r): + tasks.append((l, m, r)) + task_group.append(tasks) + # last frame + tasks = [] + for m in range(index_style[-1], n): + tasks.append((index_style[-1], m, index_style[-1])) + task_group.append(tasks) + return task_group + + def run(self, frames_guide, frames_style, index_style, batch_size, ebsynth_config, save_path=None): + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + use_mean_target_style=False, + use_pairwise_patch_error=True, + **ebsynth_config + ) + # task + index_dict = self.get_index_dict(index_style) + task_group = self.get_task_group(index_style, len(frames_guide)) + # run + for tasks in task_group: + index_start, index_end = min([i[1] for i in tasks]), max([i[1] for i in tasks]) + for batch_id in tqdm(range(0, len(tasks), batch_size), desc=f"Rendering frames {index_start}...{index_end}"): + tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))] + source_guide, target_guide, source_style = [], [], [] + for l, m, r in tasks_batch: + # l -> m + source_guide.append(frames_guide[l]) + target_guide.append(frames_guide[m]) + source_style.append(frames_style[index_dict[l]]) + # r -> m + source_guide.append(frames_guide[r]) + target_guide.append(frames_guide[m]) + source_style.append(frames_style[index_dict[r]]) + source_guide = np.stack(source_guide) + target_guide = np.stack(target_guide) + source_style = np.stack(source_style) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + if save_path is not None: + for frame_l, frame_r, (l, m, r) in zip(target_style[0::2], target_style[1::2], tasks_batch): + weight_l, weight_r = self.get_weight(l, m, r) + frame = frame_l * weight_l + frame_r * weight_r + frame = frame.clip(0, 255).astype("uint8") + Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % m)) + + +class InterpolationModeSingleFrameRunner: + def __init__(self): + pass + + def run(self, frames_guide, frames_style, index_style, batch_size, ebsynth_config, save_path=None): + # check input + tracking_window_size = ebsynth_config["tracking_window_size"] + if tracking_window_size * 2 >= batch_size: + raise ValueError("batch_size should be larger than track_window_size * 2") + frame_style = frames_style[0] + frame_guide = frames_guide[index_style[0]] + patch_match_engine = PyramidPatchMatcher( + image_height=frame_style.shape[0], + image_width=frame_style.shape[1], + channel=3, + **ebsynth_config + ) + # run + frame_id, n = 0, len(frames_guide) + for i in tqdm(range(0, n, batch_size - tracking_window_size * 2), desc=f"Rendering frames 0...{n}"): + if i + batch_size > n: + l, r = max(n - batch_size, 0), n + else: + l, r = i, i + batch_size + source_guide = np.stack([frame_guide] * (r-l)) + target_guide = np.stack([frames_guide[i] for i in range(l, r)]) + source_style = np.stack([frame_style] * (r-l)) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + for i, frame in zip(range(l, r), target_style): + if i==frame_id: + frame = frame.clip(0, 255).astype("uint8") + Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % frame_id)) + frame_id += 1 + if r < n and r-frame_id <= tracking_window_size: + break diff --git a/diffsynth/extensions/RIFE/__init__.py b/diffsynth/extensions/RIFE/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..410550ed946348b9e537138a8b391df97f5ad84e --- /dev/null +++ b/diffsynth/extensions/RIFE/__init__.py @@ -0,0 +1,241 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from PIL import Image + + +def warp(tenInput, tenFlow, device): + backwarp_tenGrid = {} + k = (str(tenFlow.device), str(tenFlow.size())) + if k not in backwarp_tenGrid: + tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device).view( + 1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1) + tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device).view( + 1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3]) + backwarp_tenGrid[k] = torch.cat( + [tenHorizontal, tenVertical], 1).to(device) + + tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0), + tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1) + + g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1) + return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True) + + +def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1): + return nn.Sequential( + nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, + padding=padding, dilation=dilation, bias=True), + nn.PReLU(out_planes) + ) + + +class IFBlock(nn.Module): + def __init__(self, in_planes, c=64): + super(IFBlock, self).__init__() + self.conv0 = nn.Sequential(conv(in_planes, c//2, 3, 2, 1), conv(c//2, c, 3, 2, 1),) + self.convblock0 = nn.Sequential(conv(c, c), conv(c, c)) + self.convblock1 = nn.Sequential(conv(c, c), conv(c, c)) + self.convblock2 = nn.Sequential(conv(c, c), conv(c, c)) + self.convblock3 = nn.Sequential(conv(c, c), conv(c, c)) + self.conv1 = nn.Sequential(nn.ConvTranspose2d(c, c//2, 4, 2, 1), nn.PReLU(c//2), nn.ConvTranspose2d(c//2, 4, 4, 2, 1)) + self.conv2 = nn.Sequential(nn.ConvTranspose2d(c, c//2, 4, 2, 1), nn.PReLU(c//2), nn.ConvTranspose2d(c//2, 1, 4, 2, 1)) + + def forward(self, x, flow, scale=1): + x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) + flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 1. / scale + feat = self.conv0(torch.cat((x, flow), 1)) + feat = self.convblock0(feat) + feat + feat = self.convblock1(feat) + feat + feat = self.convblock2(feat) + feat + feat = self.convblock3(feat) + feat + flow = self.conv1(feat) + mask = self.conv2(feat) + flow = F.interpolate(flow, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * scale + mask = F.interpolate(mask, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) + return flow, mask + + +class IFNet(nn.Module): + def __init__(self): + super(IFNet, self).__init__() + self.block0 = IFBlock(7+4, c=90) + self.block1 = IFBlock(7+4, c=90) + self.block2 = IFBlock(7+4, c=90) + self.block_tea = IFBlock(10+4, c=90) + + def forward(self, x, scale_list=[4, 2, 1], training=False): + if training == False: + channel = x.shape[1] // 2 + img0 = x[:, :channel] + img1 = x[:, channel:] + flow_list = [] + merged = [] + mask_list = [] + warped_img0 = img0 + warped_img1 = img1 + flow = (x[:, :4]).detach() * 0 + mask = (x[:, :1]).detach() * 0 + block = [self.block0, self.block1, self.block2] + for i in range(3): + f0, m0 = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], mask), 1), flow, scale=scale_list[i]) + f1, m1 = block[i](torch.cat((warped_img1[:, :3], warped_img0[:, :3], -mask), 1), torch.cat((flow[:, 2:4], flow[:, :2]), 1), scale=scale_list[i]) + flow = flow + (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2 + mask = mask + (m0 + (-m1)) / 2 + mask_list.append(mask) + flow_list.append(flow) + warped_img0 = warp(img0, flow[:, :2], device=x.device) + warped_img1 = warp(img1, flow[:, 2:4], device=x.device) + merged.append((warped_img0, warped_img1)) + ''' + c0 = self.contextnet(img0, flow[:, :2]) + c1 = self.contextnet(img1, flow[:, 2:4]) + tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1) + res = tmp[:, 1:4] * 2 - 1 + ''' + for i in range(3): + mask_list[i] = torch.sigmoid(mask_list[i]) + merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i]) + return flow_list, mask_list[2], merged + + def state_dict_converter(self): + return IFNetStateDictConverter() + + +class IFNetStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + state_dict_ = {k.replace("module.", ""): v for k, v in state_dict.items()} + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) + + +class RIFEInterpolater: + def __init__(self, model, device="cuda"): + self.model = model + self.device = device + # IFNet only does not support float16 + self.torch_dtype = torch.float32 + + @staticmethod + def from_model_manager(model_manager): + return RIFEInterpolater(model_manager.RIFE, device=model_manager.device) + + def process_image(self, image): + width, height = image.size + if width % 32 != 0 or height % 32 != 0: + width = (width + 31) // 32 + height = (height + 31) // 32 + image = image.resize((width, height)) + image = torch.Tensor(np.array(image, dtype=np.float32)[:, :, [2,1,0]] / 255).permute(2, 0, 1) + return image + + def process_images(self, images): + images = [self.process_image(image) for image in images] + images = torch.stack(images) + return images + + def decode_images(self, images): + images = (images[:, [2,1,0]].permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8) + images = [Image.fromarray(image) for image in images] + return images + + def add_interpolated_images(self, images, interpolated_images): + output_images = [] + for image, interpolated_image in zip(images, interpolated_images): + output_images.append(image) + output_images.append(interpolated_image) + output_images.append(images[-1]) + return output_images + + + @torch.no_grad() + def interpolate_(self, images, scale=1.0): + input_tensor = self.process_images(images) + input_tensor = torch.cat((input_tensor[:-1], input_tensor[1:]), dim=1) + input_tensor = input_tensor.to(device=self.device, dtype=self.torch_dtype) + flow, mask, merged = self.model(input_tensor, [4/scale, 2/scale, 1/scale]) + output_images = self.decode_images(merged[2].cpu()) + if output_images[0].size != images[0].size: + output_images = [image.resize(images[0].size) for image in output_images] + return output_images + + + @torch.no_grad() + def interpolate(self, images, scale=1.0, batch_size=4, num_iter=1, progress_bar=lambda x:x): + # Preprocess + processed_images = self.process_images(images) + + for iter in range(num_iter): + # Input + input_tensor = torch.cat((processed_images[:-1], processed_images[1:]), dim=1) + + # Interpolate + output_tensor = [] + for batch_id in progress_bar(range(0, input_tensor.shape[0], batch_size)): + batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) + batch_input_tensor = input_tensor[batch_id: batch_id_] + batch_input_tensor = batch_input_tensor.to(device=self.device, dtype=self.torch_dtype) + flow, mask, merged = self.model(batch_input_tensor, [4/scale, 2/scale, 1/scale]) + output_tensor.append(merged[2].cpu()) + + # Output + output_tensor = torch.concat(output_tensor, dim=0).clip(0, 1) + processed_images = self.add_interpolated_images(processed_images, output_tensor) + processed_images = torch.stack(processed_images) + + # To images + output_images = self.decode_images(processed_images) + if output_images[0].size != images[0].size: + output_images = [image.resize(images[0].size) for image in output_images] + return output_images + + +class RIFESmoother(RIFEInterpolater): + def __init__(self, model, device="cuda"): + super(RIFESmoother, self).__init__(model, device=device) + + @staticmethod + def from_model_manager(model_manager): + return RIFESmoother(model_manager.RIFE, device=model_manager.device) + + def process_tensors(self, input_tensor, scale=1.0, batch_size=4): + output_tensor = [] + for batch_id in range(0, input_tensor.shape[0], batch_size): + batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) + batch_input_tensor = input_tensor[batch_id: batch_id_] + batch_input_tensor = batch_input_tensor.to(device=self.device, dtype=self.torch_dtype) + flow, mask, merged = self.model(batch_input_tensor, [4/scale, 2/scale, 1/scale]) + output_tensor.append(merged[2].cpu()) + output_tensor = torch.concat(output_tensor, dim=0) + return output_tensor + + @torch.no_grad() + def __call__(self, rendered_frames, scale=1.0, batch_size=4, num_iter=1, **kwargs): + # Preprocess + processed_images = self.process_images(rendered_frames) + + for iter in range(num_iter): + # Input + input_tensor = torch.cat((processed_images[:-2], processed_images[2:]), dim=1) + + # Interpolate + output_tensor = self.process_tensors(input_tensor, scale=scale, batch_size=batch_size) + + # Blend + input_tensor = torch.cat((processed_images[1:-1], output_tensor), dim=1) + output_tensor = self.process_tensors(input_tensor, scale=scale, batch_size=batch_size) + + # Add to frames + processed_images[1:-1] = output_tensor + + # To images + output_images = self.decode_images(processed_images) + if output_images[0].size != rendered_frames[0].size: + output_images = [image.resize(rendered_frames[0].size) for image in output_images] + return output_images diff --git a/diffsynth/models/__init__.py b/diffsynth/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e634518e5d03d27085dbb70f2138998ff581424d --- /dev/null +++ b/diffsynth/models/__init__.py @@ -0,0 +1,814 @@ +import torch, os, json +from safetensors import safe_open +from typing_extensions import Literal, TypeAlias +from typing import List + +from .downloader import download_from_huggingface, download_from_modelscope + +from .sd_text_encoder import SDTextEncoder +from .sd_unet import SDUNet +from .sd_vae_encoder import SDVAEEncoder +from .sd_vae_decoder import SDVAEDecoder +from .sd_lora import SDLoRA + +from .sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2 +from .sdxl_unet import SDXLUNet +from .sdxl_vae_decoder import SDXLVAEDecoder +from .sdxl_vae_encoder import SDXLVAEEncoder + +from .sd3_text_encoder import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3 +from .sd3_dit import SD3DiT +from .sd3_vae_decoder import SD3VAEDecoder +from .sd3_vae_encoder import SD3VAEEncoder + +from .sd_controlnet import SDControlNet + +from .sd_motion import SDMotionModel +from .sdxl_motion import SDXLMotionModel + +from .svd_image_encoder import SVDImageEncoder +from .svd_unet import SVDUNet +from .svd_vae_decoder import SVDVAEDecoder +from .svd_vae_encoder import SVDVAEEncoder + +from .sd_ipadapter import SDIpAdapter, IpAdapterCLIPImageEmbedder +from .sdxl_ipadapter import SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder + +from .hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder +from .hunyuan_dit import HunyuanDiT +from .kolors_text_encoder import ChatGLMModel + + +preset_models_on_huggingface = { + "HunyuanDiT": [ + ("Tencent-Hunyuan/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"), + ("Tencent-Hunyuan/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"), + ("Tencent-Hunyuan/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"), + ("Tencent-Hunyuan/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"), + ], + "stable-video-diffusion-img2vid-xt": [ + ("stabilityai/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"), + ], + "ExVideo-SVD-128f-v1": [ + ("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"), + ], +} +preset_models_on_modelscope = { + # Hunyuan DiT + "HunyuanDiT": [ + ("modelscope/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"), + ("modelscope/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"), + ("modelscope/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"), + ("modelscope/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"), + ], + # Stable Video Diffusion + "stable-video-diffusion-img2vid-xt": [ + ("AI-ModelScope/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"), + ], + # ExVideo + "ExVideo-SVD-128f-v1": [ + ("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"), + ], + # Stable Diffusion + "StableDiffusion_v15": [ + ("AI-ModelScope/stable-diffusion-v1-5", "v1-5-pruned-emaonly.safetensors", "models/stable_diffusion"), + ], + "DreamShaper_8": [ + ("sd_lora/dreamshaper_8", "dreamshaper_8.safetensors", "models/stable_diffusion"), + ], + "AingDiffusion_v12": [ + ("sd_lora/aingdiffusion_v12", "aingdiffusion_v12.safetensors", "models/stable_diffusion"), + ], + "Flat2DAnimerge_v45Sharp": [ + ("sd_lora/Flat-2D-Animerge", "flat2DAnimerge_v45Sharp.safetensors", "models/stable_diffusion"), + ], + # Textual Inversion + "TextualInversion_VeryBadImageNegative_v1.3": [ + ("sd_lora/verybadimagenegative_v1.3", "verybadimagenegative_v1.3.pt", "models/textual_inversion"), + ], + # Stable Diffusion XL + "StableDiffusionXL_v1": [ + ("AI-ModelScope/stable-diffusion-xl-base-1.0", "sd_xl_base_1.0.safetensors", "models/stable_diffusion_xl"), + ], + "BluePencilXL_v200": [ + ("sd_lora/bluePencilXL_v200", "bluePencilXL_v200.safetensors", "models/stable_diffusion_xl"), + ], + "StableDiffusionXL_Turbo": [ + ("AI-ModelScope/sdxl-turbo", "sd_xl_turbo_1.0_fp16.safetensors", "models/stable_diffusion_xl_turbo"), + ], + # Stable Diffusion 3 + "StableDiffusion3": [ + ("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips_t5xxlfp16.safetensors", "models/stable_diffusion_3"), + ], + "StableDiffusion3_without_T5": [ + ("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips.safetensors", "models/stable_diffusion_3"), + ], + # ControlNet + "ControlNet_v11f1p_sd15_depth": [ + ("AI-ModelScope/ControlNet-v1-1", "control_v11f1p_sd15_depth.pth", "models/ControlNet"), + ("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators") + ], + "ControlNet_v11p_sd15_softedge": [ + ("AI-ModelScope/ControlNet-v1-1", "control_v11p_sd15_softedge.pth", "models/ControlNet"), + ("sd_lora/Annotators", "ControlNetHED.pth", "models/Annotators") + ], + "ControlNet_v11f1e_sd15_tile": [ + ("AI-ModelScope/ControlNet-v1-1", "control_v11f1e_sd15_tile.pth", "models/ControlNet") + ], + "ControlNet_v11p_sd15_lineart": [ + ("AI-ModelScope/ControlNet-v1-1", "control_v11p_sd15_lineart.pth", "models/ControlNet"), + ("sd_lora/Annotators", "sk_model.pth", "models/Annotators"), + ("sd_lora/Annotators", "sk_model2.pth", "models/Annotators") + ], + # AnimateDiff + "AnimateDiff_v2": [ + ("Shanghai_AI_Laboratory/animatediff", "mm_sd_v15_v2.ckpt", "models/AnimateDiff"), + ], + "AnimateDiff_xl_beta": [ + ("Shanghai_AI_Laboratory/animatediff", "mm_sdxl_v10_beta.ckpt", "models/AnimateDiff"), + ], + # RIFE + "RIFE": [ + ("Damo_XR_Lab/cv_rife_video-frame-interpolation", "flownet.pkl", "models/RIFE"), + ], + # Beautiful Prompt + "BeautifulPrompt": [ + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "generation_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "model.safetensors", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "special_tokens_map.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ], + # Translator + "opus-mt-zh-en": [ + ("moxying/opus-mt-zh-en", "config.json", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "generation_config.json", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "metadata.json", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "pytorch_model.bin", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "source.spm", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "target.spm", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "tokenizer_config.json", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "vocab.json", "models/translator/opus-mt-zh-en"), + ], + # IP-Adapter + "IP-Adapter-SD": [ + ("AI-ModelScope/IP-Adapter", "models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion/image_encoder"), + ("AI-ModelScope/IP-Adapter", "models/ip-adapter_sd15.bin", "models/IpAdapter/stable_diffusion"), + ], + "IP-Adapter-SDXL": [ + ("AI-ModelScope/IP-Adapter", "sdxl_models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion_xl/image_encoder"), + ("AI-ModelScope/IP-Adapter", "sdxl_models/ip-adapter_sdxl.bin", "models/IpAdapter/stable_diffusion_xl"), + ], + # Kolors + "Kolors": [ + ("Kwai-Kolors/Kolors", "text_encoder/config.json", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model.bin.index.json", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00001-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00002-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00003-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00004-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00005-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00006-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00007-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "unet/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/unet"), + ("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/vae"), + ], + "SDXL-vae-fp16-fix": [ + ("AI-ModelScope/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors", "models/sdxl-vae-fp16-fix") + ], +} +Preset_model_id: TypeAlias = Literal[ + "HunyuanDiT", + "stable-video-diffusion-img2vid-xt", + "ExVideo-SVD-128f-v1", + "StableDiffusion_v15", + "DreamShaper_8", + "AingDiffusion_v12", + "Flat2DAnimerge_v45Sharp", + "TextualInversion_VeryBadImageNegative_v1.3", + "StableDiffusionXL_v1", + "BluePencilXL_v200", + "StableDiffusionXL_Turbo", + "ControlNet_v11f1p_sd15_depth", + "ControlNet_v11p_sd15_softedge", + "ControlNet_v11f1e_sd15_tile", + "ControlNet_v11p_sd15_lineart", + "AnimateDiff_v2", + "AnimateDiff_xl_beta", + "RIFE", + "BeautifulPrompt", + "opus-mt-zh-en", + "IP-Adapter-SD", + "IP-Adapter-SDXL", + "StableDiffusion3", + "StableDiffusion3_without_T5", + "Kolors", + "SDXL-vae-fp16-fix", +] +Preset_model_website: TypeAlias = Literal[ + "HuggingFace", + "ModelScope", +] +website_to_preset_models = { + "HuggingFace": preset_models_on_huggingface, + "ModelScope": preset_models_on_modelscope, +} +website_to_download_fn = { + "HuggingFace": download_from_huggingface, + "ModelScope": download_from_modelscope, +} + + +def download_models( + model_id_list: List[Preset_model_id] = [], + downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"], +): + downloaded_files = [] + for model_id in model_id_list: + for website in downloading_priority: + if model_id in website_to_preset_models[website]: + for model_id, origin_file_path, local_dir in website_to_preset_models[website][model_id]: + # Check if the file is downloaded. + file_to_download = os.path.join(local_dir, os.path.basename(origin_file_path)) + if file_to_download in downloaded_files: + continue + # Download + website_to_download_fn[website](model_id, origin_file_path, local_dir) + if os.path.basename(origin_file_path) in os.listdir(local_dir): + downloaded_files.append(file_to_download) + return downloaded_files + + +class ModelManager: + def __init__( + self, + torch_dtype=torch.float16, + device="cuda", + model_id_list: List[Preset_model_id] = [], + downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"], + file_path_list: List[str] = [], + ): + self.torch_dtype = torch_dtype + self.device = device + self.model = {} + self.model_path = {} + self.textual_inversion_dict = {} + downloaded_files = download_models(model_id_list, downloading_priority) + self.load_models(downloaded_files + file_path_list) + + def load_model_from_origin( + self, + download_from: Preset_model_website = "ModelScope", + model_id = "", + origin_file_path = "", + local_dir = "" + ): + website_to_download_fn[download_from](model_id, origin_file_path, local_dir) + file_to_download = os.path.join(local_dir, os.path.basename(origin_file_path)) + self.load_model(file_to_download) + + def is_stable_video_diffusion(self, state_dict): + param_name = "model.diffusion_model.output_blocks.9.1.time_stack.0.norm_in.weight" + return param_name in state_dict + + def is_RIFE(self, state_dict): + param_name = "block_tea.convblock3.0.1.weight" + return param_name in state_dict or ("module." + param_name) in state_dict + + def is_beautiful_prompt(self, state_dict): + param_name = "transformer.h.9.self_attention.query_key_value.weight" + return param_name in state_dict + + def is_stabe_diffusion_xl(self, state_dict): + param_name = "conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight" + return param_name in state_dict + + def is_stable_diffusion(self, state_dict): + if self.is_stabe_diffusion_xl(state_dict): + return False + param_name = "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.weight" + return param_name in state_dict + + def is_controlnet(self, state_dict): + param_name = "control_model.time_embed.0.weight" + return param_name in state_dict + + def is_animatediff(self, state_dict): + param_name = "mid_block.motion_modules.0.temporal_transformer.proj_out.weight" + return param_name in state_dict + + def is_animatediff_xl(self, state_dict): + param_name = "up_blocks.2.motion_modules.2.temporal_transformer.transformer_blocks.0.ff_norm.weight" + return param_name in state_dict + + def is_sd_lora(self, state_dict): + param_name = "lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2.lora_up.weight" + return param_name in state_dict + + def is_translator(self, state_dict): + param_name = "model.encoder.layers.5.self_attn_layer_norm.weight" + return param_name in state_dict and len(state_dict) == 258 + + def is_ipadapter(self, state_dict): + return "image_proj" in state_dict and "ip_adapter" in state_dict and state_dict["image_proj"]["proj.weight"].shape == torch.Size([3072, 1024]) + + def is_ipadapter_image_encoder(self, state_dict): + param_name = "vision_model.encoder.layers.31.self_attn.v_proj.weight" + return param_name in state_dict and len(state_dict) == 521 + + def is_ipadapter_xl(self, state_dict): + return "image_proj" in state_dict and "ip_adapter" in state_dict and state_dict["image_proj"]["proj.weight"].shape == torch.Size([8192, 1280]) + + def is_ipadapter_xl_image_encoder(self, state_dict): + param_name = "vision_model.encoder.layers.47.self_attn.v_proj.weight" + return param_name in state_dict and len(state_dict) == 777 + + def is_hunyuan_dit_clip_text_encoder(self, state_dict): + param_name = "bert.encoder.layer.23.attention.output.dense.weight" + return param_name in state_dict + + def is_hunyuan_dit_t5_text_encoder(self, state_dict): + param_name = "encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight" + param_name_ = "decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight" + return param_name in state_dict and param_name_ in state_dict + + def is_hunyuan_dit(self, state_dict): + param_name = "final_layer.adaLN_modulation.1.weight" + return param_name in state_dict + + def is_diffusers_vae(self, state_dict): + param_name = "quant_conv.weight" + return param_name in state_dict + + def is_ExVideo_StableVideoDiffusion(self, state_dict): + param_name = "blocks.185.positional_embedding.embeddings" + return param_name in state_dict + + def is_stable_diffusion_3(self, state_dict): + param_names = [ + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight", + "model.diffusion_model.joint_blocks.9.x_block.mlp.fc2.weight", + "first_stage_model.encoder.mid.block_2.norm2.weight", + "first_stage_model.decoder.mid.block_2.norm2.weight", + ] + for param_name in param_names: + if param_name not in state_dict: + return False + return True + + def is_stable_diffusion_3_t5(self, state_dict): + param_name = "encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight" + return param_name in state_dict + + def is_kolors_text_encoder(self, file_path): + file_list = os.listdir(file_path) + if "config.json" in file_list: + try: + with open(os.path.join(file_path, "config.json"), "r") as f: + config = json.load(f) + if config.get("model_type") == "chatglm": + return True + except: + pass + return False + + def is_kolors_unet(self, state_dict): + return "up_blocks.2.resnets.2.time_emb_proj.weight" in state_dict and "encoder_hid_proj.weight" in state_dict + + def load_stable_video_diffusion(self, state_dict, components=None, file_path="", add_positional_conv=None): + component_dict = { + "image_encoder": SVDImageEncoder, + "unet": SVDUNet, + "vae_decoder": SVDVAEDecoder, + "vae_encoder": SVDVAEEncoder, + } + if components is None: + components = ["image_encoder", "unet", "vae_decoder", "vae_encoder"] + for component in components: + if component == "unet": + self.model[component] = component_dict[component](add_positional_conv=add_positional_conv) + self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict, add_positional_conv=add_positional_conv), strict=False) + else: + self.model[component] = component_dict[component]() + self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict)) + self.model[component].to(self.torch_dtype).to(self.device) + self.model_path[component] = file_path + + def load_stable_diffusion(self, state_dict, components=None, file_path=""): + component_dict = { + "text_encoder": SDTextEncoder, + "unet": SDUNet, + "vae_decoder": SDVAEDecoder, + "vae_encoder": SDVAEEncoder, + "refiner": SDXLUNet, + } + if components is None: + components = ["text_encoder", "unet", "vae_decoder", "vae_encoder"] + for component in components: + if component == "text_encoder": + # Add additional token embeddings to text encoder + token_embeddings = [state_dict["cond_stage_model.transformer.text_model.embeddings.token_embedding.weight"]] + for keyword in self.textual_inversion_dict: + _, embeddings = self.textual_inversion_dict[keyword] + token_embeddings.append(embeddings.to(dtype=token_embeddings[0].dtype)) + token_embeddings = torch.concat(token_embeddings, dim=0) + state_dict["cond_stage_model.transformer.text_model.embeddings.token_embedding.weight"] = token_embeddings + self.model[component] = component_dict[component](vocab_size=token_embeddings.shape[0]) + self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict)) + self.model[component].to(self.torch_dtype).to(self.device) + else: + self.model[component] = component_dict[component]() + self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict)) + self.model[component].to(self.torch_dtype).to(self.device) + self.model_path[component] = file_path + + def load_stable_diffusion_xl(self, state_dict, components=None, file_path=""): + component_dict = { + "text_encoder": SDXLTextEncoder, + "text_encoder_2": SDXLTextEncoder2, + "unet": SDXLUNet, + "vae_decoder": SDXLVAEDecoder, + "vae_encoder": SDXLVAEEncoder, + } + if components is None: + components = ["text_encoder", "text_encoder_2", "unet", "vae_decoder", "vae_encoder"] + for component in components: + self.model[component] = component_dict[component]() + self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict)) + if component in ["vae_decoder", "vae_encoder"]: + # These two model will output nan when float16 is enabled. + # The precision problem happens in the last three resnet blocks. + # I do not know how to solve this problem. + self.model[component].to(torch.float32).to(self.device) + else: + self.model[component].to(self.torch_dtype).to(self.device) + self.model_path[component] = file_path + + def load_controlnet(self, state_dict, file_path=""): + component = "controlnet" + if component not in self.model: + self.model[component] = [] + self.model_path[component] = [] + model = SDControlNet() + model.load_state_dict(model.state_dict_converter().from_civitai(state_dict)) + model.to(self.torch_dtype).to(self.device) + self.model[component].append(model) + self.model_path[component].append(file_path) + + def load_animatediff(self, state_dict, file_path=""): + component = "motion_modules" + model = SDMotionModel() + model.load_state_dict(model.state_dict_converter().from_civitai(state_dict)) + model.to(self.torch_dtype).to(self.device) + self.model[component] = model + self.model_path[component] = file_path + + def load_animatediff_xl(self, state_dict, file_path=""): + component = "motion_modules_xl" + model = SDXLMotionModel() + model.load_state_dict(model.state_dict_converter().from_civitai(state_dict)) + model.to(self.torch_dtype).to(self.device) + self.model[component] = model + self.model_path[component] = file_path + + def load_beautiful_prompt(self, state_dict, file_path=""): + component = "beautiful_prompt" + from transformers import AutoModelForCausalLM + model_folder = os.path.dirname(file_path) + model = AutoModelForCausalLM.from_pretrained( + model_folder, state_dict=state_dict, local_files_only=True, torch_dtype=self.torch_dtype + ).to(self.device).eval() + self.model[component] = model + self.model_path[component] = file_path + + def load_RIFE(self, state_dict, file_path=""): + component = "RIFE" + from ..extensions.RIFE import IFNet + model = IFNet().eval() + model.load_state_dict(model.state_dict_converter().from_civitai(state_dict)) + model.to(torch.float32).to(self.device) + self.model[component] = model + self.model_path[component] = file_path + + def load_sd_lora(self, state_dict, alpha): + SDLoRA().add_lora_to_text_encoder(self.model["text_encoder"], state_dict, alpha=alpha, device=self.device) + SDLoRA().add_lora_to_unet(self.model["unet"], state_dict, alpha=alpha, device=self.device) + + def load_translator(self, state_dict, file_path=""): + # This model is lightweight, we do not place it on GPU. + component = "translator" + from transformers import AutoModelForSeq2SeqLM + model_folder = os.path.dirname(file_path) + model = AutoModelForSeq2SeqLM.from_pretrained(model_folder).eval() + self.model[component] = model + self.model_path[component] = file_path + + def load_ipadapter(self, state_dict, file_path=""): + component = "ipadapter" + model = SDIpAdapter() + model.load_state_dict(model.state_dict_converter().from_civitai(state_dict)) + model.to(self.torch_dtype).to(self.device) + self.model[component] = model + self.model_path[component] = file_path + + def load_ipadapter_image_encoder(self, state_dict, file_path=""): + component = "ipadapter_image_encoder" + model = IpAdapterCLIPImageEmbedder() + model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict)) + model.to(self.torch_dtype).to(self.device) + self.model[component] = model + self.model_path[component] = file_path + + def load_ipadapter_xl(self, state_dict, file_path=""): + component = "ipadapter_xl" + model = SDXLIpAdapter() + model.load_state_dict(model.state_dict_converter().from_civitai(state_dict)) + model.to(self.torch_dtype).to(self.device) + self.model[component] = model + self.model_path[component] = file_path + + def load_ipadapter_xl_image_encoder(self, state_dict, file_path=""): + component = "ipadapter_xl_image_encoder" + model = IpAdapterXLCLIPImageEmbedder() + model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict)) + model.to(self.torch_dtype).to(self.device) + self.model[component] = model + self.model_path[component] = file_path + + def load_hunyuan_dit_clip_text_encoder(self, state_dict, file_path=""): + component = "hunyuan_dit_clip_text_encoder" + model = HunyuanDiTCLIPTextEncoder() + model.load_state_dict(model.state_dict_converter().from_civitai(state_dict)) + model.to(self.torch_dtype).to(self.device) + self.model[component] = model + self.model_path[component] = file_path + + def load_hunyuan_dit_t5_text_encoder(self, state_dict, file_path=""): + component = "hunyuan_dit_t5_text_encoder" + model = HunyuanDiTT5TextEncoder() + model.load_state_dict(model.state_dict_converter().from_civitai(state_dict)) + model.to(self.torch_dtype).to(self.device) + self.model[component] = model + self.model_path[component] = file_path + + def load_hunyuan_dit(self, state_dict, file_path=""): + component = "hunyuan_dit" + model = HunyuanDiT() + model.load_state_dict(model.state_dict_converter().from_civitai(state_dict)) + model.to(self.torch_dtype).to(self.device) + self.model[component] = model + self.model_path[component] = file_path + + def load_diffusers_vae(self, state_dict, file_path=""): + # TODO: detect SD and SDXL + component = "vae_encoder" + model = SDXLVAEEncoder() + model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict)) + model.to(torch.float32).to(self.device) + self.model[component] = model + self.model_path[component] = file_path + component = "vae_decoder" + model = SDXLVAEDecoder() + model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict)) + model.to(torch.float32).to(self.device) + self.model[component] = model + self.model_path[component] = file_path + + def load_ExVideo_StableVideoDiffusion(self, state_dict, file_path=""): + unet_state_dict = self.model["unet"].state_dict() + self.model["unet"].to("cpu") + del self.model["unet"] + add_positional_conv = state_dict["blocks.185.positional_embedding.embeddings"].shape[0] + self.model["unet"] = SVDUNet(add_positional_conv=add_positional_conv) + self.model["unet"].load_state_dict(unet_state_dict, strict=False) + self.model["unet"].load_state_dict(state_dict, strict=False) + self.model["unet"].to(self.torch_dtype).to(self.device) + + def load_stable_diffusion_3(self, state_dict, components=None, file_path=""): + component_dict = { + "sd3_text_encoder_1": SD3TextEncoder1, + "sd3_text_encoder_2": SD3TextEncoder2, + "sd3_text_encoder_3": SD3TextEncoder3, + "sd3_dit": SD3DiT, + "sd3_vae_decoder": SD3VAEDecoder, + "sd3_vae_encoder": SD3VAEEncoder, + } + if components is None: + components = ["sd3_text_encoder_1", "sd3_text_encoder_2", "sd3_text_encoder_3", "sd3_dit", "sd3_vae_decoder", "sd3_vae_encoder"] + for component in components: + if component == "sd3_text_encoder_3": + if "text_encoders.t5xxl.transformer.encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight" not in state_dict: + continue + if component == "sd3_text_encoder_1": + # Add additional token embeddings to text encoder + token_embeddings = [state_dict["text_encoders.clip_l.transformer.text_model.embeddings.token_embedding.weight"]] + for keyword in self.textual_inversion_dict: + _, embeddings = self.textual_inversion_dict[keyword] + token_embeddings.append(embeddings.to(dtype=token_embeddings[0].dtype)) + token_embeddings = torch.concat(token_embeddings, dim=0) + state_dict["text_encoders.clip_l.transformer.text_model.embeddings.token_embedding.weight"] = token_embeddings + self.model[component] = component_dict[component](vocab_size=token_embeddings.shape[0]) + self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict)) + self.model[component].to(self.torch_dtype).to(self.device) + else: + self.model[component] = component_dict[component]() + self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict)) + self.model[component].to(self.torch_dtype).to(self.device) + self.model_path[component] = file_path + + def load_stable_diffusion_3_t5(self, state_dict, file_path=""): + component = "sd3_text_encoder_3" + model = SD3TextEncoder3() + model.load_state_dict(model.state_dict_converter().from_civitai(state_dict)) + model.to(self.torch_dtype).to(self.device) + self.model[component] = model + self.model_path[component] = file_path + + def load_kolors_text_encoder(self, state_dict=None, file_path=""): + component = "kolors_text_encoder" + model = ChatGLMModel.from_pretrained(file_path, torch_dtype=self.torch_dtype) + model = model.to(dtype=self.torch_dtype, device=self.device) + self.model[component] = model + self.model_path[component] = file_path + + def load_kolors_unet(self, state_dict, file_path=""): + component = "kolors_unet" + model = SDXLUNet(is_kolors=True) + model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict)) + model.to(self.torch_dtype).to(self.device) + self.model[component] = model + self.model_path[component] = file_path + + def search_for_embeddings(self, state_dict): + embeddings = [] + for k in state_dict: + if isinstance(state_dict[k], torch.Tensor): + embeddings.append(state_dict[k]) + elif isinstance(state_dict[k], dict): + embeddings += self.search_for_embeddings(state_dict[k]) + return embeddings + + def load_textual_inversions(self, folder): + # Store additional tokens here + self.textual_inversion_dict = {} + + # Load every textual inversion file + for file_name in os.listdir(folder): + if os.path.isdir(os.path.join(folder, file_name)) or \ + not (file_name.endswith(".bin") or \ + file_name.endswith(".safetensors") or \ + file_name.endswith(".pth") or \ + file_name.endswith(".pt")): + continue + keyword = os.path.splitext(file_name)[0] + state_dict = load_state_dict(os.path.join(folder, file_name)) + + # Search for embeddings + for embeddings in self.search_for_embeddings(state_dict): + if len(embeddings.shape) == 2 and embeddings.shape[1] == 768: + tokens = [f"{keyword}_{i}" for i in range(embeddings.shape[0])] + self.textual_inversion_dict[keyword] = (tokens, embeddings) + break + + def load_model(self, file_path, components=None, lora_alphas=[]): + if os.path.isdir(file_path): + if self.is_kolors_text_encoder(file_path): + self.load_kolors_text_encoder(file_path=file_path) + return + state_dict = load_state_dict(file_path, torch_dtype=self.torch_dtype) + if self.is_stable_video_diffusion(state_dict): + self.load_stable_video_diffusion(state_dict, file_path=file_path) + elif self.is_animatediff(state_dict): + self.load_animatediff(state_dict, file_path=file_path) + elif self.is_animatediff_xl(state_dict): + self.load_animatediff_xl(state_dict, file_path=file_path) + elif self.is_controlnet(state_dict): + self.load_controlnet(state_dict, file_path=file_path) + elif self.is_stabe_diffusion_xl(state_dict): + self.load_stable_diffusion_xl(state_dict, components=components, file_path=file_path) + elif self.is_stable_diffusion(state_dict): + self.load_stable_diffusion(state_dict, components=components, file_path=file_path) + elif self.is_sd_lora(state_dict): + self.load_sd_lora(state_dict, alpha=lora_alphas.pop(0)) + elif self.is_beautiful_prompt(state_dict): + self.load_beautiful_prompt(state_dict, file_path=file_path) + elif self.is_RIFE(state_dict): + self.load_RIFE(state_dict, file_path=file_path) + elif self.is_translator(state_dict): + self.load_translator(state_dict, file_path=file_path) + elif self.is_ipadapter(state_dict): + self.load_ipadapter(state_dict, file_path=file_path) + elif self.is_ipadapter_image_encoder(state_dict): + self.load_ipadapter_image_encoder(state_dict, file_path=file_path) + elif self.is_ipadapter_xl(state_dict): + self.load_ipadapter_xl(state_dict, file_path=file_path) + elif self.is_ipadapter_xl_image_encoder(state_dict): + self.load_ipadapter_xl_image_encoder(state_dict, file_path=file_path) + elif self.is_hunyuan_dit_clip_text_encoder(state_dict): + self.load_hunyuan_dit_clip_text_encoder(state_dict, file_path=file_path) + elif self.is_hunyuan_dit_t5_text_encoder(state_dict): + self.load_hunyuan_dit_t5_text_encoder(state_dict, file_path=file_path) + elif self.is_hunyuan_dit(state_dict): + self.load_hunyuan_dit(state_dict, file_path=file_path) + elif self.is_diffusers_vae(state_dict): + self.load_diffusers_vae(state_dict, file_path=file_path) + elif self.is_ExVideo_StableVideoDiffusion(state_dict): + self.load_ExVideo_StableVideoDiffusion(state_dict, file_path=file_path) + elif self.is_stable_diffusion_3(state_dict): + self.load_stable_diffusion_3(state_dict, components=components, file_path=file_path) + elif self.is_stable_diffusion_3_t5(state_dict): + self.load_stable_diffusion_3_t5(state_dict, file_path=file_path) + elif self.is_kolors_unet(state_dict): + self.load_kolors_unet(state_dict, file_path=file_path) + + def load_models(self, file_path_list, lora_alphas=[]): + for file_path in file_path_list: + self.load_model(file_path, lora_alphas=lora_alphas) + + def to(self, device): + for component in self.model: + if isinstance(self.model[component], list): + for model in self.model[component]: + model.to(device) + else: + self.model[component].to(device) + torch.cuda.empty_cache() + + def get_model_with_model_path(self, model_path): + for component in self.model_path: + if isinstance(self.model_path[component], str): + if os.path.samefile(self.model_path[component], model_path): + return self.model[component] + elif isinstance(self.model_path[component], list): + for i, model_path_ in enumerate(self.model_path[component]): + if os.path.samefile(model_path_, model_path): + return self.model[component][i] + raise ValueError(f"Please load model {model_path} before you use it.") + + def __getattr__(self, __name): + if __name in self.model: + return self.model[__name] + else: + return super.__getattribute__(__name) + + +def load_state_dict(file_path, torch_dtype=None): + if file_path.endswith(".safetensors"): + return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype) + else: + return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype) + + +def load_state_dict_from_safetensors(file_path, torch_dtype=None): + state_dict = {} + with safe_open(file_path, framework="pt", device="cpu") as f: + for k in f.keys(): + state_dict[k] = f.get_tensor(k) + if torch_dtype is not None: + state_dict[k] = state_dict[k].to(torch_dtype) + return state_dict + + +def load_state_dict_from_bin(file_path, torch_dtype=None): + state_dict = torch.load(file_path, map_location="cpu") + if torch_dtype is not None: + for i in state_dict: + if isinstance(state_dict[i], torch.Tensor): + state_dict[i] = state_dict[i].to(torch_dtype) + return state_dict + + +def search_parameter(param, state_dict): + for name, param_ in state_dict.items(): + if param.numel() == param_.numel(): + if param.shape == param_.shape: + if torch.dist(param, param_) < 1e-6: + return name + else: + if torch.dist(param.flatten(), param_.flatten()) < 1e-6: + return name + return None + + +def build_rename_dict(source_state_dict, target_state_dict, split_qkv=False): + matched_keys = set() + with torch.no_grad(): + for name in source_state_dict: + rename = search_parameter(source_state_dict[name], target_state_dict) + if rename is not None: + print(f'"{name}": "{rename}",') + matched_keys.add(rename) + elif split_qkv and len(source_state_dict[name].shape)>=1 and source_state_dict[name].shape[0]%3==0: + length = source_state_dict[name].shape[0] // 3 + rename = [] + for i in range(3): + rename.append(search_parameter(source_state_dict[name][i*length: i*length+length], target_state_dict)) + if None not in rename: + print(f'"{name}": {rename},') + for rename_ in rename: + matched_keys.add(rename_) + for name in target_state_dict: + if name not in matched_keys: + print("Cannot find", name, target_state_dict[name].shape) diff --git a/diffsynth/models/attention.py b/diffsynth/models/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..eb90e1ed1a28a0541a8d9df8313997a7d3f14da7 --- /dev/null +++ b/diffsynth/models/attention.py @@ -0,0 +1,89 @@ +import torch +from einops import rearrange + + +def low_version_attention(query, key, value, attn_bias=None): + scale = 1 / query.shape[-1] ** 0.5 + query = query * scale + attn = torch.matmul(query, key.transpose(-2, -1)) + if attn_bias is not None: + attn = attn + attn_bias + attn = attn.softmax(-1) + return attn @ value + + +class Attention(torch.nn.Module): + + def __init__(self, q_dim, num_heads, head_dim, kv_dim=None, bias_q=False, bias_kv=False, bias_out=False): + super().__init__() + dim_inner = head_dim * num_heads + kv_dim = kv_dim if kv_dim is not None else q_dim + self.num_heads = num_heads + self.head_dim = head_dim + + self.to_q = torch.nn.Linear(q_dim, dim_inner, bias=bias_q) + self.to_k = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv) + self.to_v = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv) + self.to_out = torch.nn.Linear(dim_inner, q_dim, bias=bias_out) + + def interact_with_ipadapter(self, hidden_states, q, ip_k, ip_v, scale=1.0): + batch_size = q.shape[0] + ip_k = ip_k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + ip_v = ip_v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + ip_hidden_states = torch.nn.functional.scaled_dot_product_attention(q, ip_k, ip_v) + hidden_states = hidden_states + scale * ip_hidden_states + return hidden_states + + def torch_forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None, ipadapter_kwargs=None, qkv_preprocessor=None): + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + + batch_size = encoder_hidden_states.shape[0] + + q = self.to_q(hidden_states) + k = self.to_k(encoder_hidden_states) + v = self.to_v(encoder_hidden_states) + + q = q.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + k = k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + v = v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + + if qkv_preprocessor is not None: + q, k, v = qkv_preprocessor(q, k, v) + + hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) + if ipadapter_kwargs is not None: + hidden_states = self.interact_with_ipadapter(hidden_states, q, **ipadapter_kwargs) + hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) + hidden_states = hidden_states.to(q.dtype) + + hidden_states = self.to_out(hidden_states) + + return hidden_states + + def xformers_forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None): + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + + q = self.to_q(hidden_states) + k = self.to_k(encoder_hidden_states) + v = self.to_v(encoder_hidden_states) + + q = rearrange(q, "b f (n d) -> (b n) f d", n=self.num_heads) + k = rearrange(k, "b f (n d) -> (b n) f d", n=self.num_heads) + v = rearrange(v, "b f (n d) -> (b n) f d", n=self.num_heads) + + if attn_mask is not None: + hidden_states = low_version_attention(q, k, v, attn_bias=attn_mask) + else: + import xformers.ops as xops + hidden_states = xops.memory_efficient_attention(q, k, v) + hidden_states = rearrange(hidden_states, "(b n) f d -> b f (n d)", n=self.num_heads) + + hidden_states = hidden_states.to(q.dtype) + hidden_states = self.to_out(hidden_states) + + return hidden_states + + def forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None, ipadapter_kwargs=None, qkv_preprocessor=None): + return self.torch_forward(hidden_states, encoder_hidden_states=encoder_hidden_states, attn_mask=attn_mask, ipadapter_kwargs=ipadapter_kwargs, qkv_preprocessor=qkv_preprocessor) \ No newline at end of file diff --git a/diffsynth/models/downloader.py b/diffsynth/models/downloader.py new file mode 100644 index 0000000000000000000000000000000000000000..33d3da7fa117497e833e8d5879c4b4bc8f3d6ec4 --- /dev/null +++ b/diffsynth/models/downloader.py @@ -0,0 +1,28 @@ +from huggingface_hub import hf_hub_download +from modelscope import snapshot_download +import os, shutil + + +def download_from_modelscope(model_id, origin_file_path, local_dir): + os.makedirs(local_dir, exist_ok=True) + if os.path.basename(origin_file_path) in os.listdir(local_dir): + print(f"{os.path.basename(origin_file_path)} has been already in {local_dir}.") + return + else: + print(f"Start downloading {os.path.join(local_dir, os.path.basename(origin_file_path))}") + snapshot_download(model_id, allow_file_pattern=origin_file_path, local_dir=local_dir) + downloaded_file_path = os.path.join(local_dir, origin_file_path) + target_file_path = os.path.join(local_dir, os.path.split(origin_file_path)[-1]) + if downloaded_file_path != target_file_path: + shutil.move(downloaded_file_path, target_file_path) + shutil.rmtree(os.path.join(local_dir, origin_file_path.split("/")[0])) + + +def download_from_huggingface(model_id, origin_file_path, local_dir): + os.makedirs(local_dir, exist_ok=True) + if os.path.basename(origin_file_path) in os.listdir(local_dir): + print(f"{os.path.basename(origin_file_path)} has been already in {local_dir}.") + return + else: + print(f"Start downloading {os.path.join(local_dir, os.path.basename(origin_file_path))}") + hf_hub_download(model_id, origin_file_path, local_dir=local_dir) diff --git a/diffsynth/models/hunyuan_dit.py b/diffsynth/models/hunyuan_dit.py new file mode 100644 index 0000000000000000000000000000000000000000..ca797e823cbe69ec67fd7a3d5519e850e4039a8c --- /dev/null +++ b/diffsynth/models/hunyuan_dit.py @@ -0,0 +1,451 @@ +from .attention import Attention +from .tiler import TileWorker +from einops import repeat, rearrange +import math +import torch + + +class HunyuanDiTRotaryEmbedding(torch.nn.Module): + + def __init__(self, q_norm_shape=88, k_norm_shape=88, rotary_emb_on_k=True): + super().__init__() + self.q_norm = torch.nn.LayerNorm((q_norm_shape,), elementwise_affine=True, eps=1e-06) + self.k_norm = torch.nn.LayerNorm((k_norm_shape,), elementwise_affine=True, eps=1e-06) + self.rotary_emb_on_k = rotary_emb_on_k + self.k_cache, self.v_cache = [], [] + + def reshape_for_broadcast(self, freqs_cis, x): + ndim = x.ndim + shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape) + + def rotate_half(self, x): + x_real, x_imag = x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1) + return torch.stack([-x_imag, x_real], dim=-1).flatten(3) + + def apply_rotary_emb(self, xq, xk, freqs_cis): + xk_out = None + cos, sin = self.reshape_for_broadcast(freqs_cis, xq) + cos, sin = cos.to(xq.device), sin.to(xq.device) + xq_out = (xq.float() * cos + self.rotate_half(xq.float()) * sin).type_as(xq) + if xk is not None: + xk_out = (xk.float() * cos + self.rotate_half(xk.float()) * sin).type_as(xk) + return xq_out, xk_out + + def forward(self, q, k, v, freqs_cis_img, to_cache=False): + # norm + q = self.q_norm(q) + k = self.k_norm(k) + + # RoPE + if self.rotary_emb_on_k: + q, k = self.apply_rotary_emb(q, k, freqs_cis_img) + else: + q, _ = self.apply_rotary_emb(q, None, freqs_cis_img) + + if to_cache: + self.k_cache.append(k) + self.v_cache.append(v) + elif len(self.k_cache) > 0 and len(self.v_cache) > 0: + k = torch.concat([k] + self.k_cache, dim=2) + v = torch.concat([v] + self.v_cache, dim=2) + self.k_cache, self.v_cache = [], [] + return q, k, v + + +class FP32_Layernorm(torch.nn.LayerNorm): + def forward(self, inputs): + origin_dtype = inputs.dtype + return torch.nn.functional.layer_norm(inputs.float(), self.normalized_shape, self.weight.float(), self.bias.float(), self.eps).to(origin_dtype) + + +class FP32_SiLU(torch.nn.SiLU): + def forward(self, inputs): + origin_dtype = inputs.dtype + return torch.nn.functional.silu(inputs.float(), inplace=False).to(origin_dtype) + + +class HunyuanDiTFinalLayer(torch.nn.Module): + def __init__(self, final_hidden_size=1408, condition_dim=1408, patch_size=2, out_channels=8): + super().__init__() + self.norm_final = torch.nn.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = torch.nn.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True) + self.adaLN_modulation = torch.nn.Sequential( + FP32_SiLU(), + torch.nn.Linear(condition_dim, 2 * final_hidden_size, bias=True) + ) + + def modulate(self, x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + def forward(self, hidden_states, condition_emb): + shift, scale = self.adaLN_modulation(condition_emb).chunk(2, dim=1) + hidden_states = self.modulate(self.norm_final(hidden_states), shift, scale) + hidden_states = self.linear(hidden_states) + return hidden_states + + +class HunyuanDiTBlock(torch.nn.Module): + + def __init__( + self, + hidden_dim=1408, + condition_dim=1408, + num_heads=16, + mlp_ratio=4.3637, + text_dim=1024, + skip_connection=False + ): + super().__init__() + self.norm1 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True) + self.rota1 = HunyuanDiTRotaryEmbedding(hidden_dim//num_heads, hidden_dim//num_heads) + self.attn1 = Attention(hidden_dim, num_heads, hidden_dim//num_heads, bias_q=True, bias_kv=True, bias_out=True) + self.norm2 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True) + self.rota2 = HunyuanDiTRotaryEmbedding(hidden_dim//num_heads, hidden_dim//num_heads, rotary_emb_on_k=False) + self.attn2 = Attention(hidden_dim, num_heads, hidden_dim//num_heads, kv_dim=text_dim, bias_q=True, bias_kv=True, bias_out=True) + self.norm3 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True) + self.modulation = torch.nn.Sequential(FP32_SiLU(), torch.nn.Linear(condition_dim, hidden_dim, bias=True)) + self.mlp = torch.nn.Sequential( + torch.nn.Linear(hidden_dim, int(hidden_dim*mlp_ratio), bias=True), + torch.nn.GELU(approximate="tanh"), + torch.nn.Linear(int(hidden_dim*mlp_ratio), hidden_dim, bias=True) + ) + if skip_connection: + self.skip_norm = FP32_Layernorm((hidden_dim * 2,), eps=1e-6, elementwise_affine=True) + self.skip_linear = torch.nn.Linear(hidden_dim * 2, hidden_dim, bias=True) + else: + self.skip_norm, self.skip_linear = None, None + + def forward(self, hidden_states, condition_emb, text_emb, freq_cis_img, residual=None, to_cache=False): + # Long Skip Connection + if self.skip_norm is not None and self.skip_linear is not None: + hidden_states = torch.cat([hidden_states, residual], dim=-1) + hidden_states = self.skip_norm(hidden_states) + hidden_states = self.skip_linear(hidden_states) + + # Self-Attention + shift_msa = self.modulation(condition_emb).unsqueeze(dim=1) + attn_input = self.norm1(hidden_states) + shift_msa + hidden_states = hidden_states + self.attn1(attn_input, qkv_preprocessor=lambda q, k, v: self.rota1(q, k, v, freq_cis_img, to_cache=to_cache)) + + # Cross-Attention + attn_input = self.norm3(hidden_states) + hidden_states = hidden_states + self.attn2(attn_input, text_emb, qkv_preprocessor=lambda q, k, v: self.rota2(q, k, v, freq_cis_img)) + + # FFN Layer + mlp_input = self.norm2(hidden_states) + hidden_states = hidden_states + self.mlp(mlp_input) + return hidden_states + + +class AttentionPool(torch.nn.Module): + def __init__(self, spacial_dim, embed_dim, num_heads, output_dim = None): + super().__init__() + self.positional_embedding = torch.nn.Parameter(torch.randn(spacial_dim + 1, embed_dim) / embed_dim ** 0.5) + self.k_proj = torch.nn.Linear(embed_dim, embed_dim) + self.q_proj = torch.nn.Linear(embed_dim, embed_dim) + self.v_proj = torch.nn.Linear(embed_dim, embed_dim) + self.c_proj = torch.nn.Linear(embed_dim, output_dim or embed_dim) + self.num_heads = num_heads + + def forward(self, x): + x = x.permute(1, 0, 2) # NLC -> LNC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (L+1)NC + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (L+1)NC + x, _ = torch.nn.functional.multi_head_attention_forward( + query=x[:1], key=x, value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0, + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False + ) + return x.squeeze(0) + + +class PatchEmbed(torch.nn.Module): + def __init__( + self, + patch_size=(2, 2), + in_chans=4, + embed_dim=1408, + bias=True, + ): + super().__init__() + self.proj = torch.nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias) + + def forward(self, x): + x = self.proj(x) + x = x.flatten(2).transpose(1, 2) # BCHW -> BNC + return x + + +def timestep_embedding(t, dim, max_period=10000, repeat_only=False): + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=t.device) # size: [dim/2], 一个指数衰减的曲线 + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + else: + embedding = repeat(t, "b -> b d", d=dim) + return embedding + + +class TimestepEmbedder(torch.nn.Module): + def __init__(self, hidden_size=1408, frequency_embedding_size=256): + super().__init__() + self.mlp = torch.nn.Sequential( + torch.nn.Linear(frequency_embedding_size, hidden_size, bias=True), + torch.nn.SiLU(), + torch.nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + def forward(self, t): + t_freq = timestep_embedding(t, self.frequency_embedding_size).type(self.mlp[0].weight.dtype) + t_emb = self.mlp(t_freq) + return t_emb + + +class HunyuanDiT(torch.nn.Module): + def __init__(self, num_layers_down=21, num_layers_up=19, in_channels=4, out_channels=8, hidden_dim=1408, text_dim=1024, t5_dim=2048, text_length=77, t5_length=256): + super().__init__() + + # Embedders + self.text_emb_padding = torch.nn.Parameter(torch.randn(text_length + t5_length, text_dim, dtype=torch.float32)) + self.t5_embedder = torch.nn.Sequential( + torch.nn.Linear(t5_dim, t5_dim * 4, bias=True), + FP32_SiLU(), + torch.nn.Linear(t5_dim * 4, text_dim, bias=True), + ) + self.t5_pooler = AttentionPool(t5_length, t5_dim, num_heads=8, output_dim=1024) + self.style_embedder = torch.nn.Parameter(torch.randn(hidden_dim)) + self.patch_embedder = PatchEmbed(in_chans=in_channels) + self.timestep_embedder = TimestepEmbedder() + self.extra_embedder = torch.nn.Sequential( + torch.nn.Linear(256 * 6 + 1024 + hidden_dim, hidden_dim * 4), + FP32_SiLU(), + torch.nn.Linear(hidden_dim * 4, hidden_dim), + ) + + # Transformer blocks + self.num_layers_down = num_layers_down + self.num_layers_up = num_layers_up + self.blocks = torch.nn.ModuleList( + [HunyuanDiTBlock(skip_connection=False) for _ in range(num_layers_down)] + \ + [HunyuanDiTBlock(skip_connection=True) for _ in range(num_layers_up)] + ) + + # Output layers + self.final_layer = HunyuanDiTFinalLayer() + self.out_channels = out_channels + + def prepare_text_emb(self, text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5): + text_emb_mask = text_emb_mask.bool() + text_emb_mask_t5 = text_emb_mask_t5.bool() + text_emb_t5 = self.t5_embedder(text_emb_t5) + text_emb = torch.cat([text_emb, text_emb_t5], dim=1) + text_emb_mask = torch.cat([text_emb_mask, text_emb_mask_t5], dim=-1) + text_emb = torch.where(text_emb_mask.unsqueeze(2), text_emb, self.text_emb_padding.to(text_emb)) + return text_emb + + def prepare_extra_emb(self, text_emb_t5, timestep, size_emb, dtype, batch_size): + # Text embedding + pooled_text_emb_t5 = self.t5_pooler(text_emb_t5) + + # Timestep embedding + timestep_emb = self.timestep_embedder(timestep) + + # Size embedding + size_emb = timestep_embedding(size_emb.view(-1), 256).to(dtype) + size_emb = size_emb.view(-1, 6 * 256) + + # Style embedding + style_emb = repeat(self.style_embedder, "D -> B D", B=batch_size) + + # Concatenate all extra vectors + extra_emb = torch.cat([pooled_text_emb_t5, size_emb, style_emb], dim=1) + condition_emb = timestep_emb + self.extra_embedder(extra_emb) + + return condition_emb + + def unpatchify(self, x, h, w): + return rearrange(x, "B (H W) (P Q C) -> B C (H P) (W Q)", H=h, W=w, P=2, Q=2) + + def build_mask(self, data, is_bound): + _, _, H, W = data.shape + h = repeat(torch.arange(H), "H -> H W", H=H, W=W) + w = repeat(torch.arange(W), "W -> H W", H=H, W=W) + border_width = (H + W) // 4 + pad = torch.ones_like(h) * border_width + mask = torch.stack([ + pad if is_bound[0] else h + 1, + pad if is_bound[1] else H - h, + pad if is_bound[2] else w + 1, + pad if is_bound[3] else W - w + ]).min(dim=0).values + mask = mask.clip(1, border_width) + mask = (mask / border_width).to(dtype=data.dtype, device=data.device) + mask = rearrange(mask, "H W -> 1 H W") + return mask + + def tiled_block_forward(self, block, hidden_states, condition_emb, text_emb, freq_cis_img, residual, torch_dtype, data_device, computation_device, tile_size, tile_stride): + B, C, H, W = hidden_states.shape + + weight = torch.zeros((1, 1, H, W), dtype=torch_dtype, device=data_device) + values = torch.zeros((B, C, H, W), dtype=torch_dtype, device=data_device) + + # Split tasks + tasks = [] + for h in range(0, H, tile_stride): + for w in range(0, W, tile_stride): + if (h-tile_stride >= 0 and h-tile_stride+tile_size >= H) or (w-tile_stride >= 0 and w-tile_stride+tile_size >= W): + continue + h_, w_ = h + tile_size, w + tile_size + if h_ > H: h, h_ = H - tile_size, H + if w_ > W: w, w_ = W - tile_size, W + tasks.append((h, h_, w, w_)) + + # Run + for hl, hr, wl, wr in tasks: + hidden_states_batch = hidden_states[:, :, hl:hr, wl:wr].to(computation_device) + hidden_states_batch = rearrange(hidden_states_batch, "B C H W -> B (H W) C") + if residual is not None: + residual_batch = residual[:, :, hl:hr, wl:wr].to(computation_device) + residual_batch = rearrange(residual_batch, "B C H W -> B (H W) C") + else: + residual_batch = None + + # Forward + hidden_states_batch = block(hidden_states_batch, condition_emb, text_emb, freq_cis_img, residual_batch).to(data_device) + hidden_states_batch = rearrange(hidden_states_batch, "B (H W) C -> B C H W", H=hr-hl) + + mask = self.build_mask(hidden_states_batch, is_bound=(hl==0, hr>=H, wl==0, wr>=W)) + values[:, :, hl:hr, wl:wr] += hidden_states_batch * mask + weight[:, :, hl:hr, wl:wr] += mask + values /= weight + return values + + def forward( + self, hidden_states, text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5, timestep, size_emb, freq_cis_img, + tiled=False, tile_size=64, tile_stride=32, + to_cache=False, + use_gradient_checkpointing=False, + ): + # Embeddings + text_emb = self.prepare_text_emb(text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5) + condition_emb = self.prepare_extra_emb(text_emb_t5, timestep, size_emb, hidden_states.dtype, hidden_states.shape[0]) + + # Input + height, width = hidden_states.shape[-2], hidden_states.shape[-1] + hidden_states = self.patch_embedder(hidden_states) + + # Blocks + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + if tiled: + hidden_states = rearrange(hidden_states, "B (H W) C -> B C H W", H=height//2) + residuals = [] + for block_id, block in enumerate(self.blocks): + residual = residuals.pop() if block_id >= self.num_layers_down else None + hidden_states = self.tiled_block_forward( + block, hidden_states, condition_emb, text_emb, freq_cis_img, residual, + torch_dtype=hidden_states.dtype, data_device=hidden_states.device, computation_device=hidden_states.device, + tile_size=tile_size, tile_stride=tile_stride + ) + if block_id < self.num_layers_down - 2: + residuals.append(hidden_states) + hidden_states = rearrange(hidden_states, "B C H W -> B (H W) C") + else: + residuals = [] + for block_id, block in enumerate(self.blocks): + residual = residuals.pop() if block_id >= self.num_layers_down else None + if self.training and use_gradient_checkpointing: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, condition_emb, text_emb, freq_cis_img, residual, + use_reentrant=False, + ) + else: + hidden_states = block(hidden_states, condition_emb, text_emb, freq_cis_img, residual, to_cache=to_cache) + if block_id < self.num_layers_down - 2: + residuals.append(hidden_states) + + # Output + hidden_states = self.final_layer(hidden_states, condition_emb) + hidden_states = self.unpatchify(hidden_states, height//2, width//2) + hidden_states, _ = hidden_states.chunk(2, dim=1) + return hidden_states + + def state_dict_converter(self): + return HunyuanDiTStateDictConverter() + + + +class HunyuanDiTStateDictConverter(): + def __init__(self): + pass + + def from_diffusers(self, state_dict): + state_dict_ = {} + for name, param in state_dict.items(): + name_ = name + name_ = name_.replace(".default_modulation.", ".modulation.") + name_ = name_.replace(".mlp.fc1.", ".mlp.0.") + name_ = name_.replace(".mlp.fc2.", ".mlp.2.") + name_ = name_.replace(".attn1.q_norm.", ".rota1.q_norm.") + name_ = name_.replace(".attn2.q_norm.", ".rota2.q_norm.") + name_ = name_.replace(".attn1.k_norm.", ".rota1.k_norm.") + name_ = name_.replace(".attn2.k_norm.", ".rota2.k_norm.") + name_ = name_.replace(".q_proj.", ".to_q.") + name_ = name_.replace(".out_proj.", ".to_out.") + name_ = name_.replace("text_embedding_padding", "text_emb_padding") + name_ = name_.replace("mlp_t5.0.", "t5_embedder.0.") + name_ = name_.replace("mlp_t5.2.", "t5_embedder.2.") + name_ = name_.replace("pooler.", "t5_pooler.") + name_ = name_.replace("x_embedder.", "patch_embedder.") + name_ = name_.replace("t_embedder.", "timestep_embedder.") + name_ = name_.replace("t5_pooler.to_q.", "t5_pooler.q_proj.") + name_ = name_.replace("style_embedder.weight", "style_embedder") + if ".kv_proj." in name_: + param_k = param[:param.shape[0]//2] + param_v = param[param.shape[0]//2:] + state_dict_[name_.replace(".kv_proj.", ".to_k.")] = param_k + state_dict_[name_.replace(".kv_proj.", ".to_v.")] = param_v + elif ".Wqkv." in name_: + param_q = param[:param.shape[0]//3] + param_k = param[param.shape[0]//3:param.shape[0]//3*2] + param_v = param[param.shape[0]//3*2:] + state_dict_[name_.replace(".Wqkv.", ".to_q.")] = param_q + state_dict_[name_.replace(".Wqkv.", ".to_k.")] = param_k + state_dict_[name_.replace(".Wqkv.", ".to_v.")] = param_v + elif "style_embedder" in name_: + state_dict_[name_] = param.squeeze() + else: + state_dict_[name_] = param + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) diff --git a/diffsynth/models/hunyuan_dit_text_encoder.py b/diffsynth/models/hunyuan_dit_text_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..efa5d39148590352b2d4919f6bb31f070e40a8f5 --- /dev/null +++ b/diffsynth/models/hunyuan_dit_text_encoder.py @@ -0,0 +1,161 @@ +from transformers import BertModel, BertConfig, T5EncoderModel, T5Config +import torch + + + +class HunyuanDiTCLIPTextEncoder(BertModel): + def __init__(self): + config = BertConfig( + _name_or_path = "", + architectures = ["BertModel"], + attention_probs_dropout_prob = 0.1, + bos_token_id = 0, + classifier_dropout = None, + directionality = "bidi", + eos_token_id = 2, + hidden_act = "gelu", + hidden_dropout_prob = 0.1, + hidden_size = 1024, + initializer_range = 0.02, + intermediate_size = 4096, + layer_norm_eps = 1e-12, + max_position_embeddings = 512, + model_type = "bert", + num_attention_heads = 16, + num_hidden_layers = 24, + output_past = True, + pad_token_id = 0, + pooler_fc_size = 768, + pooler_num_attention_heads = 12, + pooler_num_fc_layers = 3, + pooler_size_per_head = 128, + pooler_type = "first_token_transform", + position_embedding_type = "absolute", + torch_dtype = "float32", + transformers_version = "4.37.2", + type_vocab_size = 2, + use_cache = True, + vocab_size = 47020 + ) + super().__init__(config, add_pooling_layer=False) + self.eval() + + def forward(self, input_ids, attention_mask, clip_skip=1): + input_shape = input_ids.size() + + batch_size, seq_length = input_shape + device = input_ids.device + + past_key_values_length = 0 + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=None, + token_type_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=False, + output_attentions=False, + output_hidden_states=True, + return_dict=True, + ) + all_hidden_states = encoder_outputs.hidden_states + prompt_emb = all_hidden_states[-clip_skip] + if clip_skip > 1: + mean, std = all_hidden_states[-1].mean(), all_hidden_states[-1].std() + prompt_emb = (prompt_emb - prompt_emb.mean()) / prompt_emb.std() * std + mean + return prompt_emb + + def state_dict_converter(self): + return HunyuanDiTCLIPTextEncoderStateDictConverter() + + + +class HunyuanDiTT5TextEncoder(T5EncoderModel): + def __init__(self): + config = T5Config( + _name_or_path = "../HunyuanDiT/t2i/mt5", + architectures = ["MT5ForConditionalGeneration"], + classifier_dropout = 0.0, + d_ff = 5120, + d_kv = 64, + d_model = 2048, + decoder_start_token_id = 0, + dense_act_fn = "gelu_new", + dropout_rate = 0.1, + eos_token_id = 1, + feed_forward_proj = "gated-gelu", + initializer_factor = 1.0, + is_encoder_decoder = True, + is_gated_act = True, + layer_norm_epsilon = 1e-06, + model_type = "t5", + num_decoder_layers = 24, + num_heads = 32, + num_layers = 24, + output_past = True, + pad_token_id = 0, + relative_attention_max_distance = 128, + relative_attention_num_buckets = 32, + tie_word_embeddings = False, + tokenizer_class = "T5Tokenizer", + transformers_version = "4.37.2", + use_cache = True, + vocab_size = 250112 + ) + super().__init__(config) + self.eval() + + def forward(self, input_ids, attention_mask, clip_skip=1): + outputs = super().forward( + input_ids=input_ids, + attention_mask=attention_mask, + output_hidden_states=True, + ) + prompt_emb = outputs.hidden_states[-clip_skip] + if clip_skip > 1: + mean, std = outputs.hidden_states[-1].mean(), outputs.hidden_states[-1].std() + prompt_emb = (prompt_emb - prompt_emb.mean()) / prompt_emb.std() * std + mean + return prompt_emb + + def state_dict_converter(self): + return HunyuanDiTT5TextEncoderStateDictConverter() + + + +class HunyuanDiTCLIPTextEncoderStateDictConverter(): + def __init__(self): + pass + + def from_diffusers(self, state_dict): + state_dict_ = {name[5:]: param for name, param in state_dict.items() if name.startswith("bert.")} + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) + + +class HunyuanDiTT5TextEncoderStateDictConverter(): + def __init__(self): + pass + + def from_diffusers(self, state_dict): + state_dict_ = {name: param for name, param in state_dict.items() if name.startswith("encoder.")} + state_dict_["shared.weight"] = state_dict["shared.weight"] + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) diff --git a/diffsynth/models/kolors_text_encoder.py b/diffsynth/models/kolors_text_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..3dd3213a0f504be7765e7f85eadff964af93799f --- /dev/null +++ b/diffsynth/models/kolors_text_encoder.py @@ -0,0 +1,1363 @@ +""" +This model is copied from https://github.com/Kwai-Kolors/Kolors/tree/master/kolors/models. +We didn't modify this model. +The tensor operation is performed in the prompter. +""" + + +""" PyTorch ChatGLM model. """ + +import math +import copy +import warnings +import re +import sys + +import torch +import torch.utils.checkpoint +import torch.nn.functional as F +from torch import nn +from torch.nn import CrossEntropyLoss, LayerNorm +from torch.nn import CrossEntropyLoss, LayerNorm, MSELoss, BCEWithLogitsLoss +from torch.nn.utils import skip_init +from typing import Optional, Tuple, Union, List, Callable, Dict, Any +from copy import deepcopy + +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + SequenceClassifierOutputWithPast, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.generation.logits_process import LogitsProcessor +from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput +from transformers import PretrainedConfig + + + +class ChatGLMConfig(PretrainedConfig): + model_type = "chatglm" + def __init__( + self, + num_layers=28, + padded_vocab_size=65024, + hidden_size=4096, + ffn_hidden_size=13696, + kv_channels=128, + num_attention_heads=32, + seq_length=2048, + hidden_dropout=0.0, + classifier_dropout=None, + attention_dropout=0.0, + layernorm_epsilon=1e-5, + rmsnorm=True, + apply_residual_connection_post_layernorm=False, + post_layer_norm=True, + add_bias_linear=False, + add_qkv_bias=False, + bias_dropout_fusion=True, + multi_query_attention=False, + multi_query_group_num=1, + apply_query_key_layer_scaling=True, + attention_softmax_in_fp32=True, + fp32_residual_connection=False, + quantization_bit=0, + pre_seq_len=None, + prefix_projection=False, + **kwargs + ): + self.num_layers = num_layers + self.vocab_size = padded_vocab_size + self.padded_vocab_size = padded_vocab_size + self.hidden_size = hidden_size + self.ffn_hidden_size = ffn_hidden_size + self.kv_channels = kv_channels + self.num_attention_heads = num_attention_heads + self.seq_length = seq_length + self.hidden_dropout = hidden_dropout + self.classifier_dropout = classifier_dropout + self.attention_dropout = attention_dropout + self.layernorm_epsilon = layernorm_epsilon + self.rmsnorm = rmsnorm + self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm + self.post_layer_norm = post_layer_norm + self.add_bias_linear = add_bias_linear + self.add_qkv_bias = add_qkv_bias + self.bias_dropout_fusion = bias_dropout_fusion + self.multi_query_attention = multi_query_attention + self.multi_query_group_num = multi_query_group_num + self.apply_query_key_layer_scaling = apply_query_key_layer_scaling + self.attention_softmax_in_fp32 = attention_softmax_in_fp32 + self.fp32_residual_connection = fp32_residual_connection + self.quantization_bit = quantization_bit + self.pre_seq_len = pre_seq_len + self.prefix_projection = prefix_projection + super().__init__(**kwargs) + + + +# flags required to enable jit fusion kernels + +if sys.platform != 'darwin': + torch._C._jit_set_profiling_mode(False) + torch._C._jit_set_profiling_executor(False) + torch._C._jit_override_can_fuse_on_cpu(True) + torch._C._jit_override_can_fuse_on_gpu(True) + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM" +_CONFIG_FOR_DOC = "ChatGLM6BConfig" + +CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "THUDM/chatglm3-6b-base", + # See all ChatGLM models at https://huggingface.co/models?filter=chatglm +] + + +def default_init(cls, *args, **kwargs): + return cls(*args, **kwargs) + + +class InvalidScoreLogitsProcessor(LogitsProcessor): + def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: + if torch.isnan(scores).any() or torch.isinf(scores).any(): + scores.zero_() + scores[..., 5] = 5e4 + return scores + + +class PrefixEncoder(torch.nn.Module): + """ + The torch.nn model to encode the prefix + Input shape: (batch-size, prefix-length) + Output shape: (batch-size, prefix-length, 2*layers*hidden) + """ + + def __init__(self, config: ChatGLMConfig): + super().__init__() + self.prefix_projection = config.prefix_projection + if self.prefix_projection: + # Use a two-layer MLP to encode the prefix + kv_size = config.num_layers * config.kv_channels * config.multi_query_group_num * 2 + self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size) + self.trans = torch.nn.Sequential( + torch.nn.Linear(kv_size, config.hidden_size), + torch.nn.Tanh(), + torch.nn.Linear(config.hidden_size, kv_size) + ) + else: + self.embedding = torch.nn.Embedding(config.pre_seq_len, + config.num_layers * config.kv_channels * config.multi_query_group_num * 2) + + def forward(self, prefix: torch.Tensor): + if self.prefix_projection: + prefix_tokens = self.embedding(prefix) + past_key_values = self.trans(prefix_tokens) + else: + past_key_values = self.embedding(prefix) + return past_key_values + + +def split_tensor_along_last_dim( + tensor: torch.Tensor, + num_partitions: int, + contiguous_split_chunks: bool = False, +) -> List[torch.Tensor]: + """Split a tensor along its last dimension. + + Arguments: + tensor: input tensor. + num_partitions: number of partitions to split the tensor + contiguous_split_chunks: If True, make each chunk contiguous + in memory. + + Returns: + A list of Tensors + """ + # Get the size and dimension. + last_dim = tensor.dim() - 1 + last_dim_size = tensor.size()[last_dim] // num_partitions + # Split. + tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) + # Note: torch.split does not create contiguous tensors by default. + if contiguous_split_chunks: + return tuple(chunk.contiguous() for chunk in tensor_list) + + return tensor_list + + +class RotaryEmbedding(nn.Module): + def __init__(self, dim, original_impl=False, device=None, dtype=None): + super().__init__() + inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim)) + self.register_buffer("inv_freq", inv_freq) + self.dim = dim + self.original_impl = original_impl + + def forward_impl( + self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000 + ): + """Enhanced Transformer with Rotary Position Embedding. + + Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/ + transformers/rope/__init__.py. MIT License: + https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license. + """ + # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ + theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=torch.float, device=device) / n_elem)) + + # Create position indexes `[0, 1, ..., seq_len - 1]` + seq_idx = torch.arange(seq_len, dtype=torch.float, device=device) + + # Calculate the product of position index and $\theta_i$ + idx_theta = torch.outer(seq_idx, theta).float() + + cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1) + + # this is to mimic the behaviour of complex32, else we will get different results + if dtype in (torch.float16, torch.bfloat16, torch.int8): + cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half() + return cache + + def forward(self, max_seq_len, offset=0): + return self.forward_impl( + max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device + ) + + +@torch.jit.script +def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor: + # x: [sq, b, np, hn] + sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3) + rot_dim = rope_cache.shape[-2] * 2 + x, x_pass = x[..., :rot_dim], x[..., rot_dim:] + # truncate to support variable sizes + rope_cache = rope_cache[:sq] + xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2) + rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2) + x_out2 = torch.stack( + [ + xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1], + xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1], + ], + -1, + ) + x_out2 = x_out2.flatten(3) + return torch.cat((x_out2, x_pass), dim=-1) + + +class RMSNorm(torch.nn.Module): + def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs): + super().__init__() + self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype)) + self.eps = eps + + def forward(self, hidden_states: torch.Tensor): + input_dtype = hidden_states.dtype + variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.eps) + + return (self.weight * hidden_states).to(input_dtype) + + +class CoreAttention(torch.nn.Module): + def __init__(self, config: ChatGLMConfig, layer_number): + super(CoreAttention, self).__init__() + + self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling + self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32 + if self.apply_query_key_layer_scaling: + self.attention_softmax_in_fp32 = True + self.layer_number = max(1, layer_number) + + projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + self.hidden_size_per_partition = projection_size + self.hidden_size_per_attention_head = projection_size // config.num_attention_heads + self.num_attention_heads_per_partition = config.num_attention_heads + + coeff = None + self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) + if self.apply_query_key_layer_scaling: + coeff = self.layer_number + self.norm_factor *= coeff + self.coeff = coeff + + self.attention_dropout = torch.nn.Dropout(config.attention_dropout) + + def forward(self, query_layer, key_layer, value_layer, attention_mask): + pytorch_major_version = int(torch.__version__.split('.')[0]) + if pytorch_major_version >= 2: + query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]] + if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]: + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, + is_causal=True) + else: + if attention_mask is not None: + attention_mask = ~attention_mask + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, + attention_mask) + context_layer = context_layer.permute(2, 0, 1, 3) + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.reshape(*new_context_layer_shape) + else: + # Raw attention scores + + # [b, np, sq, sk] + output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0)) + + # [sq, b, np, hn] -> [sq, b * np, hn] + query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1) + # [sk, b, np, hn] -> [sk, b * np, hn] + key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1) + + # preallocting input tensor: [b * np, sq, sk] + matmul_input_buffer = torch.empty( + output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype, + device=query_layer.device + ) + + # Raw attention scores. [b * np, sq, sk] + matmul_result = torch.baddbmm( + matmul_input_buffer, + query_layer.transpose(0, 1), # [b * np, sq, hn] + key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk] + beta=0.0, + alpha=(1.0 / self.norm_factor), + ) + + # change view to [b, np, sq, sk] + attention_scores = matmul_result.view(*output_size) + + # =========================== + # Attention probs and dropout + # =========================== + + # attention scores and attention mask [b, np, sq, sk] + if self.attention_softmax_in_fp32: + attention_scores = attention_scores.float() + if self.coeff is not None: + attention_scores = attention_scores * self.coeff + if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]: + attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3], + device=attention_scores.device, dtype=torch.bool) + attention_mask.tril_() + attention_mask = ~attention_mask + if attention_mask is not None: + attention_scores = attention_scores.masked_fill(attention_mask, float("-inf")) + attention_probs = F.softmax(attention_scores, dim=-1) + attention_probs = attention_probs.type_as(value_layer) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.attention_dropout(attention_probs) + # ========================= + # Context layer. [sq, b, hp] + # ========================= + + # value_layer -> context layer. + # [sk, b, np, hn] --> [b, np, sq, hn] + + # context layer shape: [b, np, sq, hn] + output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3)) + # change view [sk, b * np, hn] + value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1) + # change view [b * np, sq, sk] + attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) + # matmul: [b * np, sq, hn] + context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1)) + # change view [b, np, sq, hn] + context_layer = context_layer.view(*output_size) + # [b, np, sq, hn] --> [sq, b, np, hn] + context_layer = context_layer.permute(2, 0, 1, 3).contiguous() + # [sq, b, np, hn] --> [sq, b, hp] + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.view(*new_context_layer_shape) + + return context_layer + + +class SelfAttention(torch.nn.Module): + """Parallel self-attention layer abstract class. + + Self-attention layer takes input with size [s, b, h] + and returns output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(SelfAttention, self).__init__() + self.layer_number = max(1, layer_number) + + self.projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads + self.num_attention_heads_per_partition = config.num_attention_heads + + self.multi_query_attention = config.multi_query_attention + self.qkv_hidden_size = 3 * self.projection_size + if self.multi_query_attention: + self.num_multi_query_groups_per_partition = config.multi_query_group_num + self.qkv_hidden_size = ( + self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num + ) + self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size, + bias=config.add_bias_linear or config.add_qkv_bias, + device=device, **_config_to_kwargs(config) + ) + + self.core_attention = CoreAttention(config, self.layer_number) + + # Output. + self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear, + device=device, **_config_to_kwargs(config) + ) + + def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None): + if self.multi_query_attention: + num_attention_heads = self.num_multi_query_groups_per_partition + else: + num_attention_heads = self.num_attention_heads_per_partition + return torch.empty( + inference_max_sequence_len, + batch_size, + num_attention_heads, + self.hidden_size_per_attention_head, + dtype=dtype, + device=device, + ) + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True + ): + # hidden_states: [sq, b, h] + + # ================================================= + # Pre-allocate memory for key-values for inference. + # ================================================= + # ===================== + # Query, Key, and Value + # ===================== + + # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)] + mixed_x_layer = self.query_key_value(hidden_states) + + if self.multi_query_attention: + (query_layer, key_layer, value_layer) = mixed_x_layer.split( + [ + self.num_attention_heads_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + ], + dim=-1, + ) + query_layer = query_layer.view( + query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + key_layer = key_layer.view( + key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) + ) + value_layer = value_layer.view( + value_layer.size()[:-1] + + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) + ) + else: + new_tensor_shape = mixed_x_layer.size()[:-1] + \ + (self.num_attention_heads_per_partition, + 3 * self.hidden_size_per_attention_head) + mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) + + # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn] + (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3) + + # apply relative positional encoding (rotary embedding) + if rotary_pos_emb is not None: + query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb) + key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb) + + # adjust key and value for inference + if kv_cache is not None: + cache_k, cache_v = kv_cache + key_layer = torch.cat((cache_k, key_layer), dim=0) + value_layer = torch.cat((cache_v, value_layer), dim=0) + if use_cache: + kv_cache = (key_layer, value_layer) + else: + kv_cache = None + + if self.multi_query_attention: + key_layer = key_layer.unsqueeze(-2) + key_layer = key_layer.expand( + -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 + ) + key_layer = key_layer.contiguous().view( + key_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + value_layer = value_layer.unsqueeze(-2) + value_layer = value_layer.expand( + -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 + ) + value_layer = value_layer.contiguous().view( + value_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + + # ================================== + # core attention computation + # ================================== + + context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask) + + # ================= + # Output. [sq, b, h] + # ================= + + output = self.dense(context_layer) + + return output, kv_cache + + +def _config_to_kwargs(args): + common_kwargs = { + "dtype": args.torch_dtype, + } + return common_kwargs + + +class MLP(torch.nn.Module): + """MLP. + + MLP will take the input with h hidden state, project it to 4*h + hidden dimension, perform nonlinear transformation, and project the + state back into h hidden dimension. + """ + + def __init__(self, config: ChatGLMConfig, device=None): + super(MLP, self).__init__() + + self.add_bias = config.add_bias_linear + + # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf + self.dense_h_to_4h = nn.Linear( + config.hidden_size, + config.ffn_hidden_size * 2, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config) + ) + + def swiglu(x): + x = torch.chunk(x, 2, dim=-1) + return F.silu(x[0]) * x[1] + + self.activation_func = swiglu + + # Project back to h. + self.dense_4h_to_h = nn.Linear( + config.ffn_hidden_size, + config.hidden_size, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config) + ) + + def forward(self, hidden_states): + # [s, b, 4hp] + intermediate_parallel = self.dense_h_to_4h(hidden_states) + intermediate_parallel = self.activation_func(intermediate_parallel) + # [s, b, h] + output = self.dense_4h_to_h(intermediate_parallel) + return output + + +class GLMBlock(torch.nn.Module): + """A single transformer layer. + + Transformer layer takes input with size [s, b, h] and returns an + output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(GLMBlock, self).__init__() + self.layer_number = layer_number + + self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm + + self.fp32_residual_connection = config.fp32_residual_connection + + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Layernorm on the input data. + self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + # Self attention. + self.self_attention = SelfAttention(config, layer_number, device=device) + self.hidden_dropout = config.hidden_dropout + + # Layernorm on the attention output + self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + # MLP + self.mlp = MLP(config, device=device) + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True, + ): + # hidden_states: [s, b, h] + + # Layer norm at the beginning of the transformer layer. + layernorm_output = self.input_layernorm(hidden_states) + # Self attention. + attention_output, kv_cache = self.self_attention( + layernorm_output, + attention_mask, + rotary_pos_emb, + kv_cache=kv_cache, + use_cache=use_cache + ) + + # Residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = hidden_states + + layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training) + layernorm_input = residual + layernorm_input + + # Layer norm post the self attention. + layernorm_output = self.post_attention_layernorm(layernorm_input) + + # MLP. + mlp_output = self.mlp(layernorm_output) + + # Second residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = layernorm_input + + output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training) + output = residual + output + + return output, kv_cache + + +class GLMTransformer(torch.nn.Module): + """Transformer class.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(GLMTransformer, self).__init__() + + self.fp32_residual_connection = config.fp32_residual_connection + self.post_layer_norm = config.post_layer_norm + + # Number of layers. + self.num_layers = config.num_layers + + # Transformer layers. + def build_layer(layer_number): + return GLMBlock(config, layer_number, device=device) + + self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)]) + + if self.post_layer_norm: + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Final layer norm before output. + self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + self.gradient_checkpointing = False + + def _get_layer(self, layer_number): + return self.layers[layer_number] + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None, + use_cache: Optional[bool] = True, + output_hidden_states: Optional[bool] = False, + ): + if not kv_caches: + kv_caches = [None for _ in range(self.num_layers)] + presents = () if use_cache else None + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + all_self_attentions = None + all_hidden_states = () if output_hidden_states else None + for index in range(self.num_layers): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer = self._get_layer(index) + if self.gradient_checkpointing and self.training: + layer_ret = torch.utils.checkpoint.checkpoint( + layer, + hidden_states, + attention_mask, + rotary_pos_emb, + kv_caches[index], + use_cache + ) + else: + layer_ret = layer( + hidden_states, + attention_mask, + rotary_pos_emb, + kv_cache=kv_caches[index], + use_cache=use_cache + ) + hidden_states, kv_cache = layer_ret + if use_cache: + presents = presents + (kv_cache,) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # Final layer norm. + if self.post_layer_norm: + hidden_states = self.final_layernorm(hidden_states) + + return hidden_states, presents, all_hidden_states, all_self_attentions + + +class ChatGLMPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and + a simple interface for downloading and loading pretrained models. + """ + + is_parallelizable = False + supports_gradient_checkpointing = True + config_class = ChatGLMConfig + base_model_prefix = "transformer" + _no_split_modules = ["GLMBlock"] + + def _init_weights(self, module: nn.Module): + """Initialize the weights.""" + return + + def get_masks(self, input_ids, past_key_values, padding_mask=None): + batch_size, seq_length = input_ids.shape + full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device) + full_attention_mask.tril_() + past_length = 0 + if past_key_values: + past_length = past_key_values[0][0].shape[0] + if past_length: + full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length, + device=input_ids.device), full_attention_mask), dim=-1) + if padding_mask is not None: + full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1) + if not past_length and padding_mask is not None: + full_attention_mask -= padding_mask.unsqueeze(-1) - 1 + full_attention_mask = (full_attention_mask < 0.5).bool() + full_attention_mask.unsqueeze_(1) + return full_attention_mask + + def get_position_ids(self, input_ids, device): + batch_size, seq_length = input_ids.shape + position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) + return position_ids + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, GLMTransformer): + module.gradient_checkpointing = value + + +class Embedding(torch.nn.Module): + """Language model embeddings.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(Embedding, self).__init__() + + self.hidden_size = config.hidden_size + # Word embeddings (parallel). + self.word_embeddings = nn.Embedding( + config.padded_vocab_size, + self.hidden_size, + dtype=config.torch_dtype, + device=device + ) + self.fp32_residual_connection = config.fp32_residual_connection + + def forward(self, input_ids): + # Embeddings. + words_embeddings = self.word_embeddings(input_ids) + embeddings = words_embeddings + # Data format change to avoid explicit tranposes : [b s h] --> [s b h]. + embeddings = embeddings.transpose(0, 1).contiguous() + # If the input flag for fp32 residual connection is set, convert for float. + if self.fp32_residual_connection: + embeddings = embeddings.float() + return embeddings + + +class ChatGLMModel(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, device=None, empty_init=True): + super().__init__(config) + if empty_init: + init_method = skip_init + else: + init_method = default_init + init_kwargs = {} + if device is not None: + init_kwargs["device"] = device + self.embedding = init_method(Embedding, config, **init_kwargs) + self.num_layers = config.num_layers + self.multi_query_group_num = config.multi_query_group_num + self.kv_channels = config.kv_channels + + # Rotary positional embeddings + self.seq_length = config.seq_length + rotary_dim = ( + config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels + ) + + self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, original_impl=config.original_rope, device=device, + dtype=config.torch_dtype) + self.encoder = init_method(GLMTransformer, config, **init_kwargs) + self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False, + dtype=config.torch_dtype, **init_kwargs) + self.pre_seq_len = config.pre_seq_len + self.prefix_projection = config.prefix_projection + if self.pre_seq_len is not None: + for param in self.parameters(): + param.requires_grad = False + self.prefix_tokens = torch.arange(self.pre_seq_len).long() + self.prefix_encoder = PrefixEncoder(config) + self.dropout = torch.nn.Dropout(0.1) + + def get_input_embeddings(self): + return self.embedding.word_embeddings + + def get_prompt(self, batch_size, device, dtype=torch.half): + prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device) + past_key_values = self.prefix_encoder(prefix_tokens).type(dtype) + past_key_values = past_key_values.view( + batch_size, + self.pre_seq_len, + self.num_layers * 2, + self.multi_query_group_num, + self.kv_channels + ) + # seq_len, b, nh, hidden_size + past_key_values = self.dropout(past_key_values) + past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2) + return past_key_values + + def forward( + self, + input_ids, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.BoolTensor] = None, + full_attention_mask: Optional[torch.BoolTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + batch_size, seq_length = input_ids.shape + + if inputs_embeds is None: + inputs_embeds = self.embedding(input_ids) + + if self.pre_seq_len is not None: + if past_key_values is None: + past_key_values = self.get_prompt(batch_size=batch_size, device=input_ids.device, + dtype=inputs_embeds.dtype) + if attention_mask is not None: + attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)), + attention_mask], dim=-1) + + if full_attention_mask is None: + if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1): + full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask) + + # Rotary positional embeddings + rotary_pos_emb = self.rotary_pos_emb(self.seq_length) + if position_ids is not None: + rotary_pos_emb = rotary_pos_emb[position_ids] + else: + rotary_pos_emb = rotary_pos_emb[None, :seq_length] + rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous() + + # Run encoder. + hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder( + inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb, + kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states + ) + + if not return_dict: + return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + def quantize(self, weight_bit_width: int): + from .quantization import quantize + quantize(self.encoder, weight_bit_width) + return self + + +class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): + super().__init__(config) + + self.max_sequence_length = config.max_length + self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) + self.config = config + self.quantized = False + + if self.config.quantization_bit: + self.quantize(self.config.quantization_bit, empty_init=True) + + def _update_model_kwargs_for_generation( + self, + outputs: ModelOutput, + model_kwargs: Dict[str, Any], + is_encoder_decoder: bool = False, + standardize_cache_format: bool = False, + ) -> Dict[str, Any]: + # update past_key_values + model_kwargs["past_key_values"] = self._extract_past_from_model_output( + outputs, standardize_cache_format=standardize_cache_format + ) + + # update attention mask + if "attention_mask" in model_kwargs: + attention_mask = model_kwargs["attention_mask"] + model_kwargs["attention_mask"] = torch.cat( + [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 + ) + + # update position ids + if "position_ids" in model_kwargs: + position_ids = model_kwargs["position_ids"] + new_position_id = position_ids[..., -1:].clone() + new_position_id += 1 + model_kwargs["position_ids"] = torch.cat( + [position_ids, new_position_id], dim=-1 + ) + + model_kwargs["is_first_forward"] = False + return model_kwargs + + def prepare_inputs_for_generation( + self, + input_ids: torch.LongTensor, + past_key_values: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + is_first_forward: bool = True, + **kwargs + ) -> dict: + # only last token for input_ids if past is not None + if position_ids is None: + position_ids = self.get_position_ids(input_ids, device=input_ids.device) + if not is_first_forward: + if past_key_values is not None: + position_ids = position_ids[..., -1:] + input_ids = input_ids[:, -1:] + return { + "input_ids": input_ids, + "past_key_values": past_key_values, + "position_ids": position_ids, + "attention_mask": attention_mask, + "return_last_logit": True, + "use_cache": use_cache + } + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + return_last_logit: Optional[bool] = False, + ): + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + if return_last_logit: + hidden_states = hidden_states[-1:] + lm_logits = self.transformer.output_layer(hidden_states) + lm_logits = lm_logits.transpose(0, 1).contiguous() + + loss = None + if labels is not None: + lm_logits = lm_logits.to(torch.float32) + + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss(ignore_index=-100) + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + lm_logits = lm_logits.to(hidden_states.dtype) + loss = loss.to(hidden_states.dtype) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + @staticmethod + def _reorder_cache( + past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor + ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: + """ + This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or + [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct + beam_idx at every generation step. + + Output shares the same memory storage as `past`. + """ + return tuple( + ( + layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)), + layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)), + ) + for layer_past in past + ) + + def process_response(self, output, history): + content = "" + history = deepcopy(history) + for response in output.split("<|assistant|>"): + metadata, content = response.split("\n", maxsplit=1) + if not metadata.strip(): + content = content.strip() + history.append({"role": "assistant", "metadata": metadata, "content": content}) + content = content.replace("[[训练时间]]", "2023年") + else: + history.append({"role": "assistant", "metadata": metadata, "content": content}) + if history[0]["role"] == "system" and "tools" in history[0]: + content = "\n".join(content.split("\n")[1:-1]) + def tool_call(**kwargs): + return kwargs + parameters = eval(content) + content = {"name": metadata.strip(), "parameters": parameters} + else: + content = {"name": metadata.strip(), "content": content} + return content, history + + @torch.inference_mode() + def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, role: str = "user", + max_length: int = 8192, num_beams=1, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, + **kwargs): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + inputs = tokenizer.build_chat_input(query, history=history, role=role) + inputs = inputs.to(self.device) + eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"), + tokenizer.get_command("<|observation|>")] + outputs = self.generate(**inputs, **gen_kwargs, eos_token_id=eos_token_id) + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1] + response = tokenizer.decode(outputs) + history.append({"role": role, "content": query}) + response, history = self.process_response(response, history) + return response, history + + @torch.inference_mode() + def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, role: str = "user", + past_key_values=None,max_length: int = 8192, do_sample=True, top_p=0.8, temperature=0.8, + logits_processor=None, return_past_key_values=False, **kwargs): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"), + tokenizer.get_command("<|observation|>")] + gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + if past_key_values is None: + inputs = tokenizer.build_chat_input(query, history=history, role=role) + else: + inputs = tokenizer.build_chat_input(query, role=role) + inputs = inputs.to(self.device) + if past_key_values is not None: + past_length = past_key_values[0][0].shape[0] + if self.transformer.pre_seq_len is not None: + past_length -= self.transformer.pre_seq_len + inputs.position_ids += past_length + attention_mask = inputs.attention_mask + attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1) + inputs['attention_mask'] = attention_mask + history.append({"role": role, "content": query}) + for outputs in self.stream_generate(**inputs, past_key_values=past_key_values, + eos_token_id=eos_token_id, return_past_key_values=return_past_key_values, + **gen_kwargs): + if return_past_key_values: + outputs, past_key_values = outputs + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1] + response = tokenizer.decode(outputs) + if response and response[-1] != "�": + response, new_history = self.process_response(response, history) + if return_past_key_values: + yield response, new_history, past_key_values + else: + yield response, new_history + + @torch.inference_mode() + def stream_generate( + self, + input_ids, + generation_config: Optional[GenerationConfig] = None, + logits_processor: Optional[LogitsProcessorList] = None, + stopping_criteria: Optional[StoppingCriteriaList] = None, + prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None, + return_past_key_values=False, + **kwargs, + ): + batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] + + if generation_config is None: + generation_config = self.generation_config + generation_config = copy.deepcopy(generation_config) + model_kwargs = generation_config.update(**kwargs) + model_kwargs["use_cache"] = generation_config.use_cache + bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id + + if isinstance(eos_token_id, int): + eos_token_id = [eos_token_id] + eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None + + has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None + if has_default_max_length and generation_config.max_new_tokens is None: + warnings.warn( + f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. " + "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we" + " recommend using `max_new_tokens` to control the maximum length of the generation.", + UserWarning, + ) + elif generation_config.max_new_tokens is not None: + generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length + if not has_default_max_length: + logger.warn( + f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(=" + f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. " + "Please refer to the documentation for more information. " + "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)", + UserWarning, + ) + + if input_ids_seq_length >= generation_config.max_length: + input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids" + logger.warning( + f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to" + f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider" + " increasing `max_new_tokens`." + ) + + # 2. Set generation parameters if not already defined + logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList() + stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList() + + logits_processor = self._get_logits_processor( + generation_config=generation_config, + input_ids_seq_length=input_ids_seq_length, + encoder_input_ids=input_ids, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + logits_processor=logits_processor, + ) + + stopping_criteria = self._get_stopping_criteria( + generation_config=generation_config, stopping_criteria=stopping_criteria + ) + logits_warper = self._get_logits_warper(generation_config) + + unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) + scores = None + while True: + model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) + # forward pass to get next token + outputs = self( + **model_inputs, + return_dict=True, + output_attentions=False, + output_hidden_states=False, + ) + + next_token_logits = outputs.logits[:, -1, :] + + # pre-process distribution + next_token_scores = logits_processor(input_ids, next_token_logits) + next_token_scores = logits_warper(input_ids, next_token_scores) + + # sample + probs = nn.functional.softmax(next_token_scores, dim=-1) + if generation_config.do_sample: + next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) + else: + next_tokens = torch.argmax(probs, dim=-1) + # update generated ids, model inputs, and length for next step + input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) + model_kwargs = self._update_model_kwargs_for_generation( + outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder + ) + unfinished_sequences = unfinished_sequences.mul( + next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0) + ) + if return_past_key_values: + yield input_ids, outputs.past_key_values + else: + yield input_ids + # stop when each sentence is finished, or if we exceed the maximum length + if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores): + break + + def quantize(self, bits: int, empty_init=False, device=None, **kwargs): + if bits == 0: + return + + from .quantization import quantize + + if self.quantized: + logger.info("Already quantized.") + return self + + self.quantized = True + + self.config.quantization_bit = bits + + self.transformer.encoder = quantize(self.transformer.encoder, bits, empty_init=empty_init, device=device, + **kwargs) + return self + + +class ChatGLMForSequenceClassification(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): + super().__init__(config) + + self.num_labels = config.num_labels + self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) + + self.classifier_head = nn.Linear(config.hidden_size, config.num_labels, bias=True, dtype=torch.half) + if config.classifier_dropout is not None: + self.dropout = nn.Dropout(config.classifier_dropout) + else: + self.dropout = None + self.config = config + + if self.config.quantization_bit: + self.quantize(self.config.quantization_bit, empty_init=True) + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + full_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + inputs_embeds: Optional[torch.LongTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutputWithPast]: + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + full_attention_mask=full_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + pooled_hidden_states = hidden_states[-1] + if self.dropout is not None: + pooled_hidden_states = self.dropout(pooled_hidden_states) + logits = self.classifier_head(pooled_hidden_states) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze().float(), labels.squeeze()) + else: + loss = loss_fct(logits.float(), labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels).float(), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits.float(), labels.view(-1, self.num_labels)) + + if not return_dict: + output = (logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) diff --git a/diffsynth/models/sd3_dit.py b/diffsynth/models/sd3_dit.py new file mode 100644 index 0000000000000000000000000000000000000000..b21077dcae217eba01f767dd6f76e4f3dddf4308 --- /dev/null +++ b/diffsynth/models/sd3_dit.py @@ -0,0 +1,797 @@ +import torch +from einops import rearrange +from .svd_unet import TemporalTimesteps +from .tiler import TileWorker + + + +class PatchEmbed(torch.nn.Module): + def __init__(self, patch_size=2, in_channels=16, embed_dim=1536, pos_embed_max_size=192): + super().__init__() + self.pos_embed_max_size = pos_embed_max_size + self.patch_size = patch_size + + self.proj = torch.nn.Conv2d(in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size) + self.pos_embed = torch.nn.Parameter(torch.zeros(1, self.pos_embed_max_size, self.pos_embed_max_size, 1536)) + + def cropped_pos_embed(self, height, width): + height = height // self.patch_size + width = width // self.patch_size + top = (self.pos_embed_max_size - height) // 2 + left = (self.pos_embed_max_size - width) // 2 + spatial_pos_embed = self.pos_embed[:, top : top + height, left : left + width, :].flatten(1, 2) + return spatial_pos_embed + + def forward(self, latent): + height, width = latent.shape[-2:] + latent = self.proj(latent) + latent = latent.flatten(2).transpose(1, 2) + pos_embed = self.cropped_pos_embed(height, width) + return latent + pos_embed + + + +class TimestepEmbeddings(torch.nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.time_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0) + self.timestep_embedder = torch.nn.Sequential( + torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out) + ) + + def forward(self, timestep, dtype): + time_emb = self.time_proj(timestep).to(dtype) + time_emb = self.timestep_embedder(time_emb) + return time_emb + + + +class AdaLayerNorm(torch.nn.Module): + def __init__(self, dim, single=False): + super().__init__() + self.single = single + self.linear = torch.nn.Linear(dim, dim * (2 if single else 6)) + self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + + def forward(self, x, emb): + emb = self.linear(torch.nn.functional.silu(emb)) + if self.single: + scale, shift = emb.unsqueeze(1).chunk(2, dim=2) + x = self.norm(x) * (1 + scale) + shift + return x + else: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.unsqueeze(1).chunk(6, dim=2) + x = self.norm(x) * (1 + scale_msa) + shift_msa + return x, gate_msa, shift_mlp, scale_mlp, gate_mlp + + + +class JointAttention(torch.nn.Module): + def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False): + super().__init__() + self.num_heads = num_heads + self.head_dim = head_dim + self.only_out_a = only_out_a + + self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3) + self.b_to_qkv = torch.nn.Linear(dim_b, dim_b * 3) + + self.a_to_out = torch.nn.Linear(dim_a, dim_a) + if not only_out_a: + self.b_to_out = torch.nn.Linear(dim_b, dim_b) + + def forward(self, hidden_states_a, hidden_states_b): + batch_size = hidden_states_a.shape[0] + + qkv = torch.concat([self.a_to_qkv(hidden_states_a), self.b_to_qkv(hidden_states_b)], dim=1) + qkv = qkv.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) + q, k, v = qkv.chunk(3, dim=1) + + hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v) + hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) + hidden_states = hidden_states.to(q.dtype) + hidden_states_a, hidden_states_b = hidden_states[:, :hidden_states_a.shape[1]], hidden_states[:, hidden_states_a.shape[1]:] + hidden_states_a = self.a_to_out(hidden_states_a) + if self.only_out_a: + return hidden_states_a + else: + hidden_states_b = self.b_to_out(hidden_states_b) + return hidden_states_a, hidden_states_b + + + +class JointTransformerBlock(torch.nn.Module): + def __init__(self, dim, num_attention_heads): + super().__init__() + self.norm1_a = AdaLayerNorm(dim) + self.norm1_b = AdaLayerNorm(dim) + + self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads) + + self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff_a = torch.nn.Sequential( + torch.nn.Linear(dim, dim*4), + torch.nn.GELU(approximate="tanh"), + torch.nn.Linear(dim*4, dim) + ) + + self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff_b = torch.nn.Sequential( + torch.nn.Linear(dim, dim*4), + torch.nn.GELU(approximate="tanh"), + torch.nn.Linear(dim*4, dim) + ) + + + def forward(self, hidden_states_a, hidden_states_b, temb): + norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb) + norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb) + + # Attention + attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b) + + # Part A + hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a + norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a + hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a) + + # Part B + hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b + norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b + hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b) + + return hidden_states_a, hidden_states_b + + + +class JointTransformerFinalBlock(torch.nn.Module): + def __init__(self, dim, num_attention_heads): + super().__init__() + self.norm1_a = AdaLayerNorm(dim) + self.norm1_b = AdaLayerNorm(dim, single=True) + + self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, only_out_a=True) + + self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff_a = torch.nn.Sequential( + torch.nn.Linear(dim, dim*4), + torch.nn.GELU(approximate="tanh"), + torch.nn.Linear(dim*4, dim) + ) + + + def forward(self, hidden_states_a, hidden_states_b, temb): + norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb) + norm_hidden_states_b = self.norm1_b(hidden_states_b, emb=temb) + + # Attention + attn_output_a = self.attn(norm_hidden_states_a, norm_hidden_states_b) + + # Part A + hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a + norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a + hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a) + + return hidden_states_a, hidden_states_b + + + +class SD3DiT(torch.nn.Module): + def __init__(self): + super().__init__() + self.pos_embedder = PatchEmbed(patch_size=2, in_channels=16, embed_dim=1536, pos_embed_max_size=192) + self.time_embedder = TimestepEmbeddings(256, 1536) + self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(2048, 1536), torch.nn.SiLU(), torch.nn.Linear(1536, 1536)) + self.context_embedder = torch.nn.Linear(4096, 1536) + self.blocks = torch.nn.ModuleList([JointTransformerBlock(1536, 24) for _ in range(23)] + [JointTransformerFinalBlock(1536, 24)]) + self.norm_out = AdaLayerNorm(1536, single=True) + self.proj_out = torch.nn.Linear(1536, 64) + + def tiled_forward(self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, tile_size=128, tile_stride=64): + # Due to the global positional embedding, we cannot implement layer-wise tiled forward. + hidden_states = TileWorker().tiled_forward( + lambda x: self.forward(x, timestep, prompt_emb, pooled_prompt_emb), + hidden_states, + tile_size, + tile_stride, + tile_device=hidden_states.device, + tile_dtype=hidden_states.dtype + ) + return hidden_states + + def forward(self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, tiled=False, tile_size=128, tile_stride=64, use_gradient_checkpointing=False): + if tiled: + return self.tiled_forward(hidden_states, timestep, prompt_emb, pooled_prompt_emb, tile_size, tile_stride) + conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb) + prompt_emb = self.context_embedder(prompt_emb) + + height, width = hidden_states.shape[-2:] + hidden_states = self.pos_embedder(hidden_states) + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + + for block in self.blocks: + if self.training and use_gradient_checkpointing: + hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, prompt_emb, conditioning, + use_reentrant=False, + ) + else: + hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning) + + hidden_states = self.norm_out(hidden_states, conditioning) + hidden_states = self.proj_out(hidden_states) + hidden_states = rearrange(hidden_states, "B (H W) (P Q C) -> B C (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2) + return hidden_states + + def state_dict_converter(self): + return SD3DiTStateDictConverter() + + + +class SD3DiTStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + rename_dict = { + "context_embedder": "context_embedder", + "pos_embed.pos_embed": "pos_embedder.pos_embed", + "pos_embed.proj": "pos_embedder.proj", + "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0", + "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2", + "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0", + "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2", + "norm_out.linear": "norm_out.linear", + "proj_out": "proj_out", + + "norm1.linear": "norm1_a.linear", + "norm1_context.linear": "norm1_b.linear", + "attn.to_q": "attn.a_to_q", + "attn.to_k": "attn.a_to_k", + "attn.to_v": "attn.a_to_v", + "attn.to_out.0": "attn.a_to_out", + "attn.add_q_proj": "attn.b_to_q", + "attn.add_k_proj": "attn.b_to_k", + "attn.add_v_proj": "attn.b_to_v", + "attn.to_add_out": "attn.b_to_out", + "ff.net.0.proj": "ff_a.0", + "ff.net.2": "ff_a.2", + "ff_context.net.0.proj": "ff_b.0", + "ff_context.net.2": "ff_b.2", + } + state_dict_ = {} + for name, param in state_dict.items(): + if name in rename_dict: + if name == "pos_embed.pos_embed": + param = param.reshape((1, 192, 192, 1536)) + state_dict_[rename_dict[name]] = param + elif name.endswith(".weight") or name.endswith(".bias"): + suffix = ".weight" if name.endswith(".weight") else ".bias" + prefix = name[:-len(suffix)] + if prefix in rename_dict: + state_dict_[rename_dict[prefix] + suffix] = param + elif prefix.startswith("transformer_blocks."): + names = prefix.split(".") + names[0] = "blocks" + middle = ".".join(names[2:]) + if middle in rename_dict: + name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]]) + state_dict_[name_] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "model.diffusion_model.context_embedder.bias": "context_embedder.bias", + 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"blocks.9.attn.a_to_out.bias", + "model.diffusion_model.joint_blocks.9.x_block.attn.proj.weight": "blocks.9.attn.a_to_out.weight", + "model.diffusion_model.joint_blocks.9.x_block.attn.qkv.bias": ['blocks.9.attn.a_to_q.bias', 'blocks.9.attn.a_to_k.bias', 'blocks.9.attn.a_to_v.bias'], + "model.diffusion_model.joint_blocks.9.x_block.attn.qkv.weight": ['blocks.9.attn.a_to_q.weight', 'blocks.9.attn.a_to_k.weight', 'blocks.9.attn.a_to_v.weight'], + "model.diffusion_model.joint_blocks.9.x_block.mlp.fc1.bias": "blocks.9.ff_a.0.bias", + "model.diffusion_model.joint_blocks.9.x_block.mlp.fc1.weight": "blocks.9.ff_a.0.weight", + "model.diffusion_model.joint_blocks.9.x_block.mlp.fc2.bias": "blocks.9.ff_a.2.bias", + "model.diffusion_model.joint_blocks.9.x_block.mlp.fc2.weight": "blocks.9.ff_a.2.weight", + "model.diffusion_model.pos_embed": "pos_embedder.pos_embed", + "model.diffusion_model.t_embedder.mlp.0.bias": "time_embedder.timestep_embedder.0.bias", + "model.diffusion_model.t_embedder.mlp.0.weight": "time_embedder.timestep_embedder.0.weight", + "model.diffusion_model.t_embedder.mlp.2.bias": "time_embedder.timestep_embedder.2.bias", + "model.diffusion_model.t_embedder.mlp.2.weight": "time_embedder.timestep_embedder.2.weight", + "model.diffusion_model.x_embedder.proj.bias": "pos_embedder.proj.bias", + "model.diffusion_model.x_embedder.proj.weight": "pos_embedder.proj.weight", + "model.diffusion_model.y_embedder.mlp.0.bias": "pooled_text_embedder.0.bias", + "model.diffusion_model.y_embedder.mlp.0.weight": "pooled_text_embedder.0.weight", + "model.diffusion_model.y_embedder.mlp.2.bias": "pooled_text_embedder.2.bias", + "model.diffusion_model.y_embedder.mlp.2.weight": "pooled_text_embedder.2.weight", + + "model.diffusion_model.joint_blocks.23.context_block.adaLN_modulation.1.weight": "blocks.23.norm1_b.linear.weight", + "model.diffusion_model.joint_blocks.23.context_block.adaLN_modulation.1.bias": "blocks.23.norm1_b.linear.bias", + "model.diffusion_model.final_layer.adaLN_modulation.1.weight": "norm_out.linear.weight", + "model.diffusion_model.final_layer.adaLN_modulation.1.bias": "norm_out.linear.bias", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name.startswith("model.diffusion_model.joint_blocks.23.context_block.adaLN_modulation.1."): + param = torch.concat([param[1536:], param[:1536]], axis=0) + elif name.startswith("model.diffusion_model.final_layer.adaLN_modulation.1."): + param = torch.concat([param[1536:], param[:1536]], axis=0) + elif name == "model.diffusion_model.pos_embed": + param = param.reshape((1, 192, 192, 1536)) + if isinstance(rename_dict[name], str): + state_dict_[rename_dict[name]] = param + else: + name_ = rename_dict[name][0].replace(".a_to_q.", ".a_to_qkv.").replace(".b_to_q.", ".b_to_qkv.") + state_dict_[name_] = param + return state_dict_ diff --git a/diffsynth/models/sd3_text_encoder.py b/diffsynth/models/sd3_text_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..bb0fc6da5eaafd9167eee114bb6183df4abda71f --- /dev/null +++ b/diffsynth/models/sd3_text_encoder.py @@ -0,0 +1,1104 @@ +import torch +from transformers import T5EncoderModel, T5Config +from .sd_text_encoder import SDTextEncoder +from .sdxl_text_encoder import SDXLTextEncoder2, SDXLTextEncoder2StateDictConverter + + +class SD3TextEncoder1(SDTextEncoder): + def __init__(self, vocab_size=49408): + super().__init__(vocab_size=vocab_size) + + def forward(self, input_ids, clip_skip=2): + embeds = self.token_embedding(input_ids) + self.position_embeds + attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype) + for encoder_id, encoder in enumerate(self.encoders): + embeds = encoder(embeds, attn_mask=attn_mask) + if encoder_id + clip_skip == len(self.encoders): + hidden_states = embeds + embeds = self.final_layer_norm(embeds) + pooled_embeds = embeds[torch.arange(embeds.shape[0]), input_ids.to(dtype=torch.int).argmax(dim=-1)] + return pooled_embeds, hidden_states + + def state_dict_converter(self): + return SD3TextEncoder1StateDictConverter() + + + +class SD3TextEncoder2(SDXLTextEncoder2): + def __init__(self): + super().__init__() + + def state_dict_converter(self): + return SD3TextEncoder2StateDictConverter() + + +class SD3TextEncoder3(T5EncoderModel): + def __init__(self): + config = T5Config( + _name_or_path = ".", + architectures = ["T5EncoderModel"], + classifier_dropout = 0.0, + d_ff = 10240, + d_kv = 64, + d_model = 4096, + decoder_start_token_id = 0, + dense_act_fn = "gelu_new", + dropout_rate = 0.1, + eos_token_id = 1, + feed_forward_proj = "gated-gelu", + initializer_factor = 1.0, + is_encoder_decoder = True, + is_gated_act = True, + layer_norm_epsilon = 1e-06, + model_type = "t5", + num_decoder_layers = 24, + num_heads = 64, + num_layers = 24, + output_past = True, + pad_token_id = 0, + relative_attention_max_distance = 128, + relative_attention_num_buckets = 32, + tie_word_embeddings = False, + torch_dtype = torch.float16, + transformers_version = "4.41.2", + use_cache = True, + vocab_size = 32128 + ) + super().__init__(config) + self.eval() + + def forward(self, input_ids): + outputs = super().forward(input_ids=input_ids) + prompt_emb = outputs.last_hidden_state + return prompt_emb + + def state_dict_converter(self): + return SD3TextEncoder3StateDictConverter() + + + +class SD3TextEncoder1StateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + rename_dict = { + "text_model.embeddings.token_embedding.weight": "token_embedding.weight", + "text_model.embeddings.position_embedding.weight": "position_embeds", + "text_model.final_layer_norm.weight": "final_layer_norm.weight", + "text_model.final_layer_norm.bias": "final_layer_norm.bias", + } + attn_rename_dict = { + "self_attn.q_proj": "attn.to_q", + "self_attn.k_proj": "attn.to_k", + "self_attn.v_proj": "attn.to_v", + "self_attn.out_proj": "attn.to_out", + "layer_norm1": "layer_norm1", + "layer_norm2": "layer_norm2", + "mlp.fc1": "fc1", + "mlp.fc2": "fc2", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict[name]] = param + elif name.startswith("text_model.encoder.layers."): + param = state_dict[name] + names = name.split(".") + layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1] + name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail]) + state_dict_[name_] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "text_encoders.clip_l.transformer.text_model.embeddings.position_embedding.weight": "position_embeds", + "text_encoders.clip_l.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias": "encoders.0.attn.to_out.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight": "encoders.0.attn.to_out.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias": "encoders.0.attn.to_q.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.q_proj.weight": "encoders.0.attn.to_q.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias": "encoders.0.attn.to_v.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.v_proj.weight": "encoders.0.attn.to_v.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.1.layer_norm1.bias": "encoders.1.layer_norm1.bias", + 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"text_encoders.clip_l.transformer.text_model.encoder.layers.7.self_attn.q_proj.weight": "encoders.7.attn.to_q.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.7.self_attn.v_proj.bias": "encoders.7.attn.to_v.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.7.self_attn.v_proj.weight": "encoders.7.attn.to_v.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.8.layer_norm1.bias": "encoders.8.layer_norm1.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.8.layer_norm1.weight": "encoders.8.layer_norm1.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.8.layer_norm2.bias": "encoders.8.layer_norm2.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.8.layer_norm2.weight": "encoders.8.layer_norm2.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.8.mlp.fc1.bias": "encoders.8.fc1.bias", + 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"text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc1.weight": "encoders.9.fc1.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc2.bias": "encoders.9.fc2.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc2.weight": "encoders.9.fc2.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.k_proj.bias": "encoders.9.attn.to_k.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.k_proj.weight": "encoders.9.attn.to_k.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.out_proj.bias": "encoders.9.attn.to_out.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.out_proj.weight": "encoders.9.attn.to_out.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.q_proj.bias": "encoders.9.attn.to_q.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias", + "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight", + "text_encoders.clip_l.transformer.text_model.final_layer_norm.bias": "final_layer_norm.bias", + "text_encoders.clip_l.transformer.text_model.final_layer_norm.weight": "final_layer_norm.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "text_encoders.clip_l.transformer.text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict[name]] = param + return state_dict_ + + + +class SD3TextEncoder2StateDictConverter(SDXLTextEncoder2StateDictConverter): + def __init__(self): + pass + + def from_diffusers(self, state_dict): + return super().from_diffusers(state_dict) + + def from_civitai(self, state_dict): + rename_dict = { + "text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight": "position_embeds", + "text_encoders.clip_g.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias": "encoders.0.attn.to_out.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight": "encoders.0.attn.to_out.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias": "encoders.0.attn.to_q.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.q_proj.weight": "encoders.0.attn.to_q.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias": "encoders.0.attn.to_v.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.v_proj.weight": "encoders.0.attn.to_v.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm1.bias": "encoders.1.layer_norm1.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm1.weight": "encoders.1.layer_norm1.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm2.bias": "encoders.1.layer_norm2.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm2.weight": "encoders.1.layer_norm2.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc1.bias": "encoders.1.fc1.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc1.weight": "encoders.1.fc1.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc2.bias": 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"text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight", + "text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias", + "text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight", + "text_encoders.clip_g.transformer.text_model.final_layer_norm.bias": "final_layer_norm.bias", + "text_encoders.clip_g.transformer.text_model.final_layer_norm.weight": "final_layer_norm.weight", + "text_encoders.clip_g.transformer.text_projection.weight": "text_projection.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict[name]] = param + return state_dict_ + + + +class SD3TextEncoder3StateDictConverter(): + def __init__(self): + pass + + def from_diffusers(self, state_dict): + state_dict_ = state_dict + return state_dict_ + + def from_civitai(self, state_dict): + prefix = "text_encoders.t5xxl.transformer." + state_dict_ = {name[len(prefix):]: param for name, param in state_dict.items() if name.startswith(prefix)} + if len(state_dict_) > 0: + return self.from_diffusers(state_dict_) + name_list = [ + "encoder.block.0.layer.0.SelfAttention.k.weight", + "encoder.block.0.layer.0.SelfAttention.o.weight", + "encoder.block.0.layer.0.SelfAttention.q.weight", + "encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight", + "encoder.block.0.layer.0.SelfAttention.v.weight", + "encoder.block.0.layer.0.layer_norm.weight", + "encoder.block.0.layer.1.DenseReluDense.wi_0.weight", + "encoder.block.0.layer.1.DenseReluDense.wi_1.weight", + "encoder.block.0.layer.1.DenseReluDense.wo.weight", + "encoder.block.0.layer.1.layer_norm.weight", + 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Different from SD 1.x + self.shift_factor = 0.0609 # Different from SD 1.x + self.conv_in = torch.nn.Conv2d(16, 512, kernel_size=3, padding=1) # Different from SD 1.x + + self.blocks = torch.nn.ModuleList([ + # UNetMidBlock2D + ResnetBlock(512, 512, eps=1e-6), + VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + # UpDecoderBlock2D + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + UpSampler(512), + # UpDecoderBlock2D + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + UpSampler(512), + # UpDecoderBlock2D + ResnetBlock(512, 256, eps=1e-6), + ResnetBlock(256, 256, eps=1e-6), + ResnetBlock(256, 256, eps=1e-6), + UpSampler(256), + # UpDecoderBlock2D + ResnetBlock(256, 128, eps=1e-6), + ResnetBlock(128, 128, eps=1e-6), + ResnetBlock(128, 128, eps=1e-6), + ]) + + self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-6) + self.conv_act = torch.nn.SiLU() + self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1) + + def tiled_forward(self, sample, tile_size=64, tile_stride=32): + hidden_states = TileWorker().tiled_forward( + lambda x: self.forward(x), + sample, + tile_size, + tile_stride, + tile_device=sample.device, + tile_dtype=sample.dtype + ) + return hidden_states + + def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs): + # For VAE Decoder, we do not need to apply the tiler on each layer. + if tiled: + return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride) + + # 1. pre-process + hidden_states = sample / self.scaling_factor + self.shift_factor + hidden_states = self.conv_in(hidden_states) + time_emb = None + text_emb = None + res_stack = None + + # 2. blocks + for i, block in enumerate(self.blocks): + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + + # 3. output + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + + return hidden_states + + def state_dict_converter(self): + return SDVAEDecoderStateDictConverter() \ No newline at end of file diff --git a/diffsynth/models/sd3_vae_encoder.py b/diffsynth/models/sd3_vae_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..17aa47eeb1b43287072fca9b553d83bc33cfb604 --- /dev/null +++ b/diffsynth/models/sd3_vae_encoder.py @@ -0,0 +1,94 @@ +import torch +from .sd_unet import ResnetBlock, DownSampler +from .sd_vae_encoder import VAEAttentionBlock, SDVAEEncoderStateDictConverter +from .tiler import TileWorker +from einops import rearrange + + +class SD3VAEEncoder(torch.nn.Module): + def __init__(self): + super().__init__() + self.scaling_factor = 1.5305 # Different from SD 1.x + self.shift_factor = 0.0609 # Different from SD 1.x + self.conv_in = torch.nn.Conv2d(3, 128, kernel_size=3, padding=1) + + self.blocks = torch.nn.ModuleList([ + # DownEncoderBlock2D + ResnetBlock(128, 128, eps=1e-6), + ResnetBlock(128, 128, eps=1e-6), + DownSampler(128, padding=0, extra_padding=True), + # DownEncoderBlock2D + ResnetBlock(128, 256, eps=1e-6), + ResnetBlock(256, 256, eps=1e-6), + DownSampler(256, padding=0, extra_padding=True), + # DownEncoderBlock2D + ResnetBlock(256, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + DownSampler(512, padding=0, extra_padding=True), + # DownEncoderBlock2D + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + # UNetMidBlock2D + ResnetBlock(512, 512, eps=1e-6), + VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + ]) + + self.conv_norm_out = torch.nn.GroupNorm(num_channels=512, num_groups=32, eps=1e-6) + self.conv_act = torch.nn.SiLU() + self.conv_out = torch.nn.Conv2d(512, 32, kernel_size=3, padding=1) + + def tiled_forward(self, sample, tile_size=64, tile_stride=32): + hidden_states = TileWorker().tiled_forward( + lambda x: self.forward(x), + sample, + tile_size, + tile_stride, + tile_device=sample.device, + tile_dtype=sample.dtype + ) + return hidden_states + + def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs): + # For VAE Decoder, we do not need to apply the tiler on each layer. + if tiled: + return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride) + + # 1. pre-process + hidden_states = self.conv_in(sample) + time_emb = None + text_emb = None + res_stack = None + + # 2. blocks + for i, block in enumerate(self.blocks): + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + + # 3. output + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + hidden_states = hidden_states[:, :16] + hidden_states = (hidden_states - self.shift_factor) * self.scaling_factor + + return hidden_states + + def encode_video(self, sample, batch_size=8): + B = sample.shape[0] + hidden_states = [] + + for i in range(0, sample.shape[2], batch_size): + + j = min(i + batch_size, sample.shape[2]) + sample_batch = rearrange(sample[:,:,i:j], "B C T H W -> (B T) C H W") + + hidden_states_batch = self(sample_batch) + hidden_states_batch = rearrange(hidden_states_batch, "(B T) C H W -> B C T H W", B=B) + + hidden_states.append(hidden_states_batch) + + hidden_states = torch.concat(hidden_states, dim=2) + return hidden_states + + def state_dict_converter(self): + return SDVAEEncoderStateDictConverter() diff --git a/diffsynth/models/sd_controlnet.py b/diffsynth/models/sd_controlnet.py new file mode 100644 index 0000000000000000000000000000000000000000..15a483860999e089e0b7f6a4170252bbd21b7903 --- /dev/null +++ b/diffsynth/models/sd_controlnet.py @@ -0,0 +1,587 @@ +import torch +from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, DownSampler +from .tiler import TileWorker + + +class ControlNetConditioningLayer(torch.nn.Module): + def __init__(self, channels = (3, 16, 32, 96, 256, 320)): + super().__init__() + self.blocks = torch.nn.ModuleList([]) + self.blocks.append(torch.nn.Conv2d(channels[0], channels[1], kernel_size=3, padding=1)) + self.blocks.append(torch.nn.SiLU()) + for i in range(1, len(channels) - 2): + self.blocks.append(torch.nn.Conv2d(channels[i], channels[i], kernel_size=3, padding=1)) + self.blocks.append(torch.nn.SiLU()) + self.blocks.append(torch.nn.Conv2d(channels[i], channels[i+1], kernel_size=3, padding=1, stride=2)) + self.blocks.append(torch.nn.SiLU()) + self.blocks.append(torch.nn.Conv2d(channels[-2], channels[-1], kernel_size=3, padding=1)) + + def forward(self, conditioning): + for block in self.blocks: + conditioning = block(conditioning) + return conditioning + + +class SDControlNet(torch.nn.Module): + def __init__(self, global_pool=False): + super().__init__() + self.time_proj = Timesteps(320) + self.time_embedding = torch.nn.Sequential( + torch.nn.Linear(320, 1280), + torch.nn.SiLU(), + torch.nn.Linear(1280, 1280) + ) + self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1) + + self.controlnet_conv_in = ControlNetConditioningLayer(channels=(3, 16, 32, 96, 256, 320)) + + self.blocks = torch.nn.ModuleList([ + # CrossAttnDownBlock2D + ResnetBlock(320, 320, 1280), + AttentionBlock(8, 40, 320, 1, 768), + PushBlock(), + ResnetBlock(320, 320, 1280), + AttentionBlock(8, 40, 320, 1, 768), + PushBlock(), + DownSampler(320), + PushBlock(), + # CrossAttnDownBlock2D + ResnetBlock(320, 640, 1280), + AttentionBlock(8, 80, 640, 1, 768), + PushBlock(), + ResnetBlock(640, 640, 1280), + AttentionBlock(8, 80, 640, 1, 768), + PushBlock(), + DownSampler(640), + PushBlock(), + # CrossAttnDownBlock2D + ResnetBlock(640, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768), + PushBlock(), + ResnetBlock(1280, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768), + PushBlock(), + DownSampler(1280), + PushBlock(), + # DownBlock2D + ResnetBlock(1280, 1280, 1280), + PushBlock(), + ResnetBlock(1280, 1280, 1280), + PushBlock(), + # UNetMidBlock2DCrossAttn + ResnetBlock(1280, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768), + ResnetBlock(1280, 1280, 1280), + PushBlock() + ]) + + self.controlnet_blocks = torch.nn.ModuleList([ + torch.nn.Conv2d(320, 320, kernel_size=(1, 1)), + torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(640, 640, kernel_size=(1, 1)), + torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)), + torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), + torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), + ]) + + self.global_pool = global_pool + + def forward( + self, + sample, timestep, encoder_hidden_states, conditioning, + tiled=False, tile_size=64, tile_stride=32, + ): + # 1. time + time_emb = self.time_proj(timestep[None]).to(sample.dtype) + time_emb = self.time_embedding(time_emb) + time_emb = time_emb.repeat(sample.shape[0], 1) + + # 2. pre-process + height, width = sample.shape[2], sample.shape[3] + hidden_states = self.conv_in(sample) + self.controlnet_conv_in(conditioning) + text_emb = encoder_hidden_states + res_stack = [hidden_states] + + # 3. blocks + for i, block in enumerate(self.blocks): + if tiled and not isinstance(block, PushBlock): + _, _, inter_height, _ = hidden_states.shape + resize_scale = inter_height / height + hidden_states = TileWorker().tiled_forward( + lambda x: block(x, time_emb, text_emb, res_stack)[0], + hidden_states, + int(tile_size * resize_scale), + int(tile_stride * resize_scale), + tile_device=hidden_states.device, + tile_dtype=hidden_states.dtype + ) + else: + hidden_states, _, _, _ = block(hidden_states, time_emb, text_emb, res_stack) + + # 4. ControlNet blocks + controlnet_res_stack = [block(res) for block, res in zip(self.controlnet_blocks, res_stack)] + + # pool + if self.global_pool: + controlnet_res_stack = [res.mean(dim=(2, 3), keepdim=True) for res in controlnet_res_stack] + + return controlnet_res_stack + + def state_dict_converter(self): + return SDControlNetStateDictConverter() + + +class SDControlNetStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + # architecture + block_types = [ + 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', + 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'ResnetBlock', + 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'UpSampler', + 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', + 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', + 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock' + ] + + # controlnet_rename_dict + controlnet_rename_dict = { + "controlnet_cond_embedding.conv_in.weight": "controlnet_conv_in.blocks.0.weight", + "controlnet_cond_embedding.conv_in.bias": "controlnet_conv_in.blocks.0.bias", + "controlnet_cond_embedding.blocks.0.weight": "controlnet_conv_in.blocks.2.weight", + "controlnet_cond_embedding.blocks.0.bias": "controlnet_conv_in.blocks.2.bias", + "controlnet_cond_embedding.blocks.1.weight": "controlnet_conv_in.blocks.4.weight", + "controlnet_cond_embedding.blocks.1.bias": "controlnet_conv_in.blocks.4.bias", + "controlnet_cond_embedding.blocks.2.weight": "controlnet_conv_in.blocks.6.weight", + "controlnet_cond_embedding.blocks.2.bias": "controlnet_conv_in.blocks.6.bias", + "controlnet_cond_embedding.blocks.3.weight": "controlnet_conv_in.blocks.8.weight", + "controlnet_cond_embedding.blocks.3.bias": "controlnet_conv_in.blocks.8.bias", + "controlnet_cond_embedding.blocks.4.weight": "controlnet_conv_in.blocks.10.weight", + "controlnet_cond_embedding.blocks.4.bias": "controlnet_conv_in.blocks.10.bias", + "controlnet_cond_embedding.blocks.5.weight": "controlnet_conv_in.blocks.12.weight", + "controlnet_cond_embedding.blocks.5.bias": "controlnet_conv_in.blocks.12.bias", + "controlnet_cond_embedding.conv_out.weight": "controlnet_conv_in.blocks.14.weight", + "controlnet_cond_embedding.conv_out.bias": "controlnet_conv_in.blocks.14.bias", + } + + # Rename each parameter + name_list = sorted([name for name in state_dict]) + rename_dict = {} + block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1} + last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""} + for name in name_list: + names = name.split(".") + if names[0] in ["conv_in", "conv_norm_out", "conv_out"]: + pass + elif name in controlnet_rename_dict: + names = controlnet_rename_dict[name].split(".") + elif names[0] == "controlnet_down_blocks": + names[0] = "controlnet_blocks" + elif names[0] == "controlnet_mid_block": + names = ["controlnet_blocks", "12", names[-1]] + elif names[0] in ["time_embedding", "add_embedding"]: + if names[0] == "add_embedding": + names[0] = "add_time_embedding" + names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]] + elif names[0] in ["down_blocks", "mid_block", "up_blocks"]: + if names[0] == "mid_block": + names.insert(1, "0") + block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]] + block_type_with_id = ".".join(names[:4]) + if block_type_with_id != last_block_type_with_id[block_type]: + block_id[block_type] += 1 + last_block_type_with_id[block_type] = block_type_with_id + while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: + block_id[block_type] += 1 + block_type_with_id = ".".join(names[:4]) + names = ["blocks", str(block_id[block_type])] + names[4:] + if "ff" in names: + ff_index = names.index("ff") + component = ".".join(names[ff_index:ff_index+3]) + component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component] + names = names[:ff_index] + [component] + names[ff_index+3:] + if "to_out" in names: + names.pop(names.index("to_out") + 1) + else: + raise ValueError(f"Unknown parameters: {name}") + rename_dict[name] = ".".join(names) + + # Convert state_dict + state_dict_ = {} + for name, param in state_dict.items(): + if ".proj_in." in name or ".proj_out." in name: + param = param.squeeze() + if rename_dict[name] in [ + "controlnet_blocks.1.bias", "controlnet_blocks.2.bias", "controlnet_blocks.3.bias", "controlnet_blocks.5.bias", "controlnet_blocks.6.bias", + "controlnet_blocks.8.bias", "controlnet_blocks.9.bias", "controlnet_blocks.10.bias", "controlnet_blocks.11.bias", "controlnet_blocks.12.bias" + ]: + continue + state_dict_[rename_dict[name]] = param + return state_dict_ + + def from_civitai(self, state_dict): + if "mid_block.resnets.1.time_emb_proj.weight" in state_dict: + # For controlnets in diffusers format + return self.from_diffusers(state_dict) + rename_dict = { + "control_model.time_embed.0.weight": "time_embedding.0.weight", + "control_model.time_embed.0.bias": "time_embedding.0.bias", + "control_model.time_embed.2.weight": "time_embedding.2.weight", + "control_model.time_embed.2.bias": "time_embedding.2.bias", + "control_model.input_blocks.0.0.weight": "conv_in.weight", + "control_model.input_blocks.0.0.bias": "conv_in.bias", + "control_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight", + "control_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias", + "control_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight", + "control_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias", + "control_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight", + "control_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias", + "control_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight", + "control_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias", + "control_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight", + "control_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias", + "control_model.input_blocks.1.1.norm.weight": "blocks.1.norm.weight", + "control_model.input_blocks.1.1.norm.bias": "blocks.1.norm.bias", + "control_model.input_blocks.1.1.proj_in.weight": "blocks.1.proj_in.weight", + "control_model.input_blocks.1.1.proj_in.bias": "blocks.1.proj_in.bias", + "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight": "blocks.1.transformer_blocks.0.attn1.to_q.weight", + "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight": "blocks.1.transformer_blocks.0.attn1.to_k.weight", + "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight": "blocks.1.transformer_blocks.0.attn1.to_v.weight", + "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.1.transformer_blocks.0.attn1.to_out.weight", + "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.1.transformer_blocks.0.attn1.to_out.bias", + "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.1.transformer_blocks.0.act_fn.proj.weight", + "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.1.transformer_blocks.0.act_fn.proj.bias", + "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.weight": "blocks.1.transformer_blocks.0.ff.weight", + "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias": "blocks.1.transformer_blocks.0.ff.bias", + "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_q.weight": "blocks.1.transformer_blocks.0.attn2.to_q.weight", + "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight": "blocks.1.transformer_blocks.0.attn2.to_k.weight", + "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_v.weight": "blocks.1.transformer_blocks.0.attn2.to_v.weight", + "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.1.transformer_blocks.0.attn2.to_out.weight", + "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.1.transformer_blocks.0.attn2.to_out.bias", + "control_model.input_blocks.1.1.transformer_blocks.0.norm1.weight": "blocks.1.transformer_blocks.0.norm1.weight", + 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"control_model.middle_block.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.29.transformer_blocks.0.attn2.to_out.bias", + "control_model.middle_block.1.transformer_blocks.0.norm1.weight": "blocks.29.transformer_blocks.0.norm1.weight", + "control_model.middle_block.1.transformer_blocks.0.norm1.bias": "blocks.29.transformer_blocks.0.norm1.bias", + "control_model.middle_block.1.transformer_blocks.0.norm2.weight": "blocks.29.transformer_blocks.0.norm2.weight", + "control_model.middle_block.1.transformer_blocks.0.norm2.bias": "blocks.29.transformer_blocks.0.norm2.bias", + "control_model.middle_block.1.transformer_blocks.0.norm3.weight": "blocks.29.transformer_blocks.0.norm3.weight", + "control_model.middle_block.1.transformer_blocks.0.norm3.bias": "blocks.29.transformer_blocks.0.norm3.bias", + "control_model.middle_block.1.proj_out.weight": "blocks.29.proj_out.weight", + "control_model.middle_block.1.proj_out.bias": "blocks.29.proj_out.bias", + "control_model.middle_block.2.in_layers.0.weight": "blocks.30.norm1.weight", + "control_model.middle_block.2.in_layers.0.bias": "blocks.30.norm1.bias", + "control_model.middle_block.2.in_layers.2.weight": "blocks.30.conv1.weight", + "control_model.middle_block.2.in_layers.2.bias": "blocks.30.conv1.bias", + "control_model.middle_block.2.emb_layers.1.weight": "blocks.30.time_emb_proj.weight", + "control_model.middle_block.2.emb_layers.1.bias": "blocks.30.time_emb_proj.bias", + "control_model.middle_block.2.out_layers.0.weight": "blocks.30.norm2.weight", + "control_model.middle_block.2.out_layers.0.bias": "blocks.30.norm2.bias", + "control_model.middle_block.2.out_layers.3.weight": "blocks.30.conv2.weight", + "control_model.middle_block.2.out_layers.3.bias": "blocks.30.conv2.bias", + "control_model.middle_block_out.0.weight": "controlnet_blocks.12.weight", + "control_model.middle_block_out.0.bias": "controlnet_blocks.7.bias", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if ".proj_in." in name or ".proj_out." in name: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ diff --git a/diffsynth/models/sd_ipadapter.py b/diffsynth/models/sd_ipadapter.py new file mode 100644 index 0000000000000000000000000000000000000000..e1554f27a207e6e3de8c79f703469361c205d2e6 --- /dev/null +++ b/diffsynth/models/sd_ipadapter.py @@ -0,0 +1,56 @@ +from .svd_image_encoder import SVDImageEncoder +from .sdxl_ipadapter import IpAdapterImageProjModel, IpAdapterModule, SDXLIpAdapterStateDictConverter +from transformers import CLIPImageProcessor +import torch + + +class IpAdapterCLIPImageEmbedder(SVDImageEncoder): + def __init__(self): + super().__init__() + self.image_processor = CLIPImageProcessor() + + def forward(self, image): + pixel_values = self.image_processor(images=image, return_tensors="pt").pixel_values + pixel_values = pixel_values.to(device=self.embeddings.class_embedding.device, dtype=self.embeddings.class_embedding.dtype) + return super().forward(pixel_values) + + +class SDIpAdapter(torch.nn.Module): + def __init__(self): + super().__init__() + shape_list = [(768, 320)] * 2 + [(768, 640)] * 2 + [(768, 1280)] * 5 + [(768, 640)] * 3 + [(768, 320)] * 3 + [(768, 1280)] * 1 + self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(*shape) for shape in shape_list]) + self.image_proj = IpAdapterImageProjModel(cross_attention_dim=768, clip_embeddings_dim=1024, clip_extra_context_tokens=4) + self.set_full_adapter() + + def set_full_adapter(self): + block_ids = [1, 4, 9, 12, 17, 20, 40, 43, 46, 50, 53, 56, 60, 63, 66, 29] + self.call_block_id = {(i, 0): j for j, i in enumerate(block_ids)} + + def set_less_adapter(self): + # IP-Adapter for SD v1.5 doesn't support this feature. + self.set_full_adapter() + + def forward(self, hidden_states, scale=1.0): + hidden_states = self.image_proj(hidden_states) + hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1]) + ip_kv_dict = {} + for (block_id, transformer_id) in self.call_block_id: + ipadapter_id = self.call_block_id[(block_id, transformer_id)] + ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states) + if block_id not in ip_kv_dict: + ip_kv_dict[block_id] = {} + ip_kv_dict[block_id][transformer_id] = { + "ip_k": ip_k, + "ip_v": ip_v, + "scale": scale + } + return ip_kv_dict + + def state_dict_converter(self): + return SDIpAdapterStateDictConverter() + + +class SDIpAdapterStateDictConverter(SDXLIpAdapterStateDictConverter): + def __init__(self): + pass diff --git a/diffsynth/models/sd_lora.py b/diffsynth/models/sd_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..3b7ecacc3f19040152ef4036988082eff35d8f4c --- /dev/null +++ b/diffsynth/models/sd_lora.py @@ -0,0 +1,60 @@ +import torch +from .sd_unet import SDUNetStateDictConverter, SDUNet +from .sd_text_encoder import SDTextEncoderStateDictConverter, SDTextEncoder + + +class SDLoRA: + def __init__(self): + pass + + def convert_state_dict(self, state_dict, lora_prefix="lora_unet_", alpha=1.0, device="cuda"): + special_keys = { + "down.blocks": "down_blocks", + "up.blocks": "up_blocks", + "mid.block": "mid_block", + "proj.in": "proj_in", + "proj.out": "proj_out", + "transformer.blocks": "transformer_blocks", + "to.q": "to_q", + "to.k": "to_k", + "to.v": "to_v", + "to.out": "to_out", + } + state_dict_ = {} + for key in state_dict: + if ".lora_up" not in key: + continue + if not key.startswith(lora_prefix): + continue + weight_up = state_dict[key].to(device="cuda", dtype=torch.float16) + weight_down = state_dict[key.replace(".lora_up", ".lora_down")].to(device="cuda", dtype=torch.float16) + if len(weight_up.shape) == 4: + weight_up = weight_up.squeeze(3).squeeze(2).to(torch.float32) + weight_down = weight_down.squeeze(3).squeeze(2).to(torch.float32) + lora_weight = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) + else: + lora_weight = alpha * torch.mm(weight_up, weight_down) + target_name = key.split(".")[0].replace("_", ".")[len(lora_prefix):] + ".weight" + for special_key in special_keys: + target_name = target_name.replace(special_key, special_keys[special_key]) + state_dict_[target_name] = lora_weight.cpu() + return state_dict_ + + def add_lora_to_unet(self, unet: SDUNet, state_dict_lora, alpha=1.0, device="cuda"): + state_dict_unet = unet.state_dict() + state_dict_lora = self.convert_state_dict(state_dict_lora, lora_prefix="lora_unet_", alpha=alpha, device=device) + state_dict_lora = SDUNetStateDictConverter().from_diffusers(state_dict_lora) + if len(state_dict_lora) > 0: + for name in state_dict_lora: + state_dict_unet[name] += state_dict_lora[name].to(device=device) + unet.load_state_dict(state_dict_unet) + + def add_lora_to_text_encoder(self, text_encoder: SDTextEncoder, state_dict_lora, alpha=1.0, device="cuda"): + state_dict_text_encoder = text_encoder.state_dict() + state_dict_lora = self.convert_state_dict(state_dict_lora, lora_prefix="lora_te_", alpha=alpha, device=device) + state_dict_lora = SDTextEncoderStateDictConverter().from_diffusers(state_dict_lora) + if len(state_dict_lora) > 0: + for name in state_dict_lora: + state_dict_text_encoder[name] += state_dict_lora[name].to(device=device) + text_encoder.load_state_dict(state_dict_text_encoder) + diff --git a/diffsynth/models/sd_motion.py b/diffsynth/models/sd_motion.py new file mode 100644 index 0000000000000000000000000000000000000000..b313e62c8c6382ffbe5d30a2601583191209f847 --- /dev/null +++ b/diffsynth/models/sd_motion.py @@ -0,0 +1,198 @@ +from .sd_unet import SDUNet, Attention, GEGLU +import torch +from einops import rearrange, repeat + + +class TemporalTransformerBlock(torch.nn.Module): + + def __init__(self, dim, num_attention_heads, attention_head_dim, max_position_embeddings=32): + super().__init__() + + # 1. Self-Attn + self.pe1 = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, dim)) + self.norm1 = torch.nn.LayerNorm(dim, elementwise_affine=True) + self.attn1 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True) + + # 2. Cross-Attn + self.pe2 = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, dim)) + self.norm2 = torch.nn.LayerNorm(dim, elementwise_affine=True) + self.attn2 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True) + + # 3. Feed-forward + self.norm3 = torch.nn.LayerNorm(dim, elementwise_affine=True) + self.act_fn = GEGLU(dim, dim * 4) + self.ff = torch.nn.Linear(dim * 4, dim) + + + def forward(self, hidden_states, batch_size=1): + + # 1. Self-Attention + norm_hidden_states = self.norm1(hidden_states) + norm_hidden_states = rearrange(norm_hidden_states, "(b f) h c -> (b h) f c", b=batch_size) + attn_output = self.attn1(norm_hidden_states + self.pe1[:, :norm_hidden_states.shape[1]]) + attn_output = rearrange(attn_output, "(b h) f c -> (b f) h c", b=batch_size) + hidden_states = attn_output + hidden_states + + # 2. Cross-Attention + norm_hidden_states = self.norm2(hidden_states) + norm_hidden_states = rearrange(norm_hidden_states, "(b f) h c -> (b h) f c", b=batch_size) + attn_output = self.attn2(norm_hidden_states + self.pe2[:, :norm_hidden_states.shape[1]]) + attn_output = rearrange(attn_output, "(b h) f c -> (b f) h c", b=batch_size) + hidden_states = attn_output + hidden_states + + # 3. Feed-forward + norm_hidden_states = self.norm3(hidden_states) + ff_output = self.act_fn(norm_hidden_states) + ff_output = self.ff(ff_output) + hidden_states = ff_output + hidden_states + + return hidden_states + + +class TemporalBlock(torch.nn.Module): + + def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5): + super().__init__() + inner_dim = num_attention_heads * attention_head_dim + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True) + self.proj_in = torch.nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = torch.nn.ModuleList([ + TemporalTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim + ) + for d in range(num_layers) + ]) + + self.proj_out = torch.nn.Linear(inner_dim, in_channels) + + def forward(self, hidden_states, time_emb, text_emb, res_stack, batch_size=1): + batch, _, height, width = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + hidden_states = self.proj_in(hidden_states) + + for block in self.transformer_blocks: + hidden_states = block( + hidden_states, + batch_size=batch_size + ) + + hidden_states = self.proj_out(hidden_states) + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + hidden_states = hidden_states + residual + + return hidden_states, time_emb, text_emb, res_stack + + +class SDMotionModel(torch.nn.Module): + def __init__(self): + super().__init__() + self.motion_modules = torch.nn.ModuleList([ + TemporalBlock(8, 40, 320, eps=1e-6), + TemporalBlock(8, 40, 320, eps=1e-6), + TemporalBlock(8, 80, 640, eps=1e-6), + TemporalBlock(8, 80, 640, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 160, 1280, eps=1e-6), + TemporalBlock(8, 80, 640, eps=1e-6), + TemporalBlock(8, 80, 640, eps=1e-6), + TemporalBlock(8, 80, 640, eps=1e-6), + TemporalBlock(8, 40, 320, eps=1e-6), + TemporalBlock(8, 40, 320, eps=1e-6), + TemporalBlock(8, 40, 320, eps=1e-6), + ]) + self.call_block_id = { + 1: 0, + 4: 1, + 9: 2, + 12: 3, + 17: 4, + 20: 5, + 24: 6, + 26: 7, + 29: 8, + 32: 9, + 34: 10, + 36: 11, + 40: 12, + 43: 13, + 46: 14, + 50: 15, + 53: 16, + 56: 17, + 60: 18, + 63: 19, + 66: 20 + } + + def forward(self): + pass + + def state_dict_converter(self): + return SDMotionModelStateDictConverter() + + +class SDMotionModelStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + rename_dict = { + "norm": "norm", + "proj_in": "proj_in", + "transformer_blocks.0.attention_blocks.0.to_q": "transformer_blocks.0.attn1.to_q", + "transformer_blocks.0.attention_blocks.0.to_k": "transformer_blocks.0.attn1.to_k", + "transformer_blocks.0.attention_blocks.0.to_v": "transformer_blocks.0.attn1.to_v", + "transformer_blocks.0.attention_blocks.0.to_out.0": "transformer_blocks.0.attn1.to_out", + "transformer_blocks.0.attention_blocks.0.pos_encoder": "transformer_blocks.0.pe1", + "transformer_blocks.0.attention_blocks.1.to_q": "transformer_blocks.0.attn2.to_q", + "transformer_blocks.0.attention_blocks.1.to_k": "transformer_blocks.0.attn2.to_k", + "transformer_blocks.0.attention_blocks.1.to_v": "transformer_blocks.0.attn2.to_v", + "transformer_blocks.0.attention_blocks.1.to_out.0": "transformer_blocks.0.attn2.to_out", + "transformer_blocks.0.attention_blocks.1.pos_encoder": "transformer_blocks.0.pe2", + "transformer_blocks.0.norms.0": "transformer_blocks.0.norm1", + "transformer_blocks.0.norms.1": "transformer_blocks.0.norm2", + "transformer_blocks.0.ff.net.0.proj": "transformer_blocks.0.act_fn.proj", + "transformer_blocks.0.ff.net.2": "transformer_blocks.0.ff", + "transformer_blocks.0.ff_norm": "transformer_blocks.0.norm3", + "proj_out": "proj_out", + } + name_list = sorted([i for i in state_dict if i.startswith("down_blocks.")]) + name_list += sorted([i for i in state_dict if i.startswith("mid_block.")]) + name_list += sorted([i for i in state_dict if i.startswith("up_blocks.")]) + state_dict_ = {} + last_prefix, module_id = "", -1 + for name in name_list: + names = name.split(".") + prefix_index = names.index("temporal_transformer") + 1 + prefix = ".".join(names[:prefix_index]) + if prefix != last_prefix: + last_prefix = prefix + module_id += 1 + middle_name = ".".join(names[prefix_index:-1]) + suffix = names[-1] + if "pos_encoder" in names: + rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name]]) + else: + rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name], suffix]) + state_dict_[rename] = state_dict[name] + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) diff --git a/diffsynth/models/sd_text_encoder.py b/diffsynth/models/sd_text_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..c58e47710a5661d08920169f1d9849d39d45e73e --- /dev/null +++ b/diffsynth/models/sd_text_encoder.py @@ -0,0 +1,320 @@ +import torch +from .attention import Attention + + +class CLIPEncoderLayer(torch.nn.Module): + def __init__(self, embed_dim, intermediate_size, num_heads=12, head_dim=64, use_quick_gelu=True): + super().__init__() + self.attn = Attention(q_dim=embed_dim, num_heads=num_heads, head_dim=head_dim, bias_q=True, bias_kv=True, bias_out=True) + self.layer_norm1 = torch.nn.LayerNorm(embed_dim) + self.layer_norm2 = torch.nn.LayerNorm(embed_dim) + self.fc1 = torch.nn.Linear(embed_dim, intermediate_size) + self.fc2 = torch.nn.Linear(intermediate_size, embed_dim) + + self.use_quick_gelu = use_quick_gelu + + def quickGELU(self, x): + return x * torch.sigmoid(1.702 * x) + + def forward(self, hidden_states, attn_mask=None): + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states = self.attn(hidden_states, attn_mask=attn_mask) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.fc1(hidden_states) + if self.use_quick_gelu: + hidden_states = self.quickGELU(hidden_states) + else: + hidden_states = torch.nn.functional.gelu(hidden_states) + hidden_states = self.fc2(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +class SDTextEncoder(torch.nn.Module): + def __init__(self, embed_dim=768, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=12, encoder_intermediate_size=3072): + super().__init__() + + # token_embedding + self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim) + + # position_embeds (This is a fixed tensor) + self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim)) + + # encoders + self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)]) + + # attn_mask + self.attn_mask = self.attention_mask(max_position_embeddings) + + # final_layer_norm + self.final_layer_norm = torch.nn.LayerNorm(embed_dim) + + def attention_mask(self, length): + mask = torch.empty(length, length) + mask.fill_(float("-inf")) + mask.triu_(1) + return mask + + def forward(self, input_ids, clip_skip=1): + embeds = self.token_embedding(input_ids) + self.position_embeds + attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype) + for encoder_id, encoder in enumerate(self.encoders): + embeds = encoder(embeds, attn_mask=attn_mask) + if encoder_id + clip_skip == len(self.encoders): + break + embeds = self.final_layer_norm(embeds) + return embeds + + def state_dict_converter(self): + return SDTextEncoderStateDictConverter() + + +class SDTextEncoderStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + rename_dict = { + "text_model.embeddings.token_embedding.weight": "token_embedding.weight", + "text_model.embeddings.position_embedding.weight": "position_embeds", + "text_model.final_layer_norm.weight": "final_layer_norm.weight", + "text_model.final_layer_norm.bias": "final_layer_norm.bias" + } + attn_rename_dict = { + "self_attn.q_proj": "attn.to_q", + "self_attn.k_proj": "attn.to_k", + "self_attn.v_proj": "attn.to_v", + "self_attn.out_proj": "attn.to_out", + "layer_norm1": "layer_norm1", + "layer_norm2": "layer_norm2", + "mlp.fc1": "fc1", + "mlp.fc2": "fc2", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict[name]] = param + elif name.startswith("text_model.encoder.layers."): + param = state_dict[name] + names = name.split(".") + layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1] + name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail]) + state_dict_[name_] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight", + "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias", + "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight", + "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias", + 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"cond_stage_model.transformer.text_model.encoder.layers.9.layer_norm1.weight": "encoders.9.layer_norm1.weight", + "cond_stage_model.transformer.text_model.encoder.layers.9.layer_norm2.bias": "encoders.9.layer_norm2.bias", + "cond_stage_model.transformer.text_model.encoder.layers.9.layer_norm2.weight": "encoders.9.layer_norm2.weight", + "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc1.bias": "encoders.9.fc1.bias", + "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc1.weight": "encoders.9.fc1.weight", + "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc2.bias": "encoders.9.fc2.bias", + "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc2.weight": "encoders.9.fc2.weight", + "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.k_proj.bias": "encoders.9.attn.to_k.bias", + "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.k_proj.weight": "encoders.9.attn.to_k.weight", + "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.out_proj.bias": "encoders.9.attn.to_out.bias", + "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.out_proj.weight": "encoders.9.attn.to_out.weight", + "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.q_proj.bias": "encoders.9.attn.to_q.bias", + "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight", + "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias", + "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight", + "cond_stage_model.transformer.text_model.final_layer_norm.bias": "final_layer_norm.bias", + "cond_stage_model.transformer.text_model.final_layer_norm.weight": "final_layer_norm.weight", + "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight": "position_embeds" + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict[name]] = param + return state_dict_ diff --git a/diffsynth/models/sd_unet.py b/diffsynth/models/sd_unet.py new file mode 100644 index 0000000000000000000000000000000000000000..8a8b17e76451de27cf2933f5cdd2cfd82eb87977 --- /dev/null +++ b/diffsynth/models/sd_unet.py @@ -0,0 +1,1107 @@ +import torch, math +from .attention import Attention +from .tiler import TileWorker + + +class Timesteps(torch.nn.Module): + def __init__(self, num_channels): + super().__init__() + self.num_channels = num_channels + + def forward(self, timesteps): + half_dim = self.num_channels // 2 + exponent = -math.log(10000) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device) / half_dim + timesteps = timesteps.unsqueeze(-1) + emb = timesteps.float() * torch.exp(exponent) + emb = torch.cat([torch.cos(emb), torch.sin(emb)], dim=-1) + return emb + + +class GEGLU(torch.nn.Module): + + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = torch.nn.Linear(dim_in, dim_out * 2) + + def forward(self, hidden_states): + hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1) + return hidden_states * torch.nn.functional.gelu(gate) + + +class BasicTransformerBlock(torch.nn.Module): + + def __init__(self, dim, num_attention_heads, attention_head_dim, cross_attention_dim): + super().__init__() + + # 1. Self-Attn + self.norm1 = torch.nn.LayerNorm(dim, elementwise_affine=True) + self.attn1 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True) + + # 2. Cross-Attn + self.norm2 = torch.nn.LayerNorm(dim, elementwise_affine=True) + self.attn2 = Attention(q_dim=dim, kv_dim=cross_attention_dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True) + + # 3. Feed-forward + self.norm3 = torch.nn.LayerNorm(dim, elementwise_affine=True) + self.act_fn = GEGLU(dim, dim * 4) + self.ff = torch.nn.Linear(dim * 4, dim) + + + def forward(self, hidden_states, encoder_hidden_states, ipadapter_kwargs=None): + # 1. Self-Attention + norm_hidden_states = self.norm1(hidden_states) + attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None) + hidden_states = attn_output + hidden_states + + # 2. Cross-Attention + norm_hidden_states = self.norm2(hidden_states) + attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states, ipadapter_kwargs=ipadapter_kwargs) + hidden_states = attn_output + hidden_states + + # 3. Feed-forward + norm_hidden_states = self.norm3(hidden_states) + ff_output = self.act_fn(norm_hidden_states) + ff_output = self.ff(ff_output) + hidden_states = ff_output + hidden_states + + return hidden_states + + +class DownSampler(torch.nn.Module): + def __init__(self, channels, padding=1, extra_padding=False): + super().__init__() + self.conv = torch.nn.Conv2d(channels, channels, 3, stride=2, padding=padding) + self.extra_padding = extra_padding + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + if self.extra_padding: + hidden_states = torch.nn.functional.pad(hidden_states, (0, 1, 0, 1), mode="constant", value=0) + hidden_states = self.conv(hidden_states) + return hidden_states, time_emb, text_emb, res_stack + + +class UpSampler(torch.nn.Module): + def __init__(self, channels): + super().__init__() + self.conv = torch.nn.Conv2d(channels, channels, 3, padding=1) + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + hidden_states = torch.nn.functional.interpolate(hidden_states, scale_factor=2.0, mode="nearest") + hidden_states = self.conv(hidden_states) + return hidden_states, time_emb, text_emb, res_stack + + +class ResnetBlock(torch.nn.Module): + def __init__(self, in_channels, out_channels, temb_channels=None, groups=32, eps=1e-5): + super().__init__() + self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) + self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + if temb_channels is not None: + self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels) + self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True) + self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + self.nonlinearity = torch.nn.SiLU() + self.conv_shortcut = None + if in_channels != out_channels: + self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=True) + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + x = hidden_states + x = self.norm1(x) + x = self.nonlinearity(x) + x = self.conv1(x) + if time_emb is not None: + emb = self.nonlinearity(time_emb) + emb = self.time_emb_proj(emb)[:, :, None, None] + x = x + emb + x = self.norm2(x) + x = self.nonlinearity(x) + x = self.conv2(x) + if self.conv_shortcut is not None: + hidden_states = self.conv_shortcut(hidden_states) + hidden_states = hidden_states + x + return hidden_states, time_emb, text_emb, res_stack + + +class AttentionBlock(torch.nn.Module): + + def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, cross_attention_dim=None, norm_num_groups=32, eps=1e-5, need_proj_out=True): + super().__init__() + inner_dim = num_attention_heads * attention_head_dim + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True) + self.proj_in = torch.nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = torch.nn.ModuleList([ + BasicTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + cross_attention_dim=cross_attention_dim + ) + for d in range(num_layers) + ]) + self.need_proj_out = need_proj_out + if need_proj_out: + self.proj_out = torch.nn.Linear(inner_dim, in_channels) + + def forward( + self, + hidden_states, time_emb, text_emb, res_stack, + cross_frame_attention=False, + tiled=False, tile_size=64, tile_stride=32, + ipadapter_kwargs_list={}, + **kwargs + ): + batch, _, height, width = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + hidden_states = self.proj_in(hidden_states) + + if cross_frame_attention: + hidden_states = hidden_states.reshape(1, batch * height * width, inner_dim) + encoder_hidden_states = text_emb.mean(dim=0, keepdim=True) + else: + encoder_hidden_states = text_emb + if encoder_hidden_states.shape[0] != hidden_states.shape[0]: + encoder_hidden_states = encoder_hidden_states.repeat(hidden_states.shape[0], 1, 1) + + if tiled: + tile_size = min(tile_size, min(height, width)) + hidden_states = hidden_states.permute(0, 2, 1).reshape(batch, inner_dim, height, width) + def block_tile_forward(x): + b, c, h, w = x.shape + x = x.permute(0, 2, 3, 1).reshape(b, h*w, c) + x = block(x, encoder_hidden_states) + x = x.reshape(b, h, w, c).permute(0, 3, 1, 2) + return x + for block in self.transformer_blocks: + hidden_states = TileWorker().tiled_forward( + block_tile_forward, + hidden_states, + tile_size, + tile_stride, + tile_device=hidden_states.device, + tile_dtype=hidden_states.dtype + ) + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + else: + for block_id, block in enumerate(self.transformer_blocks): + hidden_states = block( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + ipadapter_kwargs=ipadapter_kwargs_list.get(block_id, None) + ) + if cross_frame_attention: + hidden_states = hidden_states.reshape(batch, height * width, inner_dim) + + if self.need_proj_out: + hidden_states = self.proj_out(hidden_states) + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + hidden_states = hidden_states + residual + else: + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + + return hidden_states, time_emb, text_emb, res_stack + + +class PushBlock(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + res_stack.append(hidden_states) + return hidden_states, time_emb, text_emb, res_stack + + +class PopBlock(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + res_hidden_states = res_stack.pop() + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + return hidden_states, time_emb, text_emb, res_stack + + +class SDUNet(torch.nn.Module): + def __init__(self): + super().__init__() + self.time_proj = Timesteps(320) + self.time_embedding = torch.nn.Sequential( + torch.nn.Linear(320, 1280), + torch.nn.SiLU(), + torch.nn.Linear(1280, 1280) + ) + self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1) + + self.blocks = torch.nn.ModuleList([ + # CrossAttnDownBlock2D + ResnetBlock(320, 320, 1280), + AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), + PushBlock(), + ResnetBlock(320, 320, 1280), + AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), + PushBlock(), + DownSampler(320), + PushBlock(), + # CrossAttnDownBlock2D + ResnetBlock(320, 640, 1280), + AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), + PushBlock(), + ResnetBlock(640, 640, 1280), + AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), + PushBlock(), + DownSampler(640), + PushBlock(), + # CrossAttnDownBlock2D + ResnetBlock(640, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), + PushBlock(), + ResnetBlock(1280, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), + PushBlock(), + DownSampler(1280), + PushBlock(), + # DownBlock2D + ResnetBlock(1280, 1280, 1280), + PushBlock(), + ResnetBlock(1280, 1280, 1280), + PushBlock(), + # UNetMidBlock2DCrossAttn + ResnetBlock(1280, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), + ResnetBlock(1280, 1280, 1280), + # UpBlock2D + PopBlock(), + ResnetBlock(2560, 1280, 1280), + PopBlock(), + ResnetBlock(2560, 1280, 1280), + PopBlock(), + ResnetBlock(2560, 1280, 1280), + UpSampler(1280), + # CrossAttnUpBlock2D + PopBlock(), + ResnetBlock(2560, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), + PopBlock(), + ResnetBlock(2560, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), + PopBlock(), + ResnetBlock(1920, 1280, 1280), + AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), + UpSampler(1280), + # CrossAttnUpBlock2D + PopBlock(), + ResnetBlock(1920, 640, 1280), + AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), + PopBlock(), + ResnetBlock(1280, 640, 1280), + AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), + PopBlock(), + ResnetBlock(960, 640, 1280), + AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), + UpSampler(640), + # CrossAttnUpBlock2D + PopBlock(), + ResnetBlock(960, 320, 1280), + AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), + PopBlock(), + ResnetBlock(640, 320, 1280), + AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), + PopBlock(), + ResnetBlock(640, 320, 1280), + AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), + ]) + + self.conv_norm_out = torch.nn.GroupNorm(num_channels=320, num_groups=32, eps=1e-5) + self.conv_act = torch.nn.SiLU() + self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1) + + def forward(self, sample, timestep, encoder_hidden_states, **kwargs): + # 1. time + time_emb = self.time_proj(timestep[None]).to(sample.dtype) + time_emb = self.time_embedding(time_emb) + + # 2. pre-process + hidden_states = self.conv_in(sample) + text_emb = encoder_hidden_states + res_stack = [hidden_states] + + # 3. blocks + for i, block in enumerate(self.blocks): + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + + # 4. output + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + + return hidden_states + + def state_dict_converter(self): + return SDUNetStateDictConverter() + + +class SDUNetStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + # architecture + block_types = [ + 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', + 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'ResnetBlock', + 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'UpSampler', + 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', + 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', + 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock' + ] + + # Rename each parameter + name_list = sorted([name for name in state_dict]) + rename_dict = {} + block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1} + last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""} + for name in name_list: + names = name.split(".") + if names[0] in ["conv_in", "conv_norm_out", "conv_out"]: + pass + elif names[0] in ["time_embedding", "add_embedding"]: + if names[0] == "add_embedding": + names[0] = "add_time_embedding" + names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]] + elif names[0] in ["down_blocks", "mid_block", "up_blocks"]: + if names[0] == "mid_block": + names.insert(1, "0") + block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]] + block_type_with_id = ".".join(names[:4]) + if block_type_with_id != last_block_type_with_id[block_type]: + block_id[block_type] += 1 + last_block_type_with_id[block_type] = block_type_with_id + while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: + block_id[block_type] += 1 + block_type_with_id = ".".join(names[:4]) + names = ["blocks", str(block_id[block_type])] + names[4:] + if "ff" in names: + ff_index = names.index("ff") + component = ".".join(names[ff_index:ff_index+3]) + component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component] + names = names[:ff_index] + [component] + names[ff_index+3:] + if "to_out" in names: + names.pop(names.index("to_out") + 1) + else: + raise ValueError(f"Unknown parameters: {name}") + rename_dict[name] = ".".join(names) + + # Convert state_dict + state_dict_ = {} + for name, param in state_dict.items(): + if ".proj_in." in name or ".proj_out." in name: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias", + "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight", + "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias", + 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"blocks.60.transformer_blocks.0.norm1.weight", + "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm2.bias": "blocks.60.transformer_blocks.0.norm2.bias", + "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm2.weight": "blocks.60.transformer_blocks.0.norm2.weight", + "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.bias": "blocks.60.transformer_blocks.0.norm3.bias", + "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.weight": "blocks.60.transformer_blocks.0.norm3.weight", + "model.diffusion_model.time_embed.0.bias": "time_embedding.0.bias", + "model.diffusion_model.time_embed.0.weight": "time_embedding.0.weight", + "model.diffusion_model.time_embed.2.bias": "time_embedding.2.bias", + "model.diffusion_model.time_embed.2.weight": "time_embedding.2.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if ".proj_in." in name or ".proj_out." in name: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ \ No newline at end of file diff --git a/diffsynth/models/sd_vae_decoder.py b/diffsynth/models/sd_vae_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..0fba92d763a3a6d0b0331b95a5bd7c5ac65135dc --- /dev/null +++ b/diffsynth/models/sd_vae_decoder.py @@ -0,0 +1,332 @@ +import torch +from .attention import Attention +from .sd_unet import ResnetBlock, UpSampler +from .tiler import TileWorker + + +class VAEAttentionBlock(torch.nn.Module): + + def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5): + super().__init__() + inner_dim = num_attention_heads * attention_head_dim + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True) + + self.transformer_blocks = torch.nn.ModuleList([ + Attention( + inner_dim, + num_attention_heads, + attention_head_dim, + bias_q=True, + bias_kv=True, + bias_out=True + ) + for d in range(num_layers) + ]) + + def forward(self, hidden_states, time_emb, text_emb, res_stack): + batch, _, height, width = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + + for block in self.transformer_blocks: + hidden_states = block(hidden_states) + + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + hidden_states = hidden_states + residual + + return hidden_states, time_emb, text_emb, res_stack + + +class SDVAEDecoder(torch.nn.Module): + def __init__(self): + super().__init__() + self.scaling_factor = 0.18215 + self.post_quant_conv = torch.nn.Conv2d(4, 4, kernel_size=1) + self.conv_in = torch.nn.Conv2d(4, 512, kernel_size=3, padding=1) + + self.blocks = torch.nn.ModuleList([ + # UNetMidBlock2D + ResnetBlock(512, 512, eps=1e-6), + VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + # UpDecoderBlock2D + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + UpSampler(512), + # UpDecoderBlock2D + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + UpSampler(512), + # UpDecoderBlock2D + ResnetBlock(512, 256, eps=1e-6), + ResnetBlock(256, 256, eps=1e-6), + ResnetBlock(256, 256, eps=1e-6), + UpSampler(256), + # UpDecoderBlock2D + ResnetBlock(256, 128, eps=1e-6), + ResnetBlock(128, 128, eps=1e-6), + ResnetBlock(128, 128, eps=1e-6), + ]) + + self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-5) + self.conv_act = torch.nn.SiLU() + self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1) + + def tiled_forward(self, sample, tile_size=64, tile_stride=32): + hidden_states = TileWorker().tiled_forward( + lambda x: self.forward(x), + sample, + tile_size, + tile_stride, + tile_device=sample.device, + tile_dtype=sample.dtype + ) + return hidden_states + + def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs): + # For VAE Decoder, we do not need to apply the tiler on each layer. + if tiled: + return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride) + + # 1. pre-process + sample = sample / self.scaling_factor + hidden_states = self.post_quant_conv(sample) + hidden_states = self.conv_in(hidden_states) + time_emb = None + text_emb = None + res_stack = None + + # 2. blocks + for i, block in enumerate(self.blocks): + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + + # 3. output + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + + return hidden_states + + def state_dict_converter(self): + return SDVAEDecoderStateDictConverter() + + +class SDVAEDecoderStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + # architecture + block_types = [ + 'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock', + 'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler', + 'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler', + 'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler', + 'ResnetBlock', 'ResnetBlock', 'ResnetBlock' + ] + + # Rename each parameter + local_rename_dict = { + "post_quant_conv": "post_quant_conv", + "decoder.conv_in": "conv_in", + "decoder.mid_block.attentions.0.group_norm": "blocks.1.norm", + "decoder.mid_block.attentions.0.to_q": "blocks.1.transformer_blocks.0.to_q", + "decoder.mid_block.attentions.0.to_k": "blocks.1.transformer_blocks.0.to_k", + "decoder.mid_block.attentions.0.to_v": "blocks.1.transformer_blocks.0.to_v", + "decoder.mid_block.attentions.0.to_out.0": "blocks.1.transformer_blocks.0.to_out", + "decoder.mid_block.resnets.0.norm1": "blocks.0.norm1", + "decoder.mid_block.resnets.0.conv1": "blocks.0.conv1", + "decoder.mid_block.resnets.0.norm2": "blocks.0.norm2", + "decoder.mid_block.resnets.0.conv2": "blocks.0.conv2", + "decoder.mid_block.resnets.1.norm1": "blocks.2.norm1", + "decoder.mid_block.resnets.1.conv1": "blocks.2.conv1", + "decoder.mid_block.resnets.1.norm2": "blocks.2.norm2", + "decoder.mid_block.resnets.1.conv2": "blocks.2.conv2", + "decoder.conv_norm_out": "conv_norm_out", + "decoder.conv_out": "conv_out", + } + name_list = sorted([name for name in state_dict]) + rename_dict = {} + block_id = {"ResnetBlock": 2, "DownSampler": 2, "UpSampler": 2} + last_block_type_with_id = {"ResnetBlock": "", "DownSampler": "", "UpSampler": ""} + for name in name_list: + names = name.split(".") + name_prefix = ".".join(names[:-1]) + if name_prefix in local_rename_dict: + rename_dict[name] = local_rename_dict[name_prefix] + "." + names[-1] + elif name.startswith("decoder.up_blocks"): + block_type = {"resnets": "ResnetBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[3]] + block_type_with_id = ".".join(names[:5]) + if block_type_with_id != last_block_type_with_id[block_type]: + block_id[block_type] += 1 + last_block_type_with_id[block_type] = block_type_with_id + while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: + block_id[block_type] += 1 + block_type_with_id = ".".join(names[:5]) + names = ["blocks", str(block_id[block_type])] + names[5:] + rename_dict[name] = ".".join(names) + + # Convert state_dict + state_dict_ = {} + for name, param in state_dict.items(): + if name in rename_dict: + state_dict_[rename_dict[name]] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "first_stage_model.decoder.conv_in.bias": "conv_in.bias", + "first_stage_model.decoder.conv_in.weight": "conv_in.weight", + "first_stage_model.decoder.conv_out.bias": "conv_out.bias", + "first_stage_model.decoder.conv_out.weight": "conv_out.weight", + "first_stage_model.decoder.mid.attn_1.k.bias": "blocks.1.transformer_blocks.0.to_k.bias", + "first_stage_model.decoder.mid.attn_1.k.weight": "blocks.1.transformer_blocks.0.to_k.weight", + "first_stage_model.decoder.mid.attn_1.norm.bias": "blocks.1.norm.bias", + "first_stage_model.decoder.mid.attn_1.norm.weight": "blocks.1.norm.weight", + "first_stage_model.decoder.mid.attn_1.proj_out.bias": "blocks.1.transformer_blocks.0.to_out.bias", + "first_stage_model.decoder.mid.attn_1.proj_out.weight": "blocks.1.transformer_blocks.0.to_out.weight", + "first_stage_model.decoder.mid.attn_1.q.bias": "blocks.1.transformer_blocks.0.to_q.bias", + "first_stage_model.decoder.mid.attn_1.q.weight": "blocks.1.transformer_blocks.0.to_q.weight", + "first_stage_model.decoder.mid.attn_1.v.bias": "blocks.1.transformer_blocks.0.to_v.bias", + "first_stage_model.decoder.mid.attn_1.v.weight": "blocks.1.transformer_blocks.0.to_v.weight", + "first_stage_model.decoder.mid.block_1.conv1.bias": "blocks.0.conv1.bias", + "first_stage_model.decoder.mid.block_1.conv1.weight": "blocks.0.conv1.weight", + "first_stage_model.decoder.mid.block_1.conv2.bias": "blocks.0.conv2.bias", + "first_stage_model.decoder.mid.block_1.conv2.weight": "blocks.0.conv2.weight", + "first_stage_model.decoder.mid.block_1.norm1.bias": "blocks.0.norm1.bias", + "first_stage_model.decoder.mid.block_1.norm1.weight": "blocks.0.norm1.weight", + "first_stage_model.decoder.mid.block_1.norm2.bias": "blocks.0.norm2.bias", + "first_stage_model.decoder.mid.block_1.norm2.weight": "blocks.0.norm2.weight", + "first_stage_model.decoder.mid.block_2.conv1.bias": "blocks.2.conv1.bias", + "first_stage_model.decoder.mid.block_2.conv1.weight": "blocks.2.conv1.weight", + "first_stage_model.decoder.mid.block_2.conv2.bias": "blocks.2.conv2.bias", + "first_stage_model.decoder.mid.block_2.conv2.weight": "blocks.2.conv2.weight", + "first_stage_model.decoder.mid.block_2.norm1.bias": "blocks.2.norm1.bias", + "first_stage_model.decoder.mid.block_2.norm1.weight": "blocks.2.norm1.weight", + "first_stage_model.decoder.mid.block_2.norm2.bias": "blocks.2.norm2.bias", + "first_stage_model.decoder.mid.block_2.norm2.weight": "blocks.2.norm2.weight", + "first_stage_model.decoder.norm_out.bias": "conv_norm_out.bias", + "first_stage_model.decoder.norm_out.weight": "conv_norm_out.weight", + "first_stage_model.decoder.up.0.block.0.conv1.bias": "blocks.15.conv1.bias", + "first_stage_model.decoder.up.0.block.0.conv1.weight": "blocks.15.conv1.weight", + "first_stage_model.decoder.up.0.block.0.conv2.bias": "blocks.15.conv2.bias", + "first_stage_model.decoder.up.0.block.0.conv2.weight": "blocks.15.conv2.weight", + "first_stage_model.decoder.up.0.block.0.nin_shortcut.bias": "blocks.15.conv_shortcut.bias", + "first_stage_model.decoder.up.0.block.0.nin_shortcut.weight": "blocks.15.conv_shortcut.weight", + "first_stage_model.decoder.up.0.block.0.norm1.bias": "blocks.15.norm1.bias", + "first_stage_model.decoder.up.0.block.0.norm1.weight": "blocks.15.norm1.weight", + "first_stage_model.decoder.up.0.block.0.norm2.bias": "blocks.15.norm2.bias", + "first_stage_model.decoder.up.0.block.0.norm2.weight": "blocks.15.norm2.weight", + "first_stage_model.decoder.up.0.block.1.conv1.bias": "blocks.16.conv1.bias", + "first_stage_model.decoder.up.0.block.1.conv1.weight": "blocks.16.conv1.weight", + "first_stage_model.decoder.up.0.block.1.conv2.bias": "blocks.16.conv2.bias", + "first_stage_model.decoder.up.0.block.1.conv2.weight": "blocks.16.conv2.weight", + "first_stage_model.decoder.up.0.block.1.norm1.bias": "blocks.16.norm1.bias", + "first_stage_model.decoder.up.0.block.1.norm1.weight": "blocks.16.norm1.weight", + "first_stage_model.decoder.up.0.block.1.norm2.bias": "blocks.16.norm2.bias", + "first_stage_model.decoder.up.0.block.1.norm2.weight": "blocks.16.norm2.weight", + "first_stage_model.decoder.up.0.block.2.conv1.bias": "blocks.17.conv1.bias", + "first_stage_model.decoder.up.0.block.2.conv1.weight": "blocks.17.conv1.weight", + "first_stage_model.decoder.up.0.block.2.conv2.bias": "blocks.17.conv2.bias", + "first_stage_model.decoder.up.0.block.2.conv2.weight": "blocks.17.conv2.weight", + "first_stage_model.decoder.up.0.block.2.norm1.bias": "blocks.17.norm1.bias", + "first_stage_model.decoder.up.0.block.2.norm1.weight": "blocks.17.norm1.weight", + "first_stage_model.decoder.up.0.block.2.norm2.bias": "blocks.17.norm2.bias", + "first_stage_model.decoder.up.0.block.2.norm2.weight": "blocks.17.norm2.weight", + "first_stage_model.decoder.up.1.block.0.conv1.bias": "blocks.11.conv1.bias", + "first_stage_model.decoder.up.1.block.0.conv1.weight": "blocks.11.conv1.weight", + "first_stage_model.decoder.up.1.block.0.conv2.bias": "blocks.11.conv2.bias", + "first_stage_model.decoder.up.1.block.0.conv2.weight": "blocks.11.conv2.weight", + "first_stage_model.decoder.up.1.block.0.nin_shortcut.bias": "blocks.11.conv_shortcut.bias", + "first_stage_model.decoder.up.1.block.0.nin_shortcut.weight": "blocks.11.conv_shortcut.weight", + "first_stage_model.decoder.up.1.block.0.norm1.bias": "blocks.11.norm1.bias", + "first_stage_model.decoder.up.1.block.0.norm1.weight": "blocks.11.norm1.weight", + "first_stage_model.decoder.up.1.block.0.norm2.bias": "blocks.11.norm2.bias", + "first_stage_model.decoder.up.1.block.0.norm2.weight": "blocks.11.norm2.weight", + "first_stage_model.decoder.up.1.block.1.conv1.bias": "blocks.12.conv1.bias", + "first_stage_model.decoder.up.1.block.1.conv1.weight": "blocks.12.conv1.weight", + "first_stage_model.decoder.up.1.block.1.conv2.bias": "blocks.12.conv2.bias", + "first_stage_model.decoder.up.1.block.1.conv2.weight": "blocks.12.conv2.weight", + "first_stage_model.decoder.up.1.block.1.norm1.bias": "blocks.12.norm1.bias", + "first_stage_model.decoder.up.1.block.1.norm1.weight": "blocks.12.norm1.weight", + "first_stage_model.decoder.up.1.block.1.norm2.bias": "blocks.12.norm2.bias", + "first_stage_model.decoder.up.1.block.1.norm2.weight": "blocks.12.norm2.weight", + "first_stage_model.decoder.up.1.block.2.conv1.bias": "blocks.13.conv1.bias", + "first_stage_model.decoder.up.1.block.2.conv1.weight": "blocks.13.conv1.weight", + "first_stage_model.decoder.up.1.block.2.conv2.bias": "blocks.13.conv2.bias", + "first_stage_model.decoder.up.1.block.2.conv2.weight": "blocks.13.conv2.weight", + "first_stage_model.decoder.up.1.block.2.norm1.bias": "blocks.13.norm1.bias", + "first_stage_model.decoder.up.1.block.2.norm1.weight": "blocks.13.norm1.weight", + "first_stage_model.decoder.up.1.block.2.norm2.bias": "blocks.13.norm2.bias", + "first_stage_model.decoder.up.1.block.2.norm2.weight": "blocks.13.norm2.weight", + "first_stage_model.decoder.up.1.upsample.conv.bias": "blocks.14.conv.bias", + "first_stage_model.decoder.up.1.upsample.conv.weight": "blocks.14.conv.weight", + "first_stage_model.decoder.up.2.block.0.conv1.bias": "blocks.7.conv1.bias", + "first_stage_model.decoder.up.2.block.0.conv1.weight": "blocks.7.conv1.weight", + "first_stage_model.decoder.up.2.block.0.conv2.bias": "blocks.7.conv2.bias", + "first_stage_model.decoder.up.2.block.0.conv2.weight": "blocks.7.conv2.weight", + "first_stage_model.decoder.up.2.block.0.norm1.bias": "blocks.7.norm1.bias", + "first_stage_model.decoder.up.2.block.0.norm1.weight": "blocks.7.norm1.weight", + "first_stage_model.decoder.up.2.block.0.norm2.bias": "blocks.7.norm2.bias", + "first_stage_model.decoder.up.2.block.0.norm2.weight": "blocks.7.norm2.weight", + "first_stage_model.decoder.up.2.block.1.conv1.bias": "blocks.8.conv1.bias", + "first_stage_model.decoder.up.2.block.1.conv1.weight": "blocks.8.conv1.weight", + "first_stage_model.decoder.up.2.block.1.conv2.bias": "blocks.8.conv2.bias", + "first_stage_model.decoder.up.2.block.1.conv2.weight": "blocks.8.conv2.weight", + "first_stage_model.decoder.up.2.block.1.norm1.bias": "blocks.8.norm1.bias", + "first_stage_model.decoder.up.2.block.1.norm1.weight": "blocks.8.norm1.weight", + "first_stage_model.decoder.up.2.block.1.norm2.bias": "blocks.8.norm2.bias", + "first_stage_model.decoder.up.2.block.1.norm2.weight": "blocks.8.norm2.weight", + "first_stage_model.decoder.up.2.block.2.conv1.bias": "blocks.9.conv1.bias", + "first_stage_model.decoder.up.2.block.2.conv1.weight": "blocks.9.conv1.weight", + "first_stage_model.decoder.up.2.block.2.conv2.bias": "blocks.9.conv2.bias", + "first_stage_model.decoder.up.2.block.2.conv2.weight": "blocks.9.conv2.weight", + "first_stage_model.decoder.up.2.block.2.norm1.bias": "blocks.9.norm1.bias", + "first_stage_model.decoder.up.2.block.2.norm1.weight": "blocks.9.norm1.weight", + "first_stage_model.decoder.up.2.block.2.norm2.bias": "blocks.9.norm2.bias", + "first_stage_model.decoder.up.2.block.2.norm2.weight": "blocks.9.norm2.weight", + "first_stage_model.decoder.up.2.upsample.conv.bias": "blocks.10.conv.bias", + "first_stage_model.decoder.up.2.upsample.conv.weight": "blocks.10.conv.weight", + "first_stage_model.decoder.up.3.block.0.conv1.bias": "blocks.3.conv1.bias", + "first_stage_model.decoder.up.3.block.0.conv1.weight": "blocks.3.conv1.weight", + "first_stage_model.decoder.up.3.block.0.conv2.bias": "blocks.3.conv2.bias", + "first_stage_model.decoder.up.3.block.0.conv2.weight": "blocks.3.conv2.weight", + "first_stage_model.decoder.up.3.block.0.norm1.bias": "blocks.3.norm1.bias", + "first_stage_model.decoder.up.3.block.0.norm1.weight": "blocks.3.norm1.weight", + "first_stage_model.decoder.up.3.block.0.norm2.bias": "blocks.3.norm2.bias", + "first_stage_model.decoder.up.3.block.0.norm2.weight": "blocks.3.norm2.weight", + "first_stage_model.decoder.up.3.block.1.conv1.bias": "blocks.4.conv1.bias", + "first_stage_model.decoder.up.3.block.1.conv1.weight": "blocks.4.conv1.weight", + "first_stage_model.decoder.up.3.block.1.conv2.bias": "blocks.4.conv2.bias", + "first_stage_model.decoder.up.3.block.1.conv2.weight": "blocks.4.conv2.weight", + "first_stage_model.decoder.up.3.block.1.norm1.bias": "blocks.4.norm1.bias", + "first_stage_model.decoder.up.3.block.1.norm1.weight": "blocks.4.norm1.weight", + "first_stage_model.decoder.up.3.block.1.norm2.bias": "blocks.4.norm2.bias", + "first_stage_model.decoder.up.3.block.1.norm2.weight": "blocks.4.norm2.weight", + "first_stage_model.decoder.up.3.block.2.conv1.bias": "blocks.5.conv1.bias", + "first_stage_model.decoder.up.3.block.2.conv1.weight": "blocks.5.conv1.weight", + "first_stage_model.decoder.up.3.block.2.conv2.bias": "blocks.5.conv2.bias", + "first_stage_model.decoder.up.3.block.2.conv2.weight": "blocks.5.conv2.weight", + "first_stage_model.decoder.up.3.block.2.norm1.bias": "blocks.5.norm1.bias", + "first_stage_model.decoder.up.3.block.2.norm1.weight": "blocks.5.norm1.weight", + "first_stage_model.decoder.up.3.block.2.norm2.bias": "blocks.5.norm2.bias", + "first_stage_model.decoder.up.3.block.2.norm2.weight": "blocks.5.norm2.weight", + "first_stage_model.decoder.up.3.upsample.conv.bias": "blocks.6.conv.bias", + "first_stage_model.decoder.up.3.upsample.conv.weight": "blocks.6.conv.weight", + "first_stage_model.post_quant_conv.bias": "post_quant_conv.bias", + "first_stage_model.post_quant_conv.weight": "post_quant_conv.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if "transformer_blocks" in rename_dict[name]: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ diff --git a/diffsynth/models/sd_vae_encoder.py b/diffsynth/models/sd_vae_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..ea76cc99889ca1d97007ef9fafe654b55ccac48a --- /dev/null +++ b/diffsynth/models/sd_vae_encoder.py @@ -0,0 +1,278 @@ +import torch +from .sd_unet import ResnetBlock, DownSampler +from .sd_vae_decoder import VAEAttentionBlock +from .tiler import TileWorker +from einops import rearrange + + +class SDVAEEncoder(torch.nn.Module): + def __init__(self): + super().__init__() + self.scaling_factor = 0.18215 + self.quant_conv = torch.nn.Conv2d(8, 8, kernel_size=1) + self.conv_in = torch.nn.Conv2d(3, 128, kernel_size=3, padding=1) + + self.blocks = torch.nn.ModuleList([ + # DownEncoderBlock2D + ResnetBlock(128, 128, eps=1e-6), + ResnetBlock(128, 128, eps=1e-6), + DownSampler(128, padding=0, extra_padding=True), + # DownEncoderBlock2D + ResnetBlock(128, 256, eps=1e-6), + ResnetBlock(256, 256, eps=1e-6), + DownSampler(256, padding=0, extra_padding=True), + # DownEncoderBlock2D + ResnetBlock(256, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + DownSampler(512, padding=0, extra_padding=True), + # DownEncoderBlock2D + ResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + # UNetMidBlock2D + ResnetBlock(512, 512, eps=1e-6), + VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + ]) + + self.conv_norm_out = torch.nn.GroupNorm(num_channels=512, num_groups=32, eps=1e-6) + self.conv_act = torch.nn.SiLU() + self.conv_out = torch.nn.Conv2d(512, 8, kernel_size=3, padding=1) + + def tiled_forward(self, sample, tile_size=64, tile_stride=32): + hidden_states = TileWorker().tiled_forward( + lambda x: self.forward(x), + sample, + tile_size, + tile_stride, + tile_device=sample.device, + tile_dtype=sample.dtype + ) + return hidden_states + + def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs): + # For VAE Decoder, we do not need to apply the tiler on each layer. + if tiled: + return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride) + + # 1. pre-process + hidden_states = self.conv_in(sample) + time_emb = None + text_emb = None + res_stack = None + + # 2. blocks + for i, block in enumerate(self.blocks): + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + + # 3. output + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + hidden_states = self.quant_conv(hidden_states) + hidden_states = hidden_states[:, :4] + hidden_states *= self.scaling_factor + + return hidden_states + + def encode_video(self, sample, batch_size=8): + B = sample.shape[0] + hidden_states = [] + + for i in range(0, sample.shape[2], batch_size): + + j = min(i + batch_size, sample.shape[2]) + sample_batch = rearrange(sample[:,:,i:j], "B C T H W -> (B T) C H W") + + hidden_states_batch = self(sample_batch) + hidden_states_batch = rearrange(hidden_states_batch, "(B T) C H W -> B C T H W", B=B) + + hidden_states.append(hidden_states_batch) + + hidden_states = torch.concat(hidden_states, dim=2) + return hidden_states + + def state_dict_converter(self): + return SDVAEEncoderStateDictConverter() + + +class SDVAEEncoderStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + # architecture + block_types = [ + 'ResnetBlock', 'ResnetBlock', 'DownSampler', + 'ResnetBlock', 'ResnetBlock', 'DownSampler', + 'ResnetBlock', 'ResnetBlock', 'DownSampler', + 'ResnetBlock', 'ResnetBlock', + 'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock' + ] + + # Rename each parameter + local_rename_dict = { + "quant_conv": "quant_conv", + "encoder.conv_in": "conv_in", + "encoder.mid_block.attentions.0.group_norm": "blocks.12.norm", + "encoder.mid_block.attentions.0.to_q": "blocks.12.transformer_blocks.0.to_q", + "encoder.mid_block.attentions.0.to_k": "blocks.12.transformer_blocks.0.to_k", + "encoder.mid_block.attentions.0.to_v": "blocks.12.transformer_blocks.0.to_v", + "encoder.mid_block.attentions.0.to_out.0": "blocks.12.transformer_blocks.0.to_out", + "encoder.mid_block.resnets.0.norm1": "blocks.11.norm1", + "encoder.mid_block.resnets.0.conv1": "blocks.11.conv1", + "encoder.mid_block.resnets.0.norm2": "blocks.11.norm2", + "encoder.mid_block.resnets.0.conv2": "blocks.11.conv2", + "encoder.mid_block.resnets.1.norm1": "blocks.13.norm1", + "encoder.mid_block.resnets.1.conv1": "blocks.13.conv1", + "encoder.mid_block.resnets.1.norm2": "blocks.13.norm2", + "encoder.mid_block.resnets.1.conv2": "blocks.13.conv2", + "encoder.conv_norm_out": "conv_norm_out", + "encoder.conv_out": "conv_out", + } + name_list = sorted([name for name in state_dict]) + rename_dict = {} + block_id = {"ResnetBlock": -1, "DownSampler": -1, "UpSampler": -1} + last_block_type_with_id = {"ResnetBlock": "", "DownSampler": "", "UpSampler": ""} + for name in name_list: + names = name.split(".") + name_prefix = ".".join(names[:-1]) + if name_prefix in local_rename_dict: + rename_dict[name] = local_rename_dict[name_prefix] + "." + names[-1] + elif name.startswith("encoder.down_blocks"): + block_type = {"resnets": "ResnetBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[3]] + block_type_with_id = ".".join(names[:5]) + if block_type_with_id != last_block_type_with_id[block_type]: + block_id[block_type] += 1 + last_block_type_with_id[block_type] = block_type_with_id + while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: + block_id[block_type] += 1 + block_type_with_id = ".".join(names[:5]) + names = ["blocks", str(block_id[block_type])] + names[5:] + rename_dict[name] = ".".join(names) + + # Convert state_dict + state_dict_ = {} + for name, param in state_dict.items(): + if name in rename_dict: + state_dict_[rename_dict[name]] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "first_stage_model.encoder.conv_in.bias": "conv_in.bias", + "first_stage_model.encoder.conv_in.weight": "conv_in.weight", + "first_stage_model.encoder.conv_out.bias": "conv_out.bias", + "first_stage_model.encoder.conv_out.weight": "conv_out.weight", + "first_stage_model.encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias", + "first_stage_model.encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight", + "first_stage_model.encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias", + "first_stage_model.encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight", + "first_stage_model.encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias", + "first_stage_model.encoder.down.0.block.0.norm1.weight": "blocks.0.norm1.weight", + "first_stage_model.encoder.down.0.block.0.norm2.bias": "blocks.0.norm2.bias", + "first_stage_model.encoder.down.0.block.0.norm2.weight": "blocks.0.norm2.weight", + "first_stage_model.encoder.down.0.block.1.conv1.bias": "blocks.1.conv1.bias", + "first_stage_model.encoder.down.0.block.1.conv1.weight": "blocks.1.conv1.weight", + "first_stage_model.encoder.down.0.block.1.conv2.bias": "blocks.1.conv2.bias", + "first_stage_model.encoder.down.0.block.1.conv2.weight": "blocks.1.conv2.weight", + "first_stage_model.encoder.down.0.block.1.norm1.bias": "blocks.1.norm1.bias", + "first_stage_model.encoder.down.0.block.1.norm1.weight": "blocks.1.norm1.weight", + "first_stage_model.encoder.down.0.block.1.norm2.bias": "blocks.1.norm2.bias", + "first_stage_model.encoder.down.0.block.1.norm2.weight": "blocks.1.norm2.weight", + "first_stage_model.encoder.down.0.downsample.conv.bias": "blocks.2.conv.bias", + "first_stage_model.encoder.down.0.downsample.conv.weight": "blocks.2.conv.weight", + "first_stage_model.encoder.down.1.block.0.conv1.bias": "blocks.3.conv1.bias", + "first_stage_model.encoder.down.1.block.0.conv1.weight": "blocks.3.conv1.weight", + "first_stage_model.encoder.down.1.block.0.conv2.bias": "blocks.3.conv2.bias", + "first_stage_model.encoder.down.1.block.0.conv2.weight": "blocks.3.conv2.weight", + "first_stage_model.encoder.down.1.block.0.nin_shortcut.bias": "blocks.3.conv_shortcut.bias", + "first_stage_model.encoder.down.1.block.0.nin_shortcut.weight": "blocks.3.conv_shortcut.weight", + "first_stage_model.encoder.down.1.block.0.norm1.bias": "blocks.3.norm1.bias", + "first_stage_model.encoder.down.1.block.0.norm1.weight": "blocks.3.norm1.weight", + "first_stage_model.encoder.down.1.block.0.norm2.bias": "blocks.3.norm2.bias", + "first_stage_model.encoder.down.1.block.0.norm2.weight": "blocks.3.norm2.weight", + "first_stage_model.encoder.down.1.block.1.conv1.bias": "blocks.4.conv1.bias", + "first_stage_model.encoder.down.1.block.1.conv1.weight": "blocks.4.conv1.weight", + "first_stage_model.encoder.down.1.block.1.conv2.bias": "blocks.4.conv2.bias", + "first_stage_model.encoder.down.1.block.1.conv2.weight": "blocks.4.conv2.weight", + "first_stage_model.encoder.down.1.block.1.norm1.bias": "blocks.4.norm1.bias", + "first_stage_model.encoder.down.1.block.1.norm1.weight": "blocks.4.norm1.weight", + "first_stage_model.encoder.down.1.block.1.norm2.bias": "blocks.4.norm2.bias", + "first_stage_model.encoder.down.1.block.1.norm2.weight": "blocks.4.norm2.weight", + "first_stage_model.encoder.down.1.downsample.conv.bias": "blocks.5.conv.bias", + "first_stage_model.encoder.down.1.downsample.conv.weight": "blocks.5.conv.weight", + "first_stage_model.encoder.down.2.block.0.conv1.bias": "blocks.6.conv1.bias", + "first_stage_model.encoder.down.2.block.0.conv1.weight": "blocks.6.conv1.weight", + "first_stage_model.encoder.down.2.block.0.conv2.bias": "blocks.6.conv2.bias", + "first_stage_model.encoder.down.2.block.0.conv2.weight": "blocks.6.conv2.weight", + "first_stage_model.encoder.down.2.block.0.nin_shortcut.bias": "blocks.6.conv_shortcut.bias", + "first_stage_model.encoder.down.2.block.0.nin_shortcut.weight": "blocks.6.conv_shortcut.weight", + "first_stage_model.encoder.down.2.block.0.norm1.bias": "blocks.6.norm1.bias", + "first_stage_model.encoder.down.2.block.0.norm1.weight": "blocks.6.norm1.weight", + "first_stage_model.encoder.down.2.block.0.norm2.bias": "blocks.6.norm2.bias", + "first_stage_model.encoder.down.2.block.0.norm2.weight": "blocks.6.norm2.weight", + "first_stage_model.encoder.down.2.block.1.conv1.bias": "blocks.7.conv1.bias", + "first_stage_model.encoder.down.2.block.1.conv1.weight": "blocks.7.conv1.weight", + "first_stage_model.encoder.down.2.block.1.conv2.bias": "blocks.7.conv2.bias", + "first_stage_model.encoder.down.2.block.1.conv2.weight": "blocks.7.conv2.weight", + "first_stage_model.encoder.down.2.block.1.norm1.bias": "blocks.7.norm1.bias", + "first_stage_model.encoder.down.2.block.1.norm1.weight": "blocks.7.norm1.weight", + "first_stage_model.encoder.down.2.block.1.norm2.bias": "blocks.7.norm2.bias", + "first_stage_model.encoder.down.2.block.1.norm2.weight": "blocks.7.norm2.weight", + "first_stage_model.encoder.down.2.downsample.conv.bias": "blocks.8.conv.bias", + "first_stage_model.encoder.down.2.downsample.conv.weight": "blocks.8.conv.weight", + "first_stage_model.encoder.down.3.block.0.conv1.bias": "blocks.9.conv1.bias", + "first_stage_model.encoder.down.3.block.0.conv1.weight": "blocks.9.conv1.weight", + "first_stage_model.encoder.down.3.block.0.conv2.bias": "blocks.9.conv2.bias", + "first_stage_model.encoder.down.3.block.0.conv2.weight": "blocks.9.conv2.weight", + "first_stage_model.encoder.down.3.block.0.norm1.bias": "blocks.9.norm1.bias", + "first_stage_model.encoder.down.3.block.0.norm1.weight": "blocks.9.norm1.weight", + "first_stage_model.encoder.down.3.block.0.norm2.bias": "blocks.9.norm2.bias", + "first_stage_model.encoder.down.3.block.0.norm2.weight": "blocks.9.norm2.weight", + "first_stage_model.encoder.down.3.block.1.conv1.bias": "blocks.10.conv1.bias", + "first_stage_model.encoder.down.3.block.1.conv1.weight": "blocks.10.conv1.weight", + "first_stage_model.encoder.down.3.block.1.conv2.bias": "blocks.10.conv2.bias", + "first_stage_model.encoder.down.3.block.1.conv2.weight": "blocks.10.conv2.weight", + "first_stage_model.encoder.down.3.block.1.norm1.bias": "blocks.10.norm1.bias", + "first_stage_model.encoder.down.3.block.1.norm1.weight": "blocks.10.norm1.weight", + "first_stage_model.encoder.down.3.block.1.norm2.bias": "blocks.10.norm2.bias", + "first_stage_model.encoder.down.3.block.1.norm2.weight": "blocks.10.norm2.weight", + "first_stage_model.encoder.mid.attn_1.k.bias": "blocks.12.transformer_blocks.0.to_k.bias", + "first_stage_model.encoder.mid.attn_1.k.weight": "blocks.12.transformer_blocks.0.to_k.weight", + "first_stage_model.encoder.mid.attn_1.norm.bias": "blocks.12.norm.bias", + "first_stage_model.encoder.mid.attn_1.norm.weight": "blocks.12.norm.weight", + "first_stage_model.encoder.mid.attn_1.proj_out.bias": "blocks.12.transformer_blocks.0.to_out.bias", + "first_stage_model.encoder.mid.attn_1.proj_out.weight": "blocks.12.transformer_blocks.0.to_out.weight", + "first_stage_model.encoder.mid.attn_1.q.bias": "blocks.12.transformer_blocks.0.to_q.bias", + "first_stage_model.encoder.mid.attn_1.q.weight": "blocks.12.transformer_blocks.0.to_q.weight", + "first_stage_model.encoder.mid.attn_1.v.bias": "blocks.12.transformer_blocks.0.to_v.bias", + "first_stage_model.encoder.mid.attn_1.v.weight": "blocks.12.transformer_blocks.0.to_v.weight", + "first_stage_model.encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias", + "first_stage_model.encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight", + "first_stage_model.encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias", + "first_stage_model.encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight", + "first_stage_model.encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias", + "first_stage_model.encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight", + "first_stage_model.encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias", + "first_stage_model.encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight", + "first_stage_model.encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias", + "first_stage_model.encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight", + "first_stage_model.encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias", + "first_stage_model.encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight", + "first_stage_model.encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias", + "first_stage_model.encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight", + "first_stage_model.encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias", + "first_stage_model.encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight", + "first_stage_model.encoder.norm_out.bias": "conv_norm_out.bias", + "first_stage_model.encoder.norm_out.weight": "conv_norm_out.weight", + "first_stage_model.quant_conv.bias": "quant_conv.bias", + "first_stage_model.quant_conv.weight": "quant_conv.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if "transformer_blocks" in rename_dict[name]: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ diff --git a/diffsynth/models/sdxl_ipadapter.py b/diffsynth/models/sdxl_ipadapter.py new file mode 100644 index 0000000000000000000000000000000000000000..0b3faca2a5ad988a207242b4112eb1ffe855d3d1 --- /dev/null +++ b/diffsynth/models/sdxl_ipadapter.py @@ -0,0 +1,121 @@ +from .svd_image_encoder import SVDImageEncoder +from transformers import CLIPImageProcessor +import torch + + +class IpAdapterXLCLIPImageEmbedder(SVDImageEncoder): + def __init__(self): + super().__init__(embed_dim=1664, encoder_intermediate_size=8192, projection_dim=1280, num_encoder_layers=48, num_heads=16, head_dim=104) + self.image_processor = CLIPImageProcessor() + + def forward(self, image): + pixel_values = self.image_processor(images=image, return_tensors="pt").pixel_values + pixel_values = pixel_values.to(device=self.embeddings.class_embedding.device, dtype=self.embeddings.class_embedding.dtype) + return super().forward(pixel_values) + + +class IpAdapterImageProjModel(torch.nn.Module): + def __init__(self, cross_attention_dim=2048, clip_embeddings_dim=1280, clip_extra_context_tokens=4): + super().__init__() + self.cross_attention_dim = cross_attention_dim + self.clip_extra_context_tokens = clip_extra_context_tokens + self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim) + self.norm = torch.nn.LayerNorm(cross_attention_dim) + + def forward(self, image_embeds): + clip_extra_context_tokens = self.proj(image_embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim) + clip_extra_context_tokens = self.norm(clip_extra_context_tokens) + return clip_extra_context_tokens + + +class IpAdapterModule(torch.nn.Module): + def __init__(self, input_dim, output_dim): + super().__init__() + self.to_k_ip = torch.nn.Linear(input_dim, output_dim, bias=False) + self.to_v_ip = torch.nn.Linear(input_dim, output_dim, bias=False) + + def forward(self, hidden_states): + ip_k = self.to_k_ip(hidden_states) + ip_v = self.to_v_ip(hidden_states) + return ip_k, ip_v + + +class SDXLIpAdapter(torch.nn.Module): + def __init__(self): + super().__init__() + shape_list = [(2048, 640)] * 4 + [(2048, 1280)] * 50 + [(2048, 640)] * 6 + [(2048, 1280)] * 10 + self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(*shape) for shape in shape_list]) + self.image_proj = IpAdapterImageProjModel() + self.set_full_adapter() + + def set_full_adapter(self): + map_list = sum([ + [(7, i) for i in range(2)], + [(10, i) for i in range(2)], + [(15, i) for i in range(10)], + [(18, i) for i in range(10)], + [(25, i) for i in range(10)], + [(28, i) for i in range(10)], + [(31, i) for i in range(10)], + [(35, i) for i in range(2)], + [(38, i) for i in range(2)], + [(41, i) for i in range(2)], + [(21, i) for i in range(10)], + ], []) + self.call_block_id = {i: j for j, i in enumerate(map_list)} + + def set_less_adapter(self): + map_list = sum([ + [(7, i) for i in range(2)], + [(10, i) for i in range(2)], + [(15, i) for i in range(10)], + [(18, i) for i in range(10)], + [(25, i) for i in range(10)], + [(28, i) for i in range(10)], + [(31, i) for i in range(10)], + [(35, i) for i in range(2)], + [(38, i) for i in range(2)], + [(41, i) for i in range(2)], + [(21, i) for i in range(10)], + ], []) + self.call_block_id = {i: j for j, i in enumerate(map_list) if j>=34 and j<44} + + def forward(self, hidden_states, scale=1.0): + hidden_states = self.image_proj(hidden_states) + hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1]) + ip_kv_dict = {} + for (block_id, transformer_id) in self.call_block_id: + ipadapter_id = self.call_block_id[(block_id, transformer_id)] + ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states) + if block_id not in ip_kv_dict: + ip_kv_dict[block_id] = {} + ip_kv_dict[block_id][transformer_id] = { + "ip_k": ip_k, + "ip_v": ip_v, + "scale": scale + } + return ip_kv_dict + + def state_dict_converter(self): + return SDXLIpAdapterStateDictConverter() + + +class SDXLIpAdapterStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + state_dict_ = {} + for name in state_dict["ip_adapter"]: + names = name.split(".") + layer_id = str(int(names[0]) // 2) + name_ = ".".join(["ipadapter_modules"] + [layer_id] + names[1:]) + state_dict_[name_] = state_dict["ip_adapter"][name] + for name in state_dict["image_proj"]: + name_ = "image_proj." + name + state_dict_[name_] = state_dict["image_proj"][name] + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) + diff --git a/diffsynth/models/sdxl_motion.py b/diffsynth/models/sdxl_motion.py new file mode 100644 index 0000000000000000000000000000000000000000..329b1c65cf11d2aeec4564a273b1282b835dc1c0 --- /dev/null +++ b/diffsynth/models/sdxl_motion.py @@ -0,0 +1,103 @@ +from .sd_motion import TemporalBlock +import torch + + + +class SDXLMotionModel(torch.nn.Module): + def __init__(self): + super().__init__() + self.motion_modules = torch.nn.ModuleList([ + TemporalBlock(8, 320//8, 320, eps=1e-6), + TemporalBlock(8, 320//8, 320, eps=1e-6), + + TemporalBlock(8, 640//8, 640, eps=1e-6), + TemporalBlock(8, 640//8, 640, eps=1e-6), + + TemporalBlock(8, 1280//8, 1280, eps=1e-6), + TemporalBlock(8, 1280//8, 1280, eps=1e-6), + + TemporalBlock(8, 1280//8, 1280, eps=1e-6), + TemporalBlock(8, 1280//8, 1280, eps=1e-6), + TemporalBlock(8, 1280//8, 1280, eps=1e-6), + + TemporalBlock(8, 640//8, 640, eps=1e-6), + TemporalBlock(8, 640//8, 640, eps=1e-6), + TemporalBlock(8, 640//8, 640, eps=1e-6), + + TemporalBlock(8, 320//8, 320, eps=1e-6), + TemporalBlock(8, 320//8, 320, eps=1e-6), + TemporalBlock(8, 320//8, 320, eps=1e-6), + ]) + self.call_block_id = { + 0: 0, + 2: 1, + 7: 2, + 10: 3, + 15: 4, + 18: 5, + 25: 6, + 28: 7, + 31: 8, + 35: 9, + 38: 10, + 41: 11, + 44: 12, + 46: 13, + 48: 14, + } + + def forward(self): + pass + + def state_dict_converter(self): + return SDMotionModelStateDictConverter() + + +class SDMotionModelStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + rename_dict = { + "norm": "norm", + "proj_in": "proj_in", + "transformer_blocks.0.attention_blocks.0.to_q": "transformer_blocks.0.attn1.to_q", + "transformer_blocks.0.attention_blocks.0.to_k": "transformer_blocks.0.attn1.to_k", + "transformer_blocks.0.attention_blocks.0.to_v": "transformer_blocks.0.attn1.to_v", + "transformer_blocks.0.attention_blocks.0.to_out.0": "transformer_blocks.0.attn1.to_out", + "transformer_blocks.0.attention_blocks.0.pos_encoder": "transformer_blocks.0.pe1", + "transformer_blocks.0.attention_blocks.1.to_q": "transformer_blocks.0.attn2.to_q", + "transformer_blocks.0.attention_blocks.1.to_k": "transformer_blocks.0.attn2.to_k", + "transformer_blocks.0.attention_blocks.1.to_v": "transformer_blocks.0.attn2.to_v", + "transformer_blocks.0.attention_blocks.1.to_out.0": "transformer_blocks.0.attn2.to_out", + "transformer_blocks.0.attention_blocks.1.pos_encoder": "transformer_blocks.0.pe2", + "transformer_blocks.0.norms.0": "transformer_blocks.0.norm1", + "transformer_blocks.0.norms.1": "transformer_blocks.0.norm2", + "transformer_blocks.0.ff.net.0.proj": "transformer_blocks.0.act_fn.proj", + "transformer_blocks.0.ff.net.2": "transformer_blocks.0.ff", + "transformer_blocks.0.ff_norm": "transformer_blocks.0.norm3", + "proj_out": "proj_out", + } + name_list = sorted([i for i in state_dict if i.startswith("down_blocks.")]) + name_list += sorted([i for i in state_dict if i.startswith("mid_block.")]) + name_list += sorted([i for i in state_dict if i.startswith("up_blocks.")]) + state_dict_ = {} + last_prefix, module_id = "", -1 + for name in name_list: + names = name.split(".") + prefix_index = names.index("temporal_transformer") + 1 + prefix = ".".join(names[:prefix_index]) + if prefix != last_prefix: + last_prefix = prefix + module_id += 1 + middle_name = ".".join(names[prefix_index:-1]) + suffix = names[-1] + if "pos_encoder" in names: + rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name]]) + else: + rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name], suffix]) + state_dict_[rename] = state_dict[name] + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict) diff --git a/diffsynth/models/sdxl_text_encoder.py b/diffsynth/models/sdxl_text_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..3df6ec6fa36574ffcbae5bf982a29d2fb25f358f --- /dev/null +++ b/diffsynth/models/sdxl_text_encoder.py @@ -0,0 +1,757 @@ +import torch +from .sd_text_encoder import CLIPEncoderLayer + + +class SDXLTextEncoder(torch.nn.Module): + def __init__(self, embed_dim=768, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=11, encoder_intermediate_size=3072): + super().__init__() + + # token_embedding + self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim) + + # position_embeds (This is a fixed tensor) + self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim)) + + # encoders + self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)]) + + # attn_mask + self.attn_mask = self.attention_mask(max_position_embeddings) + + # The text encoder is different to that in Stable Diffusion 1.x. + # It does not include final_layer_norm. + + def attention_mask(self, length): + mask = torch.empty(length, length) + mask.fill_(float("-inf")) + mask.triu_(1) + return mask + + def forward(self, input_ids, clip_skip=1): + embeds = self.token_embedding(input_ids) + self.position_embeds + attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype) + for encoder_id, encoder in enumerate(self.encoders): + embeds = encoder(embeds, attn_mask=attn_mask) + if encoder_id + clip_skip == len(self.encoders): + break + return embeds + + def state_dict_converter(self): + return SDXLTextEncoderStateDictConverter() + + +class SDXLTextEncoder2(torch.nn.Module): + def __init__(self, embed_dim=1280, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=32, encoder_intermediate_size=5120): + super().__init__() + + # token_embedding + self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim) + + # position_embeds (This is a fixed tensor) + self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim)) + + # encoders + self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size, num_heads=20, head_dim=64, use_quick_gelu=False) for _ in range(num_encoder_layers)]) + + # attn_mask + self.attn_mask = self.attention_mask(max_position_embeddings) + + # final_layer_norm + self.final_layer_norm = torch.nn.LayerNorm(embed_dim) + + # text_projection + self.text_projection = torch.nn.Linear(embed_dim, embed_dim, bias=False) + + def attention_mask(self, length): + mask = torch.empty(length, length) + mask.fill_(float("-inf")) + mask.triu_(1) + return mask + + def forward(self, input_ids, clip_skip=2): + embeds = self.token_embedding(input_ids) + self.position_embeds + attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype) + for encoder_id, encoder in enumerate(self.encoders): + embeds = encoder(embeds, attn_mask=attn_mask) + if encoder_id + clip_skip == len(self.encoders): + hidden_states = embeds + embeds = self.final_layer_norm(embeds) + pooled_embeds = embeds[torch.arange(embeds.shape[0]), input_ids.to(dtype=torch.int).argmax(dim=-1)] + pooled_embeds = self.text_projection(pooled_embeds) + return pooled_embeds, hidden_states + + def state_dict_converter(self): + return SDXLTextEncoder2StateDictConverter() + + +class SDXLTextEncoderStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + rename_dict = { + "text_model.embeddings.token_embedding.weight": "token_embedding.weight", + "text_model.embeddings.position_embedding.weight": "position_embeds", + "text_model.final_layer_norm.weight": "final_layer_norm.weight", + "text_model.final_layer_norm.bias": "final_layer_norm.bias" + } + attn_rename_dict = { + "self_attn.q_proj": "attn.to_q", + "self_attn.k_proj": "attn.to_k", + "self_attn.v_proj": "attn.to_v", + "self_attn.out_proj": "attn.to_out", + "layer_norm1": "layer_norm1", + "layer_norm2": "layer_norm2", + "mlp.fc1": "fc1", + "mlp.fc2": "fc2", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict[name]] = param + elif name.startswith("text_model.encoder.layers."): + param = state_dict[name] + names = name.split(".") + layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1] + name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail]) + state_dict_[name_] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + 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"conditioner.embedders.0.transformer.text_model.encoder.layers.6.layer_norm2.bias": "encoders.6.layer_norm2.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.6.layer_norm2.weight": "encoders.6.layer_norm2.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.6.mlp.fc1.bias": "encoders.6.fc1.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.6.mlp.fc1.weight": "encoders.6.fc1.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.6.mlp.fc2.bias": "encoders.6.fc2.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.6.mlp.fc2.weight": "encoders.6.fc2.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.k_proj.bias": "encoders.6.attn.to_k.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.k_proj.weight": "encoders.6.attn.to_k.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.out_proj.bias": "encoders.6.attn.to_out.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.out_proj.weight": "encoders.6.attn.to_out.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.q_proj.bias": "encoders.6.attn.to_q.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.q_proj.weight": "encoders.6.attn.to_q.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.v_proj.bias": "encoders.6.attn.to_v.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.v_proj.weight": "encoders.6.attn.to_v.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.7.layer_norm1.bias": "encoders.7.layer_norm1.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.7.layer_norm1.weight": "encoders.7.layer_norm1.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.7.layer_norm2.bias": "encoders.7.layer_norm2.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.7.layer_norm2.weight": "encoders.7.layer_norm2.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.7.mlp.fc1.bias": "encoders.7.fc1.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.7.mlp.fc1.weight": "encoders.7.fc1.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.7.mlp.fc2.bias": "encoders.7.fc2.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.7.mlp.fc2.weight": "encoders.7.fc2.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.k_proj.bias": "encoders.7.attn.to_k.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.k_proj.weight": "encoders.7.attn.to_k.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.out_proj.bias": "encoders.7.attn.to_out.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.out_proj.weight": "encoders.7.attn.to_out.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.q_proj.bias": "encoders.7.attn.to_q.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.q_proj.weight": "encoders.7.attn.to_q.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.v_proj.bias": "encoders.7.attn.to_v.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.v_proj.weight": "encoders.7.attn.to_v.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.8.layer_norm1.bias": "encoders.8.layer_norm1.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.8.layer_norm1.weight": "encoders.8.layer_norm1.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.8.layer_norm2.bias": "encoders.8.layer_norm2.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.8.layer_norm2.weight": "encoders.8.layer_norm2.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.8.mlp.fc1.bias": "encoders.8.fc1.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.8.mlp.fc1.weight": "encoders.8.fc1.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.8.mlp.fc2.bias": "encoders.8.fc2.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.8.mlp.fc2.weight": "encoders.8.fc2.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.k_proj.bias": "encoders.8.attn.to_k.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.k_proj.weight": "encoders.8.attn.to_k.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.out_proj.bias": "encoders.8.attn.to_out.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.out_proj.weight": "encoders.8.attn.to_out.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.q_proj.bias": "encoders.8.attn.to_q.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.q_proj.weight": "encoders.8.attn.to_q.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.v_proj.bias": "encoders.8.attn.to_v.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.v_proj.weight": "encoders.8.attn.to_v.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.layer_norm1.bias": "encoders.9.layer_norm1.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.layer_norm1.weight": "encoders.9.layer_norm1.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.layer_norm2.bias": "encoders.9.layer_norm2.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.layer_norm2.weight": "encoders.9.layer_norm2.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.mlp.fc1.bias": "encoders.9.fc1.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.mlp.fc1.weight": "encoders.9.fc1.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.mlp.fc2.bias": "encoders.9.fc2.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.mlp.fc2.weight": "encoders.9.fc2.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.k_proj.bias": "encoders.9.attn.to_k.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.k_proj.weight": "encoders.9.attn.to_k.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.out_proj.bias": "encoders.9.attn.to_out.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.out_proj.weight": "encoders.9.attn.to_out.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.q_proj.bias": "encoders.9.attn.to_q.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias", + "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict[name]] = param + return state_dict_ + + +class SDXLTextEncoder2StateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + rename_dict = { + "text_model.embeddings.token_embedding.weight": "token_embedding.weight", + "text_model.embeddings.position_embedding.weight": "position_embeds", + "text_model.final_layer_norm.weight": "final_layer_norm.weight", + "text_model.final_layer_norm.bias": "final_layer_norm.bias", + "text_projection.weight": "text_projection.weight" + } + attn_rename_dict = { + "self_attn.q_proj": "attn.to_q", + "self_attn.k_proj": "attn.to_k", + "self_attn.v_proj": "attn.to_v", + "self_attn.out_proj": "attn.to_out", + "layer_norm1": "layer_norm1", + "layer_norm2": "layer_norm2", + "mlp.fc1": "fc1", + "mlp.fc2": "fc2", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "text_model.embeddings.position_embedding.weight": + param = param.reshape((1, param.shape[0], param.shape[1])) + state_dict_[rename_dict[name]] = param + elif name.startswith("text_model.encoder.layers."): + param = state_dict[name] + names = name.split(".") + layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1] + name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail]) + state_dict_[name_] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "conditioner.embedders.1.model.ln_final.bias": "final_layer_norm.bias", + "conditioner.embedders.1.model.ln_final.weight": "final_layer_norm.weight", + "conditioner.embedders.1.model.positional_embedding": "position_embeds", + "conditioner.embedders.1.model.token_embedding.weight": "token_embedding.weight", + "conditioner.embedders.1.model.transformer.resblocks.0.attn.in_proj_bias": ['encoders.0.attn.to_q.bias', 'encoders.0.attn.to_k.bias', 'encoders.0.attn.to_v.bias'], + "conditioner.embedders.1.model.transformer.resblocks.0.attn.in_proj_weight": ['encoders.0.attn.to_q.weight', 'encoders.0.attn.to_k.weight', 'encoders.0.attn.to_v.weight'], + "conditioner.embedders.1.model.transformer.resblocks.0.attn.out_proj.bias": "encoders.0.attn.to_out.bias", + "conditioner.embedders.1.model.transformer.resblocks.0.attn.out_proj.weight": "encoders.0.attn.to_out.weight", + "conditioner.embedders.1.model.transformer.resblocks.0.ln_1.bias": "encoders.0.layer_norm1.bias", + "conditioner.embedders.1.model.transformer.resblocks.0.ln_1.weight": "encoders.0.layer_norm1.weight", + "conditioner.embedders.1.model.transformer.resblocks.0.ln_2.bias": "encoders.0.layer_norm2.bias", + "conditioner.embedders.1.model.transformer.resblocks.0.ln_2.weight": "encoders.0.layer_norm2.weight", + "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_fc.bias": "encoders.0.fc1.bias", + "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_fc.weight": "encoders.0.fc1.weight", + "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_proj.bias": "encoders.0.fc2.bias", + "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_proj.weight": "encoders.0.fc2.weight", + "conditioner.embedders.1.model.transformer.resblocks.1.attn.in_proj_bias": ['encoders.1.attn.to_q.bias', 'encoders.1.attn.to_k.bias', 'encoders.1.attn.to_v.bias'], + "conditioner.embedders.1.model.transformer.resblocks.1.attn.in_proj_weight": ['encoders.1.attn.to_q.weight', 'encoders.1.attn.to_k.weight', 'encoders.1.attn.to_v.weight'], + "conditioner.embedders.1.model.transformer.resblocks.1.attn.out_proj.bias": "encoders.1.attn.to_out.bias", + "conditioner.embedders.1.model.transformer.resblocks.1.attn.out_proj.weight": "encoders.1.attn.to_out.weight", + "conditioner.embedders.1.model.transformer.resblocks.1.ln_1.bias": "encoders.1.layer_norm1.bias", + "conditioner.embedders.1.model.transformer.resblocks.1.ln_1.weight": "encoders.1.layer_norm1.weight", + "conditioner.embedders.1.model.transformer.resblocks.1.ln_2.bias": "encoders.1.layer_norm2.bias", + "conditioner.embedders.1.model.transformer.resblocks.1.ln_2.weight": "encoders.1.layer_norm2.weight", + "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_fc.bias": "encoders.1.fc1.bias", + "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_fc.weight": "encoders.1.fc1.weight", + "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_proj.bias": "encoders.1.fc2.bias", + "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_proj.weight": "encoders.1.fc2.weight", + "conditioner.embedders.1.model.transformer.resblocks.10.attn.in_proj_bias": ['encoders.10.attn.to_q.bias', 'encoders.10.attn.to_k.bias', 'encoders.10.attn.to_v.bias'], + "conditioner.embedders.1.model.transformer.resblocks.10.attn.in_proj_weight": ['encoders.10.attn.to_q.weight', 'encoders.10.attn.to_k.weight', 'encoders.10.attn.to_v.weight'], + "conditioner.embedders.1.model.transformer.resblocks.10.attn.out_proj.bias": "encoders.10.attn.to_out.bias", + "conditioner.embedders.1.model.transformer.resblocks.10.attn.out_proj.weight": "encoders.10.attn.to_out.weight", + "conditioner.embedders.1.model.transformer.resblocks.10.ln_1.bias": "encoders.10.layer_norm1.bias", + "conditioner.embedders.1.model.transformer.resblocks.10.ln_1.weight": "encoders.10.layer_norm1.weight", + "conditioner.embedders.1.model.transformer.resblocks.10.ln_2.bias": "encoders.10.layer_norm2.bias", + "conditioner.embedders.1.model.transformer.resblocks.10.ln_2.weight": "encoders.10.layer_norm2.weight", + "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_fc.bias": "encoders.10.fc1.bias", + "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_fc.weight": "encoders.10.fc1.weight", + "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_proj.bias": "encoders.10.fc2.bias", + "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_proj.weight": "encoders.10.fc2.weight", + 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"conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.weight": "encoders.9.fc2.weight", + "conditioner.embedders.1.model.text_projection": "text_projection.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "conditioner.embedders.1.model.positional_embedding": + param = param.reshape((1, param.shape[0], param.shape[1])) + elif name == "conditioner.embedders.1.model.text_projection": + param = param.T + if isinstance(rename_dict[name], str): + state_dict_[rename_dict[name]] = param + else: + length = param.shape[0] // 3 + for i, rename in enumerate(rename_dict[name]): + state_dict_[rename] = param[i*length: i*length+length] + return state_dict_ \ No newline at end of file diff --git a/diffsynth/models/sdxl_unet.py b/diffsynth/models/sdxl_unet.py new file mode 100644 index 0000000000000000000000000000000000000000..b70a8dff877c7056ca47f780c810ca971811b384 --- /dev/null +++ b/diffsynth/models/sdxl_unet.py @@ -0,0 +1,1892 @@ +import torch +from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, PopBlock, DownSampler, UpSampler + + +class SDXLUNet(torch.nn.Module): + def __init__(self, is_kolors=False): + super().__init__() + self.time_proj = Timesteps(320) + self.time_embedding = torch.nn.Sequential( + torch.nn.Linear(320, 1280), + torch.nn.SiLU(), + torch.nn.Linear(1280, 1280) + ) + self.add_time_proj = Timesteps(256) + self.add_time_embedding = torch.nn.Sequential( + torch.nn.Linear(5632 if is_kolors else 2816, 1280), + torch.nn.SiLU(), + torch.nn.Linear(1280, 1280) + ) + self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1) + self.text_intermediate_proj = torch.nn.Linear(4096, 2048) if is_kolors else None + + self.blocks = torch.nn.ModuleList([ + # DownBlock2D + ResnetBlock(320, 320, 1280), + PushBlock(), + ResnetBlock(320, 320, 1280), + PushBlock(), + DownSampler(320), + PushBlock(), + # CrossAttnDownBlock2D + ResnetBlock(320, 640, 1280), + AttentionBlock(10, 64, 640, 2, 2048), + PushBlock(), + ResnetBlock(640, 640, 1280), + AttentionBlock(10, 64, 640, 2, 2048), + PushBlock(), + DownSampler(640), + PushBlock(), + # CrossAttnDownBlock2D + ResnetBlock(640, 1280, 1280), + AttentionBlock(20, 64, 1280, 10, 2048), + PushBlock(), + ResnetBlock(1280, 1280, 1280), + AttentionBlock(20, 64, 1280, 10, 2048), + PushBlock(), + # UNetMidBlock2DCrossAttn + ResnetBlock(1280, 1280, 1280), + AttentionBlock(20, 64, 1280, 10, 2048), + ResnetBlock(1280, 1280, 1280), + # CrossAttnUpBlock2D + PopBlock(), + ResnetBlock(2560, 1280, 1280), + AttentionBlock(20, 64, 1280, 10, 2048), + PopBlock(), + ResnetBlock(2560, 1280, 1280), + AttentionBlock(20, 64, 1280, 10, 2048), + PopBlock(), + ResnetBlock(1920, 1280, 1280), + AttentionBlock(20, 64, 1280, 10, 2048), + UpSampler(1280), + # CrossAttnUpBlock2D + PopBlock(), + ResnetBlock(1920, 640, 1280), + AttentionBlock(10, 64, 640, 2, 2048), + PopBlock(), + ResnetBlock(1280, 640, 1280), + AttentionBlock(10, 64, 640, 2, 2048), + PopBlock(), + ResnetBlock(960, 640, 1280), + AttentionBlock(10, 64, 640, 2, 2048), + UpSampler(640), + # UpBlock2D + PopBlock(), + ResnetBlock(960, 320, 1280), + PopBlock(), + ResnetBlock(640, 320, 1280), + PopBlock(), + ResnetBlock(640, 320, 1280) + ]) + + self.conv_norm_out = torch.nn.GroupNorm(num_channels=320, num_groups=32, eps=1e-5) + self.conv_act = torch.nn.SiLU() + self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1) + + def forward( + self, + sample, timestep, encoder_hidden_states, add_time_id, add_text_embeds, + tiled=False, tile_size=64, tile_stride=8, + use_gradient_checkpointing=False, + **kwargs + ): + # 1. time + t_emb = self.time_proj(timestep[None]).to(sample.dtype) + t_emb = self.time_embedding(t_emb) + + time_embeds = self.add_time_proj(add_time_id) + time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1)) + add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1) + add_embeds = add_embeds.to(sample.dtype) + add_embeds = self.add_time_embedding(add_embeds) + + time_emb = t_emb + add_embeds + + # 2. pre-process + height, width = sample.shape[2], sample.shape[3] + hidden_states = self.conv_in(sample) + text_emb = encoder_hidden_states if self.text_intermediate_proj is None else self.text_intermediate_proj(encoder_hidden_states) + res_stack = [hidden_states] + + # 3. blocks + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + for i, block in enumerate(self.blocks): + if self.training and use_gradient_checkpointing and not (isinstance(block, PushBlock) or isinstance(block, PopBlock)): + hidden_states, time_emb, text_emb, res_stack = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, time_emb, text_emb, res_stack, + use_reentrant=False, + ) + else: + hidden_states, time_emb, text_emb, res_stack = block( + hidden_states, time_emb, text_emb, res_stack, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride + ) + + # 4. output + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + + return hidden_states + + def state_dict_converter(self): + return SDXLUNetStateDictConverter() + + +class SDXLUNetStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + # architecture + block_types = [ + 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', 'DownSampler', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', + 'ResnetBlock', 'AttentionBlock', 'ResnetBlock', + 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', + 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', + 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock' + ] + + # Rename each parameter + name_list = sorted([name for name in state_dict]) + rename_dict = {} + block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1} + last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""} + for name in name_list: + names = name.split(".") + if names[0] in ["conv_in", "conv_norm_out", "conv_out"]: + pass + elif names[0] in ["encoder_hid_proj"]: + names[0] = "text_intermediate_proj" + elif names[0] in ["time_embedding", "add_embedding"]: + if names[0] == "add_embedding": + names[0] = "add_time_embedding" + names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]] + elif names[0] in ["down_blocks", "mid_block", "up_blocks"]: + if names[0] == "mid_block": + names.insert(1, "0") + block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]] + block_type_with_id = ".".join(names[:4]) + if block_type_with_id != last_block_type_with_id[block_type]: + block_id[block_type] += 1 + last_block_type_with_id[block_type] = block_type_with_id + while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: + block_id[block_type] += 1 + block_type_with_id = ".".join(names[:4]) + names = ["blocks", str(block_id[block_type])] + names[4:] + if "ff" in names: + ff_index = names.index("ff") + component = ".".join(names[ff_index:ff_index+3]) + component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component] + names = names[:ff_index] + [component] + names[ff_index+3:] + if "to_out" in names: + names.pop(names.index("to_out") + 1) + else: + raise ValueError(f"Unknown parameters: {name}") + rename_dict[name] = ".".join(names) + + # Convert state_dict + state_dict_ = {} + for name, param in state_dict.items(): + if ".proj_in." in name or ".proj_out." in name: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias", + "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight", + "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias", + "model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight", + "model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias", + "model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight", + "model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias", + "model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight", + "model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias", + 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"model.diffusion_model.output_blocks.8.0.out_layers.0.weight": "blocks.48.norm2.weight", + "model.diffusion_model.output_blocks.8.0.out_layers.3.bias": "blocks.48.conv2.bias", + "model.diffusion_model.output_blocks.8.0.out_layers.3.weight": "blocks.48.conv2.weight", + "model.diffusion_model.output_blocks.8.0.skip_connection.bias": "blocks.48.conv_shortcut.bias", + "model.diffusion_model.output_blocks.8.0.skip_connection.weight": "blocks.48.conv_shortcut.weight", + "model.diffusion_model.time_embed.0.bias": "time_embedding.0.bias", + "model.diffusion_model.time_embed.0.weight": "time_embedding.0.weight", + "model.diffusion_model.time_embed.2.bias": "time_embedding.2.bias", + "model.diffusion_model.time_embed.2.weight": "time_embedding.2.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if ".proj_in." in name or ".proj_out." in name: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ diff --git a/diffsynth/models/sdxl_vae_decoder.py b/diffsynth/models/sdxl_vae_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..c191f52d4fb7ef5478a396159c6767111d364d2f --- /dev/null +++ b/diffsynth/models/sdxl_vae_decoder.py @@ -0,0 +1,15 @@ +from .sd_vae_decoder import SDVAEDecoder, SDVAEDecoderStateDictConverter + + +class SDXLVAEDecoder(SDVAEDecoder): + def __init__(self): + super().__init__() + self.scaling_factor = 0.13025 + + def state_dict_converter(self): + return SDXLVAEDecoderStateDictConverter() + + +class SDXLVAEDecoderStateDictConverter(SDVAEDecoderStateDictConverter): + def __init__(self): + super().__init__() diff --git a/diffsynth/models/sdxl_vae_encoder.py b/diffsynth/models/sdxl_vae_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..36b131debb28d157f15584951bdae8416c979b16 --- /dev/null +++ b/diffsynth/models/sdxl_vae_encoder.py @@ -0,0 +1,15 @@ +from .sd_vae_encoder import SDVAEEncoderStateDictConverter, SDVAEEncoder + + +class SDXLVAEEncoder(SDVAEEncoder): + def __init__(self): + super().__init__() + self.scaling_factor = 0.13025 + + def state_dict_converter(self): + return SDXLVAEEncoderStateDictConverter() + + +class SDXLVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter): + def __init__(self): + super().__init__() diff --git a/diffsynth/models/svd_image_encoder.py b/diffsynth/models/svd_image_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..c3aa32fbb00bf55d0bb59e7edae797f24e8fea42 --- /dev/null +++ b/diffsynth/models/svd_image_encoder.py @@ -0,0 +1,504 @@ +import torch +from .sd_text_encoder import CLIPEncoderLayer + + +class CLIPVisionEmbeddings(torch.nn.Module): + def __init__(self, embed_dim=1280, image_size=224, patch_size=14, num_channels=3): + super().__init__() + + # class_embeds (This is a fixed tensor) + self.class_embedding = torch.nn.Parameter(torch.randn(1, 1, embed_dim)) + + # position_embeds + self.patch_embedding = torch.nn.Conv2d(in_channels=num_channels, out_channels=embed_dim, kernel_size=patch_size, stride=patch_size, bias=False) + + # position_embeds (This is a fixed tensor) + self.position_embeds = torch.nn.Parameter(torch.zeros(1, (image_size // patch_size) ** 2 + 1, embed_dim)) + + def forward(self, pixel_values): + batch_size = pixel_values.shape[0] + patch_embeds = self.patch_embedding(pixel_values) + patch_embeds = patch_embeds.flatten(2).transpose(1, 2) + class_embeds = self.class_embedding.repeat(batch_size, 1, 1) + embeddings = torch.cat([class_embeds, patch_embeds], dim=1) + self.position_embeds + return embeddings + + +class SVDImageEncoder(torch.nn.Module): + def __init__(self, embed_dim=1280, layer_norm_eps=1e-5, num_encoder_layers=32, encoder_intermediate_size=5120, projection_dim=1024, num_heads=16, head_dim=80): + super().__init__() + self.embeddings = CLIPVisionEmbeddings(embed_dim=embed_dim) + self.pre_layernorm = torch.nn.LayerNorm(embed_dim, eps=layer_norm_eps) + self.encoders = torch.nn.ModuleList([ + CLIPEncoderLayer(embed_dim, encoder_intermediate_size, num_heads=num_heads, head_dim=head_dim, use_quick_gelu=False) + for _ in range(num_encoder_layers)]) + self.post_layernorm = torch.nn.LayerNorm(embed_dim, eps=layer_norm_eps) + self.visual_projection = torch.nn.Linear(embed_dim, projection_dim, bias=False) + + def forward(self, pixel_values): + embeds = self.embeddings(pixel_values) + embeds = self.pre_layernorm(embeds) + for encoder_id, encoder in enumerate(self.encoders): + embeds = encoder(embeds) + embeds = self.post_layernorm(embeds[:, 0, :]) + embeds = self.visual_projection(embeds) + return embeds + + def state_dict_converter(self): + return SVDImageEncoderStateDictConverter() + + +class SVDImageEncoderStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + rename_dict = { + "vision_model.embeddings.patch_embedding.weight": "embeddings.patch_embedding.weight", + "vision_model.embeddings.class_embedding": "embeddings.class_embedding", + "vision_model.embeddings.position_embedding.weight": "embeddings.position_embeds", + "vision_model.pre_layrnorm.weight": "pre_layernorm.weight", + "vision_model.pre_layrnorm.bias": "pre_layernorm.bias", + "vision_model.post_layernorm.weight": "post_layernorm.weight", + "vision_model.post_layernorm.bias": "post_layernorm.bias", + "visual_projection.weight": "visual_projection.weight" + } + attn_rename_dict = { + "self_attn.q_proj": "attn.to_q", + "self_attn.k_proj": "attn.to_k", + "self_attn.v_proj": "attn.to_v", + "self_attn.out_proj": "attn.to_out", + "layer_norm1": "layer_norm1", + "layer_norm2": "layer_norm2", + "mlp.fc1": "fc1", + "mlp.fc2": "fc2", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "vision_model.embeddings.class_embedding": + param = state_dict[name].view(1, 1, -1) + elif name == "vision_model.embeddings.position_embedding.weight": + param = state_dict[name].unsqueeze(0) + state_dict_[rename_dict[name]] = param + elif name.startswith("vision_model.encoder.layers."): + param = state_dict[name] + names = name.split(".") + layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1] + name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail]) + state_dict_[name_] = param + return state_dict_ + + def from_civitai(self, state_dict): + rename_dict = { + "conditioner.embedders.0.open_clip.model.visual.class_embedding": "embeddings.class_embedding", + "conditioner.embedders.0.open_clip.model.visual.conv1.weight": "embeddings.patch_embedding.weight", + "conditioner.embedders.0.open_clip.model.visual.ln_post.bias": "post_layernorm.bias", + "conditioner.embedders.0.open_clip.model.visual.ln_post.weight": "post_layernorm.weight", + "conditioner.embedders.0.open_clip.model.visual.ln_pre.bias": "pre_layernorm.bias", + "conditioner.embedders.0.open_clip.model.visual.ln_pre.weight": "pre_layernorm.weight", + "conditioner.embedders.0.open_clip.model.visual.positional_embedding": "embeddings.position_embeds", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.in_proj_bias": ['encoders.0.attn.to_q.bias', 'encoders.0.attn.to_k.bias', 'encoders.0.attn.to_v.bias'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.in_proj_weight": ['encoders.0.attn.to_q.weight', 'encoders.0.attn.to_k.weight', 'encoders.0.attn.to_v.weight'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.out_proj.bias": "encoders.0.attn.to_out.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.out_proj.weight": "encoders.0.attn.to_out.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_1.bias": "encoders.0.layer_norm1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_1.weight": "encoders.0.layer_norm1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_2.bias": "encoders.0.layer_norm2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_2.weight": "encoders.0.layer_norm2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_fc.bias": "encoders.0.fc1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_fc.weight": "encoders.0.fc1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_proj.bias": "encoders.0.fc2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_proj.weight": "encoders.0.fc2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.in_proj_bias": ['encoders.1.attn.to_q.bias', 'encoders.1.attn.to_k.bias', 'encoders.1.attn.to_v.bias'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.in_proj_weight": ['encoders.1.attn.to_q.weight', 'encoders.1.attn.to_k.weight', 'encoders.1.attn.to_v.weight'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.out_proj.bias": "encoders.1.attn.to_out.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.out_proj.weight": "encoders.1.attn.to_out.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_1.bias": "encoders.1.layer_norm1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_1.weight": "encoders.1.layer_norm1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_2.bias": "encoders.1.layer_norm2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_2.weight": "encoders.1.layer_norm2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_fc.bias": "encoders.1.fc1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_fc.weight": "encoders.1.fc1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_proj.bias": "encoders.1.fc2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_proj.weight": "encoders.1.fc2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.attn.in_proj_bias": ['encoders.10.attn.to_q.bias', 'encoders.10.attn.to_k.bias', 'encoders.10.attn.to_v.bias'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.attn.in_proj_weight": ['encoders.10.attn.to_q.weight', 'encoders.10.attn.to_k.weight', 'encoders.10.attn.to_v.weight'], + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.attn.out_proj.bias": "encoders.10.attn.to_out.bias", + 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"conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.ln_2.bias": "encoders.9.layer_norm2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.ln_2.weight": "encoders.9.layer_norm2.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_fc.bias": "encoders.9.fc1.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_fc.weight": "encoders.9.fc1.weight", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_proj.bias": "encoders.9.fc2.bias", + "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_proj.weight": "encoders.9.fc2.weight", + "conditioner.embedders.0.open_clip.model.visual.proj": "visual_projection.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if name == "conditioner.embedders.0.open_clip.model.visual.class_embedding": + param = param.reshape((1, 1, param.shape[0])) + elif name == "conditioner.embedders.0.open_clip.model.visual.positional_embedding": + param = param.reshape((1, param.shape[0], param.shape[1])) + elif name == "conditioner.embedders.0.open_clip.model.visual.proj": + param = param.T + if isinstance(rename_dict[name], str): + state_dict_[rename_dict[name]] = param + else: + length = param.shape[0] // 3 + for i, rename in enumerate(rename_dict[name]): + state_dict_[rename] = param[i*length: i*length+length] + return state_dict_ diff --git a/diffsynth/models/svd_unet.py b/diffsynth/models/svd_unet.py new file mode 100644 index 0000000000000000000000000000000000000000..4bab0832f13f3eaee8f4fe0e18c7b23866e9fa5b --- /dev/null +++ b/diffsynth/models/svd_unet.py @@ -0,0 +1,2003 @@ +import torch, math +from einops import rearrange, repeat +from .sd_unet import Timesteps, PushBlock, PopBlock, Attention, GEGLU, ResnetBlock, AttentionBlock, DownSampler, UpSampler + + +class TemporalResnetBlock(torch.nn.Module): + def __init__(self, in_channels, out_channels, temb_channels=None, groups=32, eps=1e-5): + super().__init__() + self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) + self.conv1 = torch.nn.Conv3d(in_channels, out_channels, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0)) + if temb_channels is not None: + self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels) + self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True) + self.conv2 = torch.nn.Conv3d(out_channels, out_channels, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0)) + self.nonlinearity = torch.nn.SiLU() + self.conv_shortcut = None + if in_channels != out_channels: + self.conv_shortcut = torch.nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=True) + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + x = rearrange(hidden_states, "f c h w -> 1 c f h w") + x = self.norm1(x) + x = self.nonlinearity(x) + x = self.conv1(x) + if time_emb is not None: + emb = self.nonlinearity(time_emb) + emb = self.time_emb_proj(emb) + emb = repeat(emb, "b c -> b c f 1 1", f=hidden_states.shape[0]) + x = x + emb + x = self.norm2(x) + x = self.nonlinearity(x) + x = self.conv2(x) + if self.conv_shortcut is not None: + hidden_states = self.conv_shortcut(hidden_states) + x = rearrange(x[0], "c f h w -> f c h w") + hidden_states = hidden_states + x + return hidden_states, time_emb, text_emb, res_stack + + +def get_timestep_embedding( + timesteps: torch.Tensor, + embedding_dim: int, + flip_sin_to_cos: bool = False, + downscale_freq_shift: float = 1, + scale: float = 1, + max_period: int = 10000, +): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. + + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the + embeddings. :return: an [N x dim] Tensor of positional embeddings. + """ + assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array" + + half_dim = embedding_dim // 2 + exponent = -math.log(max_period) * torch.arange( + start=0, end=half_dim, dtype=torch.float32, device=timesteps.device + ) + exponent = exponent / (half_dim - downscale_freq_shift) + + emb = torch.exp(exponent) + emb = timesteps[:, None].float() * emb[None, :] + + # scale embeddings + emb = scale * emb + + # concat sine and cosine embeddings + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) + + # flip sine and cosine embeddings + if flip_sin_to_cos: + emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1) + + # zero pad + if embedding_dim % 2 == 1: + emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) + return emb + + +class TemporalTimesteps(torch.nn.Module): + def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float): + super().__init__() + self.num_channels = num_channels + self.flip_sin_to_cos = flip_sin_to_cos + self.downscale_freq_shift = downscale_freq_shift + + def forward(self, timesteps): + t_emb = get_timestep_embedding( + timesteps, + self.num_channels, + flip_sin_to_cos=self.flip_sin_to_cos, + downscale_freq_shift=self.downscale_freq_shift, + ) + return t_emb + + +class TrainableTemporalTimesteps(torch.nn.Module): + def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, num_frames: int): + super().__init__() + timesteps = PositionalID()(num_frames) + embeddings = get_timestep_embedding(timesteps, num_channels, flip_sin_to_cos, downscale_freq_shift) + self.embeddings = torch.nn.Parameter(embeddings) + + def forward(self, timesteps): + t_emb = self.embeddings[timesteps] + return t_emb + + +class PositionalID(torch.nn.Module): + def __init__(self, max_id=25, repeat_length=20): + super().__init__() + self.max_id = max_id + self.repeat_length = repeat_length + + def frame_id_to_position_id(self, frame_id): + if frame_id < self.max_id: + position_id = frame_id + else: + position_id = (frame_id - self.max_id) % (self.repeat_length * 2) + if position_id < self.repeat_length: + position_id = self.max_id - 2 - position_id + else: + position_id = self.max_id - 2 * self.repeat_length + position_id + return position_id + + def forward(self, num_frames, pivot_frame_id=0): + position_ids = [self.frame_id_to_position_id(abs(i-pivot_frame_id)) for i in range(num_frames)] + position_ids = torch.IntTensor(position_ids) + return position_ids + + +class TemporalAttentionBlock(torch.nn.Module): + + def __init__(self, num_attention_heads, attention_head_dim, in_channels, cross_attention_dim=None, add_positional_conv=None): + super().__init__() + + self.positional_embedding_proj = torch.nn.Sequential( + torch.nn.Linear(in_channels, in_channels * 4), + torch.nn.SiLU(), + torch.nn.Linear(in_channels * 4, in_channels) + ) + if add_positional_conv is not None: + self.positional_embedding = TrainableTemporalTimesteps(in_channels, True, 0, add_positional_conv) + self.positional_conv = torch.nn.Conv3d(in_channels, in_channels, kernel_size=3, padding=1, padding_mode="reflect") + else: + self.positional_embedding = TemporalTimesteps(in_channels, True, 0) + self.positional_conv = None + + self.norm_in = torch.nn.LayerNorm(in_channels) + self.act_fn_in = GEGLU(in_channels, in_channels * 4) + self.ff_in = torch.nn.Linear(in_channels * 4, in_channels) + + self.norm1 = torch.nn.LayerNorm(in_channels) + self.attn1 = Attention( + q_dim=in_channels, + num_heads=num_attention_heads, + head_dim=attention_head_dim, + bias_out=True + ) + + self.norm2 = torch.nn.LayerNorm(in_channels) + self.attn2 = Attention( + q_dim=in_channels, + kv_dim=cross_attention_dim, + num_heads=num_attention_heads, + head_dim=attention_head_dim, + bias_out=True + ) + + self.norm_out = torch.nn.LayerNorm(in_channels) + self.act_fn_out = GEGLU(in_channels, in_channels * 4) + self.ff_out = torch.nn.Linear(in_channels * 4, in_channels) + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + + batch, inner_dim, height, width = hidden_states.shape + pos_emb = torch.arange(batch) + pos_emb = self.positional_embedding(pos_emb).to(dtype=hidden_states.dtype, device=hidden_states.device) + pos_emb = self.positional_embedding_proj(pos_emb) + + hidden_states = rearrange(hidden_states, "T C H W -> 1 C T H W") + rearrange(pos_emb, "T C -> 1 C T 1 1") + if self.positional_conv is not None: + hidden_states = self.positional_conv(hidden_states) + hidden_states = rearrange(hidden_states[0], "C T H W -> (H W) T C") + + residual = hidden_states + hidden_states = self.norm_in(hidden_states) + hidden_states = self.act_fn_in(hidden_states) + hidden_states = self.ff_in(hidden_states) + hidden_states = hidden_states + residual + + norm_hidden_states = self.norm1(hidden_states) + attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None) + hidden_states = attn_output + hidden_states + + norm_hidden_states = self.norm2(hidden_states) + attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=text_emb.repeat(height * width, 1)) + hidden_states = attn_output + hidden_states + + residual = hidden_states + hidden_states = self.norm_out(hidden_states) + hidden_states = self.act_fn_out(hidden_states) + hidden_states = self.ff_out(hidden_states) + hidden_states = hidden_states + residual + + hidden_states = hidden_states.reshape(height, width, batch, inner_dim).permute(2, 3, 0, 1) + + return hidden_states, time_emb, text_emb, res_stack + + +class PopMixBlock(torch.nn.Module): + def __init__(self, in_channels=None): + super().__init__() + self.mix_factor = torch.nn.Parameter(torch.Tensor([0.5])) + self.need_proj = in_channels is not None + if self.need_proj: + self.proj = torch.nn.Linear(in_channels, in_channels) + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + res_hidden_states = res_stack.pop() + alpha = torch.sigmoid(self.mix_factor) + hidden_states = alpha * res_hidden_states + (1 - alpha) * hidden_states + if self.need_proj: + hidden_states = hidden_states.permute(0, 2, 3, 1) + hidden_states = self.proj(hidden_states) + hidden_states = hidden_states.permute(0, 3, 1, 2) + res_hidden_states = res_stack.pop() + hidden_states = hidden_states + res_hidden_states + return hidden_states, time_emb, text_emb, res_stack + + +class SVDUNet(torch.nn.Module): + def __init__(self, add_positional_conv=None): + super().__init__() + self.time_proj = Timesteps(320) + self.time_embedding = torch.nn.Sequential( + torch.nn.Linear(320, 1280), + torch.nn.SiLU(), + torch.nn.Linear(1280, 1280) + ) + self.add_time_proj = Timesteps(256) + self.add_time_embedding = torch.nn.Sequential( + torch.nn.Linear(768, 1280), + torch.nn.SiLU(), + torch.nn.Linear(1280, 1280) + ) + self.conv_in = torch.nn.Conv2d(8, 320, kernel_size=3, padding=1) + + self.blocks = torch.nn.ModuleList([ + # CrossAttnDownBlockSpatioTemporal + ResnetBlock(320, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), PushBlock(), + ResnetBlock(320, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), PushBlock(), + DownSampler(320), PushBlock(), + # CrossAttnDownBlockSpatioTemporal + ResnetBlock(320, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), PushBlock(), + ResnetBlock(640, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), PushBlock(), + DownSampler(640), PushBlock(), + # CrossAttnDownBlockSpatioTemporal + ResnetBlock(640, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), PushBlock(), + ResnetBlock(1280, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), PushBlock(), + DownSampler(1280), PushBlock(), + # DownBlockSpatioTemporal + ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), PushBlock(), + ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), PushBlock(), + # UNetMidBlockSpatioTemporal + ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), + ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), + # UpBlockSpatioTemporal + PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), + PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), + PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), + UpSampler(1280), + # CrossAttnUpBlockSpatioTemporal + PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), + PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), + PopBlock(), ResnetBlock(1920, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), + UpSampler(1280), + # CrossAttnUpBlockSpatioTemporal + PopBlock(), ResnetBlock(1920, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), + PopBlock(), ResnetBlock(1280, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), + PopBlock(), ResnetBlock(960, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), + UpSampler(640), + # CrossAttnUpBlockSpatioTemporal + PopBlock(), ResnetBlock(960, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), + PopBlock(), ResnetBlock(640, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), + PopBlock(), ResnetBlock(640, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), + ]) + + self.conv_norm_out = torch.nn.GroupNorm(32, 320, eps=1e-05, affine=True) + self.conv_act = torch.nn.SiLU() + self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + + + def build_mask(self, data, is_bound): + T, C, H, W = data.shape + t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W) + h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W) + w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W) + border_width = (T + H + W) // 6 + pad = torch.ones_like(t) * border_width + mask = torch.stack([ + pad if is_bound[0] else t + 1, + pad if is_bound[1] else T - t, + pad if is_bound[2] else h + 1, + pad if is_bound[3] else H - h, + pad if is_bound[4] else w + 1, + pad if is_bound[5] else W - w + ]).min(dim=0).values + mask = mask.clip(1, border_width) + mask = (mask / border_width).to(dtype=data.dtype, device=data.device) + mask = rearrange(mask, "T H W -> T 1 H W") + return mask + + + def tiled_forward( + self, sample, timestep, encoder_hidden_states, add_time_id, + batch_time=25, batch_height=128, batch_width=128, + stride_time=5, stride_height=64, stride_width=64, + progress_bar=lambda x:x + ): + data_device = sample.device + computation_device = self.conv_in.weight.device + torch_dtype = sample.dtype + T, C, H, W = sample.shape + + weight = torch.zeros((T, 1, H, W), dtype=torch_dtype, device=data_device) + values = torch.zeros((T, 4, H, W), dtype=torch_dtype, device=data_device) + + # Split tasks + tasks = [] + for t in range(0, T, stride_time): + for h in range(0, H, stride_height): + for w in range(0, W, stride_width): + if (t-stride_time >= 0 and t-stride_time+batch_time >= T)\ + or (h-stride_height >= 0 and h-stride_height+batch_height >= H)\ + or (w-stride_width >= 0 and w-stride_width+batch_width >= W): + continue + tasks.append((t, t+batch_time, h, h+batch_height, w, w+batch_width)) + + # Run + for tl, tr, hl, hr, wl, wr in progress_bar(tasks): + sample_batch = sample[tl:tr, :, hl:hr, wl:wr].to(computation_device) + sample_batch = self.forward(sample_batch, timestep, encoder_hidden_states, add_time_id).to(data_device) + mask = self.build_mask(sample_batch, is_bound=(tl==0, tr>=T, hl==0, hr>=H, wl==0, wr>=W)) + values[tl:tr, :, hl:hr, wl:wr] += sample_batch * mask + weight[tl:tr, :, hl:hr, wl:wr] += mask + values /= weight + return values + + + def forward(self, sample, timestep, encoder_hidden_states, add_time_id, use_gradient_checkpointing=False, **kwargs): + # 1. time + timestep = torch.tensor((timestep,)).to(sample.device) + t_emb = self.time_proj(timestep).to(sample.dtype) + t_emb = self.time_embedding(t_emb) + + add_embeds = self.add_time_proj(add_time_id.flatten()).to(sample.dtype) + add_embeds = add_embeds.reshape((-1, 768)) + add_embeds = self.add_time_embedding(add_embeds) + + time_emb = t_emb + add_embeds + + # 2. pre-process + height, width = sample.shape[2], sample.shape[3] + hidden_states = self.conv_in(sample) + text_emb = encoder_hidden_states + res_stack = [hidden_states] + + # 3. blocks + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + for i, block in enumerate(self.blocks): + if self.training and use_gradient_checkpointing and not (isinstance(block, PushBlock) or isinstance(block, PopBlock) or isinstance(block, PopMixBlock)): + hidden_states, time_emb, text_emb, res_stack = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, time_emb, text_emb, res_stack, + use_reentrant=False, + ) + else: + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + + # 4. output + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + + return hidden_states + + def state_dict_converter(self): + return SVDUNetStateDictConverter() + + + +class SVDUNetStateDictConverter: + def __init__(self): + pass + + def get_block_name(self, names): + if names[0] in ["down_blocks", "mid_block", "up_blocks"]: + if names[4] in ["norm", "proj_in"]: + return ".".join(names[:4] + ["transformer_blocks"]) + elif names[4] in ["time_pos_embed"]: + return ".".join(names[:4] + ["temporal_transformer_blocks"]) + elif names[4] in ["proj_out"]: + return ".".join(names[:4] + ["time_mixer"]) + else: + return ".".join(names[:5]) + return "" + + def from_diffusers(self, state_dict): + rename_dict = { + "time_embedding.linear_1": "time_embedding.0", + "time_embedding.linear_2": "time_embedding.2", + "add_embedding.linear_1": "add_time_embedding.0", + "add_embedding.linear_2": "add_time_embedding.2", + "conv_in": "conv_in", + "conv_norm_out": "conv_norm_out", + "conv_out": "conv_out", + } + blocks_rename_dict = [ + "down_blocks.0.resnets.0.spatial_res_block", None, "down_blocks.0.resnets.0.temporal_res_block", "down_blocks.0.resnets.0.time_mixer", None, + "down_blocks.0.attentions.0.transformer_blocks", None, "down_blocks.0.attentions.0.temporal_transformer_blocks", "down_blocks.0.attentions.0.time_mixer", None, + "down_blocks.0.resnets.1.spatial_res_block", None, "down_blocks.0.resnets.1.temporal_res_block", "down_blocks.0.resnets.1.time_mixer", None, + "down_blocks.0.attentions.1.transformer_blocks", None, "down_blocks.0.attentions.1.temporal_transformer_blocks", "down_blocks.0.attentions.1.time_mixer", None, + "down_blocks.0.downsamplers.0.conv", None, + "down_blocks.1.resnets.0.spatial_res_block", None, "down_blocks.1.resnets.0.temporal_res_block", "down_blocks.1.resnets.0.time_mixer", None, + "down_blocks.1.attentions.0.transformer_blocks", None, "down_blocks.1.attentions.0.temporal_transformer_blocks", "down_blocks.1.attentions.0.time_mixer", None, + "down_blocks.1.resnets.1.spatial_res_block", None, "down_blocks.1.resnets.1.temporal_res_block", "down_blocks.1.resnets.1.time_mixer", None, + "down_blocks.1.attentions.1.transformer_blocks", None, "down_blocks.1.attentions.1.temporal_transformer_blocks", "down_blocks.1.attentions.1.time_mixer", None, + "down_blocks.1.downsamplers.0.conv", None, + "down_blocks.2.resnets.0.spatial_res_block", None, "down_blocks.2.resnets.0.temporal_res_block", "down_blocks.2.resnets.0.time_mixer", None, + "down_blocks.2.attentions.0.transformer_blocks", None, "down_blocks.2.attentions.0.temporal_transformer_blocks", "down_blocks.2.attentions.0.time_mixer", None, + "down_blocks.2.resnets.1.spatial_res_block", None, "down_blocks.2.resnets.1.temporal_res_block", "down_blocks.2.resnets.1.time_mixer", None, + "down_blocks.2.attentions.1.transformer_blocks", None, "down_blocks.2.attentions.1.temporal_transformer_blocks", "down_blocks.2.attentions.1.time_mixer", None, + "down_blocks.2.downsamplers.0.conv", None, + "down_blocks.3.resnets.0.spatial_res_block", None, "down_blocks.3.resnets.0.temporal_res_block", "down_blocks.3.resnets.0.time_mixer", None, + "down_blocks.3.resnets.1.spatial_res_block", None, "down_blocks.3.resnets.1.temporal_res_block", "down_blocks.3.resnets.1.time_mixer", None, + "mid_block.mid_block.resnets.0.spatial_res_block", None, "mid_block.mid_block.resnets.0.temporal_res_block", "mid_block.mid_block.resnets.0.time_mixer", None, + "mid_block.mid_block.attentions.0.transformer_blocks", None, "mid_block.mid_block.attentions.0.temporal_transformer_blocks", "mid_block.mid_block.attentions.0.time_mixer", + "mid_block.mid_block.resnets.1.spatial_res_block", None, "mid_block.mid_block.resnets.1.temporal_res_block", "mid_block.mid_block.resnets.1.time_mixer", + None, "up_blocks.0.resnets.0.spatial_res_block", None, "up_blocks.0.resnets.0.temporal_res_block", "up_blocks.0.resnets.0.time_mixer", + None, "up_blocks.0.resnets.1.spatial_res_block", None, "up_blocks.0.resnets.1.temporal_res_block", "up_blocks.0.resnets.1.time_mixer", + None, "up_blocks.0.resnets.2.spatial_res_block", None, "up_blocks.0.resnets.2.temporal_res_block", "up_blocks.0.resnets.2.time_mixer", + "up_blocks.0.upsamplers.0.conv", + None, "up_blocks.1.resnets.0.spatial_res_block", None, "up_blocks.1.resnets.0.temporal_res_block", "up_blocks.1.resnets.0.time_mixer", None, + "up_blocks.1.attentions.0.transformer_blocks", None, "up_blocks.1.attentions.0.temporal_transformer_blocks", "up_blocks.1.attentions.0.time_mixer", + None, "up_blocks.1.resnets.1.spatial_res_block", None, "up_blocks.1.resnets.1.temporal_res_block", "up_blocks.1.resnets.1.time_mixer", None, + "up_blocks.1.attentions.1.transformer_blocks", None, "up_blocks.1.attentions.1.temporal_transformer_blocks", "up_blocks.1.attentions.1.time_mixer", + None, "up_blocks.1.resnets.2.spatial_res_block", None, "up_blocks.1.resnets.2.temporal_res_block", "up_blocks.1.resnets.2.time_mixer", None, + "up_blocks.1.attentions.2.transformer_blocks", None, "up_blocks.1.attentions.2.temporal_transformer_blocks", "up_blocks.1.attentions.2.time_mixer", + "up_blocks.1.upsamplers.0.conv", + None, "up_blocks.2.resnets.0.spatial_res_block", None, "up_blocks.2.resnets.0.temporal_res_block", "up_blocks.2.resnets.0.time_mixer", None, + "up_blocks.2.attentions.0.transformer_blocks", None, "up_blocks.2.attentions.0.temporal_transformer_blocks", "up_blocks.2.attentions.0.time_mixer", + None, "up_blocks.2.resnets.1.spatial_res_block", None, "up_blocks.2.resnets.1.temporal_res_block", "up_blocks.2.resnets.1.time_mixer", None, + "up_blocks.2.attentions.1.transformer_blocks", None, "up_blocks.2.attentions.1.temporal_transformer_blocks", "up_blocks.2.attentions.1.time_mixer", + None, "up_blocks.2.resnets.2.spatial_res_block", None, "up_blocks.2.resnets.2.temporal_res_block", "up_blocks.2.resnets.2.time_mixer", None, + "up_blocks.2.attentions.2.transformer_blocks", None, "up_blocks.2.attentions.2.temporal_transformer_blocks", "up_blocks.2.attentions.2.time_mixer", + "up_blocks.2.upsamplers.0.conv", + None, "up_blocks.3.resnets.0.spatial_res_block", None, "up_blocks.3.resnets.0.temporal_res_block", "up_blocks.3.resnets.0.time_mixer", None, + "up_blocks.3.attentions.0.transformer_blocks", None, "up_blocks.3.attentions.0.temporal_transformer_blocks", "up_blocks.3.attentions.0.time_mixer", + None, "up_blocks.3.resnets.1.spatial_res_block", None, "up_blocks.3.resnets.1.temporal_res_block", "up_blocks.3.resnets.1.time_mixer", None, + "up_blocks.3.attentions.1.transformer_blocks", None, "up_blocks.3.attentions.1.temporal_transformer_blocks", "up_blocks.3.attentions.1.time_mixer", + None, "up_blocks.3.resnets.2.spatial_res_block", None, "up_blocks.3.resnets.2.temporal_res_block", "up_blocks.3.resnets.2.time_mixer", None, + "up_blocks.3.attentions.2.transformer_blocks", None, "up_blocks.3.attentions.2.temporal_transformer_blocks", "up_blocks.3.attentions.2.time_mixer", + ] + blocks_rename_dict = {i:j for j,i in enumerate(blocks_rename_dict) if i is not None} + state_dict_ = {} + for name, param in sorted(state_dict.items()): + names = name.split(".") + if names[0] == "mid_block": + names = ["mid_block"] + names + if names[-1] in ["weight", "bias"]: + name_prefix = ".".join(names[:-1]) + if name_prefix in rename_dict: + state_dict_[rename_dict[name_prefix] + "." + names[-1]] = param + else: + block_name = self.get_block_name(names) + if "resnets" in block_name and block_name in blocks_rename_dict: + rename = ".".join(["blocks", str(blocks_rename_dict[block_name])] + names[5:]) + state_dict_[rename] = param + elif ("downsamplers" in block_name or "upsamplers" in block_name) and block_name in blocks_rename_dict: + rename = ".".join(["blocks", str(blocks_rename_dict[block_name])] + names[-2:]) + state_dict_[rename] = param + elif "attentions" in block_name and block_name in blocks_rename_dict: + attention_id = names[5] + if "transformer_blocks" in names: + suffix_dict = { + "attn1.to_out.0": "attn1.to_out", + "attn2.to_out.0": "attn2.to_out", + "ff.net.0.proj": "act_fn.proj", + "ff.net.2": "ff", + } + suffix = ".".join(names[6:-1]) + suffix = suffix_dict.get(suffix, suffix) + rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), "transformer_blocks", attention_id, suffix, names[-1]]) + elif "temporal_transformer_blocks" in names: + suffix_dict = { + "attn1.to_out.0": "attn1.to_out", + "attn2.to_out.0": "attn2.to_out", + "ff_in.net.0.proj": "act_fn_in.proj", + "ff_in.net.2": "ff_in", + "ff.net.0.proj": "act_fn_out.proj", + "ff.net.2": "ff_out", + "norm3": "norm_out", + } + suffix = ".".join(names[6:-1]) + suffix = suffix_dict.get(suffix, suffix) + rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), suffix, names[-1]]) + elif "time_mixer" in block_name: + rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), "proj", names[-1]]) + else: + suffix_dict = { + "linear_1": "positional_embedding_proj.0", + "linear_2": "positional_embedding_proj.2", + } + suffix = names[-2] + suffix = suffix_dict.get(suffix, suffix) + rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), suffix, names[-1]]) + state_dict_[rename] = param + else: + print(name) + else: + block_name = self.get_block_name(names) + if len(block_name)>0 and block_name in blocks_rename_dict: + rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), names[-1]]) + state_dict_[rename] = param + return state_dict_ + + + def from_civitai(self, state_dict, add_positional_conv=None): + rename_dict = { + "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias", + "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight", + "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias", + "model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight", + "model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias", + "model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight", + "model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias", + "model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight", + "model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias", + "model.diffusion_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight", + "model.diffusion_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias", + "model.diffusion_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight", + "model.diffusion_model.input_blocks.1.0.time_mixer.mix_factor": "blocks.3.mix_factor", + "model.diffusion_model.input_blocks.1.0.time_stack.emb_layers.1.bias": "blocks.2.time_emb_proj.bias", + "model.diffusion_model.input_blocks.1.0.time_stack.emb_layers.1.weight": "blocks.2.time_emb_proj.weight", + "model.diffusion_model.input_blocks.1.0.time_stack.in_layers.0.bias": "blocks.2.norm1.bias", + "model.diffusion_model.input_blocks.1.0.time_stack.in_layers.0.weight": "blocks.2.norm1.weight", + "model.diffusion_model.input_blocks.1.0.time_stack.in_layers.2.bias": "blocks.2.conv1.bias", + "model.diffusion_model.input_blocks.1.0.time_stack.in_layers.2.weight": "blocks.2.conv1.weight", + "model.diffusion_model.input_blocks.1.0.time_stack.out_layers.0.bias": "blocks.2.norm2.bias", + "model.diffusion_model.input_blocks.1.0.time_stack.out_layers.0.weight": "blocks.2.norm2.weight", + "model.diffusion_model.input_blocks.1.0.time_stack.out_layers.3.bias": "blocks.2.conv2.bias", + "model.diffusion_model.input_blocks.1.0.time_stack.out_layers.3.weight": "blocks.2.conv2.weight", + "model.diffusion_model.input_blocks.1.1.norm.bias": "blocks.5.norm.bias", + "model.diffusion_model.input_blocks.1.1.norm.weight": "blocks.5.norm.weight", + "model.diffusion_model.input_blocks.1.1.proj_in.bias": "blocks.5.proj_in.bias", + "model.diffusion_model.input_blocks.1.1.proj_in.weight": "blocks.5.proj_in.weight", + "model.diffusion_model.input_blocks.1.1.proj_out.bias": "blocks.8.proj.bias", + "model.diffusion_model.input_blocks.1.1.proj_out.weight": "blocks.8.proj.weight", + "model.diffusion_model.input_blocks.1.1.time_mixer.mix_factor": "blocks.8.mix_factor", + "model.diffusion_model.input_blocks.1.1.time_pos_embed.0.bias": "blocks.7.positional_embedding_proj.0.bias", + "model.diffusion_model.input_blocks.1.1.time_pos_embed.0.weight": "blocks.7.positional_embedding_proj.0.weight", + "model.diffusion_model.input_blocks.1.1.time_pos_embed.2.bias": "blocks.7.positional_embedding_proj.2.bias", + "model.diffusion_model.input_blocks.1.1.time_pos_embed.2.weight": "blocks.7.positional_embedding_proj.2.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn1.to_k.weight": "blocks.7.attn1.to_k.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn1.to_out.0.bias": "blocks.7.attn1.to_out.bias", 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"blocks.173.transformer_blocks.0.ff.bias", + "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.2.weight": "blocks.173.transformer_blocks.0.ff.weight", + "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm1.bias": "blocks.173.transformer_blocks.0.norm1.bias", + "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm1.weight": "blocks.173.transformer_blocks.0.norm1.weight", + "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm2.bias": "blocks.173.transformer_blocks.0.norm2.bias", + "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm2.weight": "blocks.173.transformer_blocks.0.norm2.weight", + "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.bias": "blocks.173.transformer_blocks.0.norm3.bias", + "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.weight": "blocks.173.transformer_blocks.0.norm3.weight", + "model.diffusion_model.time_embed.0.bias": "time_embedding.0.bias", + "model.diffusion_model.time_embed.0.weight": "time_embedding.0.weight", + "model.diffusion_model.time_embed.2.bias": "time_embedding.2.bias", + "model.diffusion_model.time_embed.2.weight": "time_embedding.2.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if ".proj_in." in name or ".proj_out." in name: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + if add_positional_conv is not None: + extra_names = [ + "blocks.7.positional_conv", "blocks.17.positional_conv", "blocks.29.positional_conv", "blocks.39.positional_conv", + "blocks.51.positional_conv", "blocks.61.positional_conv", "blocks.83.positional_conv", "blocks.113.positional_conv", + "blocks.123.positional_conv", "blocks.133.positional_conv", "blocks.144.positional_conv", "blocks.154.positional_conv", + "blocks.164.positional_conv", "blocks.175.positional_conv", "blocks.185.positional_conv", "blocks.195.positional_conv", + ] + extra_channels = [320, 320, 640, 640, 1280, 1280, 1280, 1280, 1280, 1280, 640, 640, 640, 320, 320, 320] + for name, channels in zip(extra_names, extra_channels): + weight = torch.zeros((channels, channels, 3, 3, 3)) + weight[:,:,1,1,1] = torch.eye(channels, channels) + bias = torch.zeros((channels,)) + state_dict_[name + ".weight"] = weight + state_dict_[name + ".bias"] = bias + return state_dict_ diff --git a/diffsynth/models/svd_vae_decoder.py b/diffsynth/models/svd_vae_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..3fe6446854cfb3252096c564daedaabc3d201068 --- /dev/null +++ b/diffsynth/models/svd_vae_decoder.py @@ -0,0 +1,577 @@ +import torch +from .attention import Attention +from .sd_unet import ResnetBlock, UpSampler +from .tiler import TileWorker +from einops import rearrange, repeat + + +class VAEAttentionBlock(torch.nn.Module): + + def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5): + super().__init__() + inner_dim = num_attention_heads * attention_head_dim + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True) + + self.transformer_blocks = torch.nn.ModuleList([ + Attention( + inner_dim, + num_attention_heads, + attention_head_dim, + bias_q=True, + bias_kv=True, + bias_out=True + ) + for d in range(num_layers) + ]) + + def forward(self, hidden_states, time_emb, text_emb, res_stack): + batch, _, height, width = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + + for block in self.transformer_blocks: + hidden_states = block(hidden_states) + + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + hidden_states = hidden_states + residual + + return hidden_states, time_emb, text_emb, res_stack + + +class TemporalResnetBlock(torch.nn.Module): + + def __init__(self, in_channels, out_channels, groups=32, eps=1e-5): + super().__init__() + self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) + self.conv1 = torch.nn.Conv3d(in_channels, out_channels, kernel_size=(3, 1, 1), stride=1, padding=(1, 0, 0)) + self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True) + self.conv2 = torch.nn.Conv3d(out_channels, out_channels, kernel_size=(3, 1, 1), stride=1, padding=(1, 0, 0)) + self.nonlinearity = torch.nn.SiLU() + self.mix_factor = torch.nn.Parameter(torch.Tensor([0.5])) + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + x_spatial = hidden_states + x = rearrange(hidden_states, "T C H W -> 1 C T H W") + x = self.norm1(x) + x = self.nonlinearity(x) + x = self.conv1(x) + x = self.norm2(x) + x = self.nonlinearity(x) + x = self.conv2(x) + x_temporal = hidden_states + x[0].permute(1, 0, 2, 3) + alpha = torch.sigmoid(self.mix_factor) + hidden_states = alpha * x_temporal + (1 - alpha) * x_spatial + return hidden_states, time_emb, text_emb, res_stack + + +class SVDVAEDecoder(torch.nn.Module): + def __init__(self): + super().__init__() + self.scaling_factor = 0.18215 + self.conv_in = torch.nn.Conv2d(4, 512, kernel_size=3, padding=1) + + self.blocks = torch.nn.ModuleList([ + # UNetMidBlock + ResnetBlock(512, 512, eps=1e-6), + TemporalResnetBlock(512, 512, eps=1e-6), + VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + TemporalResnetBlock(512, 512, eps=1e-6), + # UpDecoderBlock + ResnetBlock(512, 512, eps=1e-6), + TemporalResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + TemporalResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + TemporalResnetBlock(512, 512, eps=1e-6), + UpSampler(512), + # UpDecoderBlock + ResnetBlock(512, 512, eps=1e-6), + TemporalResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + TemporalResnetBlock(512, 512, eps=1e-6), + ResnetBlock(512, 512, eps=1e-6), + TemporalResnetBlock(512, 512, eps=1e-6), + UpSampler(512), + # UpDecoderBlock + ResnetBlock(512, 256, eps=1e-6), + TemporalResnetBlock(256, 256, eps=1e-6), + ResnetBlock(256, 256, eps=1e-6), + TemporalResnetBlock(256, 256, eps=1e-6), + ResnetBlock(256, 256, eps=1e-6), + TemporalResnetBlock(256, 256, eps=1e-6), + UpSampler(256), + # UpDecoderBlock + ResnetBlock(256, 128, eps=1e-6), + TemporalResnetBlock(128, 128, eps=1e-6), + ResnetBlock(128, 128, eps=1e-6), + TemporalResnetBlock(128, 128, eps=1e-6), + ResnetBlock(128, 128, eps=1e-6), + TemporalResnetBlock(128, 128, eps=1e-6), + ]) + + self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-5) + self.conv_act = torch.nn.SiLU() + self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1) + self.time_conv_out = torch.nn.Conv3d(3, 3, kernel_size=(3, 1, 1), padding=(1, 0, 0)) + + + def forward(self, sample): + # 1. pre-process + hidden_states = rearrange(sample, "C T H W -> T C H W") + hidden_states = hidden_states / self.scaling_factor + hidden_states = self.conv_in(hidden_states) + time_emb, text_emb, res_stack = None, None, None + + # 2. blocks + for i, block in enumerate(self.blocks): + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + + # 3. output + hidden_states = self.conv_norm_out(hidden_states) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + hidden_states = rearrange(hidden_states, "T C H W -> C T H W") + hidden_states = self.time_conv_out(hidden_states) + + return hidden_states + + + def build_mask(self, data, is_bound): + _, T, H, W = data.shape + t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W) + h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W) + w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W) + border_width = (T + H + W) // 6 + pad = torch.ones_like(t) * border_width + mask = torch.stack([ + pad if is_bound[0] else t + 1, + pad if is_bound[1] else T - t, + pad if is_bound[2] else h + 1, + pad if is_bound[3] else H - h, + pad if is_bound[4] else w + 1, + pad if is_bound[5] else W - w + ]).min(dim=0).values + mask = mask.clip(1, border_width) + mask = (mask / border_width).to(dtype=data.dtype, device=data.device) + mask = rearrange(mask, "T H W -> 1 T H W") + return mask + + + def decode_video( + self, sample, + batch_time=8, batch_height=128, batch_width=128, + stride_time=4, stride_height=32, stride_width=32, + progress_bar=lambda x:x + ): + sample = sample.permute(1, 0, 2, 3) + data_device = sample.device + computation_device = self.conv_in.weight.device + torch_dtype = sample.dtype + _, T, H, W = sample.shape + + weight = torch.zeros((1, T, H*8, W*8), dtype=torch_dtype, device=data_device) + values = torch.zeros((3, T, H*8, W*8), dtype=torch_dtype, device=data_device) + + # Split tasks + tasks = [] + for t in range(0, T, stride_time): + for h in range(0, H, stride_height): + for w in range(0, W, stride_width): + if (t-stride_time >= 0 and t-stride_time+batch_time >= T)\ + or (h-stride_height >= 0 and h-stride_height+batch_height >= H)\ + or (w-stride_width >= 0 and w-stride_width+batch_width >= W): + continue + tasks.append((t, t+batch_time, h, h+batch_height, w, w+batch_width)) + + # Run + for tl, tr, hl, hr, wl, wr in progress_bar(tasks): + sample_batch = sample[:, tl:tr, hl:hr, wl:wr].to(computation_device) + sample_batch = self.forward(sample_batch).to(data_device) + mask = self.build_mask(sample_batch, is_bound=(tl==0, tr>=T, hl==0, hr>=H, wl==0, wr>=W)) + values[:, tl:tr, hl*8:hr*8, wl*8:wr*8] += sample_batch * mask + weight[:, tl:tr, hl*8:hr*8, wl*8:wr*8] += mask + values /= weight + return values + + + def state_dict_converter(self): + return SVDVAEDecoderStateDictConverter() + + +class SVDVAEDecoderStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + static_rename_dict = { + "decoder.conv_in": "conv_in", + "decoder.mid_block.attentions.0.group_norm": "blocks.2.norm", + "decoder.mid_block.attentions.0.to_q": "blocks.2.transformer_blocks.0.to_q", + "decoder.mid_block.attentions.0.to_k": "blocks.2.transformer_blocks.0.to_k", + "decoder.mid_block.attentions.0.to_v": "blocks.2.transformer_blocks.0.to_v", + "decoder.mid_block.attentions.0.to_out.0": "blocks.2.transformer_blocks.0.to_out", + "decoder.up_blocks.0.upsamplers.0.conv": "blocks.11.conv", + "decoder.up_blocks.1.upsamplers.0.conv": "blocks.18.conv", + "decoder.up_blocks.2.upsamplers.0.conv": "blocks.25.conv", + "decoder.conv_norm_out": "conv_norm_out", + "decoder.conv_out": "conv_out", + "decoder.time_conv_out": "time_conv_out" + } + prefix_rename_dict = { + "decoder.mid_block.resnets.0.spatial_res_block": "blocks.0", + "decoder.mid_block.resnets.0.temporal_res_block": "blocks.1", + "decoder.mid_block.resnets.0.time_mixer": "blocks.1", + "decoder.mid_block.resnets.1.spatial_res_block": "blocks.3", + "decoder.mid_block.resnets.1.temporal_res_block": "blocks.4", + "decoder.mid_block.resnets.1.time_mixer": "blocks.4", + + "decoder.up_blocks.0.resnets.0.spatial_res_block": "blocks.5", + "decoder.up_blocks.0.resnets.0.temporal_res_block": "blocks.6", + "decoder.up_blocks.0.resnets.0.time_mixer": "blocks.6", + "decoder.up_blocks.0.resnets.1.spatial_res_block": "blocks.7", + "decoder.up_blocks.0.resnets.1.temporal_res_block": "blocks.8", + "decoder.up_blocks.0.resnets.1.time_mixer": "blocks.8", + "decoder.up_blocks.0.resnets.2.spatial_res_block": "blocks.9", + "decoder.up_blocks.0.resnets.2.temporal_res_block": "blocks.10", + "decoder.up_blocks.0.resnets.2.time_mixer": "blocks.10", + + "decoder.up_blocks.1.resnets.0.spatial_res_block": "blocks.12", + "decoder.up_blocks.1.resnets.0.temporal_res_block": "blocks.13", + "decoder.up_blocks.1.resnets.0.time_mixer": "blocks.13", + "decoder.up_blocks.1.resnets.1.spatial_res_block": "blocks.14", + "decoder.up_blocks.1.resnets.1.temporal_res_block": "blocks.15", + "decoder.up_blocks.1.resnets.1.time_mixer": "blocks.15", + "decoder.up_blocks.1.resnets.2.spatial_res_block": "blocks.16", + "decoder.up_blocks.1.resnets.2.temporal_res_block": "blocks.17", + "decoder.up_blocks.1.resnets.2.time_mixer": "blocks.17", + + "decoder.up_blocks.2.resnets.0.spatial_res_block": "blocks.19", + "decoder.up_blocks.2.resnets.0.temporal_res_block": "blocks.20", + "decoder.up_blocks.2.resnets.0.time_mixer": "blocks.20", + "decoder.up_blocks.2.resnets.1.spatial_res_block": "blocks.21", + "decoder.up_blocks.2.resnets.1.temporal_res_block": "blocks.22", + "decoder.up_blocks.2.resnets.1.time_mixer": "blocks.22", + "decoder.up_blocks.2.resnets.2.spatial_res_block": "blocks.23", + "decoder.up_blocks.2.resnets.2.temporal_res_block": "blocks.24", + "decoder.up_blocks.2.resnets.2.time_mixer": "blocks.24", + + "decoder.up_blocks.3.resnets.0.spatial_res_block": "blocks.26", + "decoder.up_blocks.3.resnets.0.temporal_res_block": "blocks.27", + "decoder.up_blocks.3.resnets.0.time_mixer": "blocks.27", + "decoder.up_blocks.3.resnets.1.spatial_res_block": "blocks.28", + "decoder.up_blocks.3.resnets.1.temporal_res_block": "blocks.29", + "decoder.up_blocks.3.resnets.1.time_mixer": "blocks.29", + "decoder.up_blocks.3.resnets.2.spatial_res_block": "blocks.30", + "decoder.up_blocks.3.resnets.2.temporal_res_block": "blocks.31", + "decoder.up_blocks.3.resnets.2.time_mixer": "blocks.31", + } + suffix_rename_dict = { + "norm1.weight": "norm1.weight", + "conv1.weight": "conv1.weight", + "norm2.weight": "norm2.weight", + "conv2.weight": "conv2.weight", + "conv_shortcut.weight": "conv_shortcut.weight", + "norm1.bias": "norm1.bias", + "conv1.bias": "conv1.bias", + "norm2.bias": "norm2.bias", + "conv2.bias": "conv2.bias", + "conv_shortcut.bias": "conv_shortcut.bias", + "mix_factor": "mix_factor", + } + + state_dict_ = {} + for name in static_rename_dict: + state_dict_[static_rename_dict[name] + ".weight"] = state_dict[name + ".weight"] + state_dict_[static_rename_dict[name] + ".bias"] = state_dict[name + ".bias"] + for prefix_name in prefix_rename_dict: + for suffix_name in suffix_rename_dict: + name = prefix_name + "." + suffix_name + name_ = prefix_rename_dict[prefix_name] + "." + suffix_rename_dict[suffix_name] + if name in state_dict: + state_dict_[name_] = state_dict[name] + + return state_dict_ + + + def from_civitai(self, state_dict): + rename_dict = { + "first_stage_model.decoder.conv_in.bias": "conv_in.bias", + "first_stage_model.decoder.conv_in.weight": "conv_in.weight", + "first_stage_model.decoder.conv_out.bias": "conv_out.bias", + "first_stage_model.decoder.conv_out.time_mix_conv.bias": "time_conv_out.bias", + "first_stage_model.decoder.conv_out.time_mix_conv.weight": "time_conv_out.weight", + "first_stage_model.decoder.conv_out.weight": "conv_out.weight", + "first_stage_model.decoder.mid.attn_1.k.bias": "blocks.2.transformer_blocks.0.to_k.bias", + "first_stage_model.decoder.mid.attn_1.k.weight": "blocks.2.transformer_blocks.0.to_k.weight", + "first_stage_model.decoder.mid.attn_1.norm.bias": "blocks.2.norm.bias", + "first_stage_model.decoder.mid.attn_1.norm.weight": "blocks.2.norm.weight", + "first_stage_model.decoder.mid.attn_1.proj_out.bias": "blocks.2.transformer_blocks.0.to_out.bias", + "first_stage_model.decoder.mid.attn_1.proj_out.weight": "blocks.2.transformer_blocks.0.to_out.weight", + "first_stage_model.decoder.mid.attn_1.q.bias": "blocks.2.transformer_blocks.0.to_q.bias", + "first_stage_model.decoder.mid.attn_1.q.weight": "blocks.2.transformer_blocks.0.to_q.weight", + "first_stage_model.decoder.mid.attn_1.v.bias": "blocks.2.transformer_blocks.0.to_v.bias", + "first_stage_model.decoder.mid.attn_1.v.weight": "blocks.2.transformer_blocks.0.to_v.weight", + "first_stage_model.decoder.mid.block_1.conv1.bias": "blocks.0.conv1.bias", + "first_stage_model.decoder.mid.block_1.conv1.weight": "blocks.0.conv1.weight", + "first_stage_model.decoder.mid.block_1.conv2.bias": "blocks.0.conv2.bias", + "first_stage_model.decoder.mid.block_1.conv2.weight": "blocks.0.conv2.weight", + "first_stage_model.decoder.mid.block_1.mix_factor": "blocks.1.mix_factor", + "first_stage_model.decoder.mid.block_1.norm1.bias": "blocks.0.norm1.bias", + "first_stage_model.decoder.mid.block_1.norm1.weight": "blocks.0.norm1.weight", + "first_stage_model.decoder.mid.block_1.norm2.bias": "blocks.0.norm2.bias", + "first_stage_model.decoder.mid.block_1.norm2.weight": "blocks.0.norm2.weight", + "first_stage_model.decoder.mid.block_1.time_stack.in_layers.0.bias": "blocks.1.norm1.bias", + "first_stage_model.decoder.mid.block_1.time_stack.in_layers.0.weight": "blocks.1.norm1.weight", + "first_stage_model.decoder.mid.block_1.time_stack.in_layers.2.bias": "blocks.1.conv1.bias", + "first_stage_model.decoder.mid.block_1.time_stack.in_layers.2.weight": "blocks.1.conv1.weight", + "first_stage_model.decoder.mid.block_1.time_stack.out_layers.0.bias": "blocks.1.norm2.bias", + "first_stage_model.decoder.mid.block_1.time_stack.out_layers.0.weight": "blocks.1.norm2.weight", + "first_stage_model.decoder.mid.block_1.time_stack.out_layers.3.bias": "blocks.1.conv2.bias", + "first_stage_model.decoder.mid.block_1.time_stack.out_layers.3.weight": "blocks.1.conv2.weight", + "first_stage_model.decoder.mid.block_2.conv1.bias": "blocks.3.conv1.bias", + "first_stage_model.decoder.mid.block_2.conv1.weight": "blocks.3.conv1.weight", + "first_stage_model.decoder.mid.block_2.conv2.bias": "blocks.3.conv2.bias", + "first_stage_model.decoder.mid.block_2.conv2.weight": "blocks.3.conv2.weight", + "first_stage_model.decoder.mid.block_2.mix_factor": "blocks.4.mix_factor", + "first_stage_model.decoder.mid.block_2.norm1.bias": "blocks.3.norm1.bias", + "first_stage_model.decoder.mid.block_2.norm1.weight": "blocks.3.norm1.weight", + "first_stage_model.decoder.mid.block_2.norm2.bias": "blocks.3.norm2.bias", + "first_stage_model.decoder.mid.block_2.norm2.weight": "blocks.3.norm2.weight", + "first_stage_model.decoder.mid.block_2.time_stack.in_layers.0.bias": "blocks.4.norm1.bias", + "first_stage_model.decoder.mid.block_2.time_stack.in_layers.0.weight": "blocks.4.norm1.weight", + "first_stage_model.decoder.mid.block_2.time_stack.in_layers.2.bias": "blocks.4.conv1.bias", + "first_stage_model.decoder.mid.block_2.time_stack.in_layers.2.weight": "blocks.4.conv1.weight", + "first_stage_model.decoder.mid.block_2.time_stack.out_layers.0.bias": "blocks.4.norm2.bias", + "first_stage_model.decoder.mid.block_2.time_stack.out_layers.0.weight": "blocks.4.norm2.weight", + "first_stage_model.decoder.mid.block_2.time_stack.out_layers.3.bias": "blocks.4.conv2.bias", + "first_stage_model.decoder.mid.block_2.time_stack.out_layers.3.weight": "blocks.4.conv2.weight", + "first_stage_model.decoder.norm_out.bias": "conv_norm_out.bias", + "first_stage_model.decoder.norm_out.weight": "conv_norm_out.weight", + "first_stage_model.decoder.up.0.block.0.conv1.bias": "blocks.26.conv1.bias", + "first_stage_model.decoder.up.0.block.0.conv1.weight": "blocks.26.conv1.weight", + "first_stage_model.decoder.up.0.block.0.conv2.bias": "blocks.26.conv2.bias", + "first_stage_model.decoder.up.0.block.0.conv2.weight": "blocks.26.conv2.weight", + "first_stage_model.decoder.up.0.block.0.mix_factor": "blocks.27.mix_factor", + "first_stage_model.decoder.up.0.block.0.nin_shortcut.bias": "blocks.26.conv_shortcut.bias", + "first_stage_model.decoder.up.0.block.0.nin_shortcut.weight": "blocks.26.conv_shortcut.weight", + "first_stage_model.decoder.up.0.block.0.norm1.bias": "blocks.26.norm1.bias", + "first_stage_model.decoder.up.0.block.0.norm1.weight": "blocks.26.norm1.weight", + "first_stage_model.decoder.up.0.block.0.norm2.bias": "blocks.26.norm2.bias", + "first_stage_model.decoder.up.0.block.0.norm2.weight": "blocks.26.norm2.weight", + "first_stage_model.decoder.up.0.block.0.time_stack.in_layers.0.bias": "blocks.27.norm1.bias", + "first_stage_model.decoder.up.0.block.0.time_stack.in_layers.0.weight": "blocks.27.norm1.weight", + "first_stage_model.decoder.up.0.block.0.time_stack.in_layers.2.bias": "blocks.27.conv1.bias", + "first_stage_model.decoder.up.0.block.0.time_stack.in_layers.2.weight": "blocks.27.conv1.weight", + "first_stage_model.decoder.up.0.block.0.time_stack.out_layers.0.bias": "blocks.27.norm2.bias", + "first_stage_model.decoder.up.0.block.0.time_stack.out_layers.0.weight": "blocks.27.norm2.weight", + "first_stage_model.decoder.up.0.block.0.time_stack.out_layers.3.bias": "blocks.27.conv2.bias", + "first_stage_model.decoder.up.0.block.0.time_stack.out_layers.3.weight": "blocks.27.conv2.weight", + "first_stage_model.decoder.up.0.block.1.conv1.bias": "blocks.28.conv1.bias", + "first_stage_model.decoder.up.0.block.1.conv1.weight": "blocks.28.conv1.weight", + "first_stage_model.decoder.up.0.block.1.conv2.bias": "blocks.28.conv2.bias", + "first_stage_model.decoder.up.0.block.1.conv2.weight": "blocks.28.conv2.weight", + "first_stage_model.decoder.up.0.block.1.mix_factor": "blocks.29.mix_factor", + "first_stage_model.decoder.up.0.block.1.norm1.bias": "blocks.28.norm1.bias", + "first_stage_model.decoder.up.0.block.1.norm1.weight": "blocks.28.norm1.weight", + "first_stage_model.decoder.up.0.block.1.norm2.bias": "blocks.28.norm2.bias", + "first_stage_model.decoder.up.0.block.1.norm2.weight": "blocks.28.norm2.weight", + "first_stage_model.decoder.up.0.block.1.time_stack.in_layers.0.bias": "blocks.29.norm1.bias", + "first_stage_model.decoder.up.0.block.1.time_stack.in_layers.0.weight": "blocks.29.norm1.weight", + "first_stage_model.decoder.up.0.block.1.time_stack.in_layers.2.bias": "blocks.29.conv1.bias", + "first_stage_model.decoder.up.0.block.1.time_stack.in_layers.2.weight": "blocks.29.conv1.weight", + "first_stage_model.decoder.up.0.block.1.time_stack.out_layers.0.bias": "blocks.29.norm2.bias", + "first_stage_model.decoder.up.0.block.1.time_stack.out_layers.0.weight": "blocks.29.norm2.weight", + "first_stage_model.decoder.up.0.block.1.time_stack.out_layers.3.bias": "blocks.29.conv2.bias", + "first_stage_model.decoder.up.0.block.1.time_stack.out_layers.3.weight": "blocks.29.conv2.weight", + "first_stage_model.decoder.up.0.block.2.conv1.bias": "blocks.30.conv1.bias", + "first_stage_model.decoder.up.0.block.2.conv1.weight": "blocks.30.conv1.weight", + "first_stage_model.decoder.up.0.block.2.conv2.bias": "blocks.30.conv2.bias", + "first_stage_model.decoder.up.0.block.2.conv2.weight": "blocks.30.conv2.weight", + "first_stage_model.decoder.up.0.block.2.mix_factor": "blocks.31.mix_factor", + "first_stage_model.decoder.up.0.block.2.norm1.bias": "blocks.30.norm1.bias", + "first_stage_model.decoder.up.0.block.2.norm1.weight": "blocks.30.norm1.weight", + "first_stage_model.decoder.up.0.block.2.norm2.bias": "blocks.30.norm2.bias", + "first_stage_model.decoder.up.0.block.2.norm2.weight": "blocks.30.norm2.weight", + "first_stage_model.decoder.up.0.block.2.time_stack.in_layers.0.bias": "blocks.31.norm1.bias", + "first_stage_model.decoder.up.0.block.2.time_stack.in_layers.0.weight": "blocks.31.norm1.weight", + "first_stage_model.decoder.up.0.block.2.time_stack.in_layers.2.bias": "blocks.31.conv1.bias", + "first_stage_model.decoder.up.0.block.2.time_stack.in_layers.2.weight": "blocks.31.conv1.weight", + "first_stage_model.decoder.up.0.block.2.time_stack.out_layers.0.bias": "blocks.31.norm2.bias", + "first_stage_model.decoder.up.0.block.2.time_stack.out_layers.0.weight": "blocks.31.norm2.weight", + "first_stage_model.decoder.up.0.block.2.time_stack.out_layers.3.bias": "blocks.31.conv2.bias", + "first_stage_model.decoder.up.0.block.2.time_stack.out_layers.3.weight": "blocks.31.conv2.weight", + "first_stage_model.decoder.up.1.block.0.conv1.bias": "blocks.19.conv1.bias", + "first_stage_model.decoder.up.1.block.0.conv1.weight": "blocks.19.conv1.weight", + "first_stage_model.decoder.up.1.block.0.conv2.bias": "blocks.19.conv2.bias", + "first_stage_model.decoder.up.1.block.0.conv2.weight": "blocks.19.conv2.weight", + "first_stage_model.decoder.up.1.block.0.mix_factor": "blocks.20.mix_factor", + "first_stage_model.decoder.up.1.block.0.nin_shortcut.bias": "blocks.19.conv_shortcut.bias", + "first_stage_model.decoder.up.1.block.0.nin_shortcut.weight": "blocks.19.conv_shortcut.weight", + "first_stage_model.decoder.up.1.block.0.norm1.bias": "blocks.19.norm1.bias", + "first_stage_model.decoder.up.1.block.0.norm1.weight": "blocks.19.norm1.weight", + "first_stage_model.decoder.up.1.block.0.norm2.bias": "blocks.19.norm2.bias", + "first_stage_model.decoder.up.1.block.0.norm2.weight": "blocks.19.norm2.weight", + "first_stage_model.decoder.up.1.block.0.time_stack.in_layers.0.bias": "blocks.20.norm1.bias", + "first_stage_model.decoder.up.1.block.0.time_stack.in_layers.0.weight": "blocks.20.norm1.weight", + "first_stage_model.decoder.up.1.block.0.time_stack.in_layers.2.bias": "blocks.20.conv1.bias", + "first_stage_model.decoder.up.1.block.0.time_stack.in_layers.2.weight": "blocks.20.conv1.weight", + "first_stage_model.decoder.up.1.block.0.time_stack.out_layers.0.bias": "blocks.20.norm2.bias", + "first_stage_model.decoder.up.1.block.0.time_stack.out_layers.0.weight": "blocks.20.norm2.weight", + "first_stage_model.decoder.up.1.block.0.time_stack.out_layers.3.bias": "blocks.20.conv2.bias", + "first_stage_model.decoder.up.1.block.0.time_stack.out_layers.3.weight": "blocks.20.conv2.weight", + "first_stage_model.decoder.up.1.block.1.conv1.bias": "blocks.21.conv1.bias", + "first_stage_model.decoder.up.1.block.1.conv1.weight": "blocks.21.conv1.weight", + "first_stage_model.decoder.up.1.block.1.conv2.bias": "blocks.21.conv2.bias", + "first_stage_model.decoder.up.1.block.1.conv2.weight": "blocks.21.conv2.weight", + "first_stage_model.decoder.up.1.block.1.mix_factor": "blocks.22.mix_factor", + "first_stage_model.decoder.up.1.block.1.norm1.bias": "blocks.21.norm1.bias", + "first_stage_model.decoder.up.1.block.1.norm1.weight": "blocks.21.norm1.weight", + "first_stage_model.decoder.up.1.block.1.norm2.bias": "blocks.21.norm2.bias", + "first_stage_model.decoder.up.1.block.1.norm2.weight": "blocks.21.norm2.weight", + "first_stage_model.decoder.up.1.block.1.time_stack.in_layers.0.bias": "blocks.22.norm1.bias", + "first_stage_model.decoder.up.1.block.1.time_stack.in_layers.0.weight": "blocks.22.norm1.weight", + "first_stage_model.decoder.up.1.block.1.time_stack.in_layers.2.bias": "blocks.22.conv1.bias", + "first_stage_model.decoder.up.1.block.1.time_stack.in_layers.2.weight": "blocks.22.conv1.weight", + "first_stage_model.decoder.up.1.block.1.time_stack.out_layers.0.bias": "blocks.22.norm2.bias", + "first_stage_model.decoder.up.1.block.1.time_stack.out_layers.0.weight": "blocks.22.norm2.weight", + "first_stage_model.decoder.up.1.block.1.time_stack.out_layers.3.bias": "blocks.22.conv2.bias", + "first_stage_model.decoder.up.1.block.1.time_stack.out_layers.3.weight": "blocks.22.conv2.weight", + "first_stage_model.decoder.up.1.block.2.conv1.bias": "blocks.23.conv1.bias", + "first_stage_model.decoder.up.1.block.2.conv1.weight": "blocks.23.conv1.weight", + "first_stage_model.decoder.up.1.block.2.conv2.bias": "blocks.23.conv2.bias", + "first_stage_model.decoder.up.1.block.2.conv2.weight": "blocks.23.conv2.weight", + "first_stage_model.decoder.up.1.block.2.mix_factor": "blocks.24.mix_factor", + "first_stage_model.decoder.up.1.block.2.norm1.bias": "blocks.23.norm1.bias", + "first_stage_model.decoder.up.1.block.2.norm1.weight": "blocks.23.norm1.weight", + "first_stage_model.decoder.up.1.block.2.norm2.bias": "blocks.23.norm2.bias", + "first_stage_model.decoder.up.1.block.2.norm2.weight": 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"first_stage_model.decoder.up.2.block.0.conv1.bias": "blocks.12.conv1.bias", + "first_stage_model.decoder.up.2.block.0.conv1.weight": "blocks.12.conv1.weight", + "first_stage_model.decoder.up.2.block.0.conv2.bias": "blocks.12.conv2.bias", + "first_stage_model.decoder.up.2.block.0.conv2.weight": "blocks.12.conv2.weight", + "first_stage_model.decoder.up.2.block.0.mix_factor": "blocks.13.mix_factor", + "first_stage_model.decoder.up.2.block.0.norm1.bias": "blocks.12.norm1.bias", + "first_stage_model.decoder.up.2.block.0.norm1.weight": "blocks.12.norm1.weight", + "first_stage_model.decoder.up.2.block.0.norm2.bias": "blocks.12.norm2.bias", + "first_stage_model.decoder.up.2.block.0.norm2.weight": "blocks.12.norm2.weight", + "first_stage_model.decoder.up.2.block.0.time_stack.in_layers.0.bias": "blocks.13.norm1.bias", + "first_stage_model.decoder.up.2.block.0.time_stack.in_layers.0.weight": "blocks.13.norm1.weight", + "first_stage_model.decoder.up.2.block.0.time_stack.in_layers.2.bias": 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"first_stage_model.decoder.up.2.block.1.time_stack.out_layers.3.weight": "blocks.15.conv2.weight", + "first_stage_model.decoder.up.2.block.2.conv1.bias": "blocks.16.conv1.bias", + "first_stage_model.decoder.up.2.block.2.conv1.weight": "blocks.16.conv1.weight", + "first_stage_model.decoder.up.2.block.2.conv2.bias": "blocks.16.conv2.bias", + "first_stage_model.decoder.up.2.block.2.conv2.weight": "blocks.16.conv2.weight", + "first_stage_model.decoder.up.2.block.2.mix_factor": "blocks.17.mix_factor", + "first_stage_model.decoder.up.2.block.2.norm1.bias": "blocks.16.norm1.bias", + "first_stage_model.decoder.up.2.block.2.norm1.weight": "blocks.16.norm1.weight", + "first_stage_model.decoder.up.2.block.2.norm2.bias": "blocks.16.norm2.bias", + "first_stage_model.decoder.up.2.block.2.norm2.weight": "blocks.16.norm2.weight", + "first_stage_model.decoder.up.2.block.2.time_stack.in_layers.0.bias": "blocks.17.norm1.bias", + "first_stage_model.decoder.up.2.block.2.time_stack.in_layers.0.weight": 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"blocks.5.conv2.bias", + "first_stage_model.decoder.up.3.block.0.conv2.weight": "blocks.5.conv2.weight", + "first_stage_model.decoder.up.3.block.0.mix_factor": "blocks.6.mix_factor", + "first_stage_model.decoder.up.3.block.0.norm1.bias": "blocks.5.norm1.bias", + "first_stage_model.decoder.up.3.block.0.norm1.weight": "blocks.5.norm1.weight", + "first_stage_model.decoder.up.3.block.0.norm2.bias": "blocks.5.norm2.bias", + "first_stage_model.decoder.up.3.block.0.norm2.weight": "blocks.5.norm2.weight", + "first_stage_model.decoder.up.3.block.0.time_stack.in_layers.0.bias": "blocks.6.norm1.bias", + "first_stage_model.decoder.up.3.block.0.time_stack.in_layers.0.weight": "blocks.6.norm1.weight", + "first_stage_model.decoder.up.3.block.0.time_stack.in_layers.2.bias": "blocks.6.conv1.bias", + "first_stage_model.decoder.up.3.block.0.time_stack.in_layers.2.weight": "blocks.6.conv1.weight", + "first_stage_model.decoder.up.3.block.0.time_stack.out_layers.0.bias": "blocks.6.norm2.bias", + "first_stage_model.decoder.up.3.block.0.time_stack.out_layers.0.weight": "blocks.6.norm2.weight", + "first_stage_model.decoder.up.3.block.0.time_stack.out_layers.3.bias": "blocks.6.conv2.bias", + "first_stage_model.decoder.up.3.block.0.time_stack.out_layers.3.weight": "blocks.6.conv2.weight", + "first_stage_model.decoder.up.3.block.1.conv1.bias": "blocks.7.conv1.bias", + "first_stage_model.decoder.up.3.block.1.conv1.weight": "blocks.7.conv1.weight", + "first_stage_model.decoder.up.3.block.1.conv2.bias": "blocks.7.conv2.bias", + "first_stage_model.decoder.up.3.block.1.conv2.weight": "blocks.7.conv2.weight", + "first_stage_model.decoder.up.3.block.1.mix_factor": "blocks.8.mix_factor", + "first_stage_model.decoder.up.3.block.1.norm1.bias": "blocks.7.norm1.bias", + "first_stage_model.decoder.up.3.block.1.norm1.weight": "blocks.7.norm1.weight", + "first_stage_model.decoder.up.3.block.1.norm2.bias": "blocks.7.norm2.bias", + "first_stage_model.decoder.up.3.block.1.norm2.weight": "blocks.7.norm2.weight", + "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.0.bias": "blocks.8.norm1.bias", + "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.0.weight": "blocks.8.norm1.weight", + "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.2.bias": "blocks.8.conv1.bias", + "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.2.weight": "blocks.8.conv1.weight", + "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.0.bias": "blocks.8.norm2.bias", + "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.0.weight": "blocks.8.norm2.weight", + "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.3.bias": "blocks.8.conv2.bias", + "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.3.weight": "blocks.8.conv2.weight", + "first_stage_model.decoder.up.3.block.2.conv1.bias": "blocks.9.conv1.bias", + "first_stage_model.decoder.up.3.block.2.conv1.weight": "blocks.9.conv1.weight", + "first_stage_model.decoder.up.3.block.2.conv2.bias": "blocks.9.conv2.bias", + "first_stage_model.decoder.up.3.block.2.conv2.weight": "blocks.9.conv2.weight", + "first_stage_model.decoder.up.3.block.2.mix_factor": "blocks.10.mix_factor", + "first_stage_model.decoder.up.3.block.2.norm1.bias": "blocks.9.norm1.bias", + "first_stage_model.decoder.up.3.block.2.norm1.weight": "blocks.9.norm1.weight", + "first_stage_model.decoder.up.3.block.2.norm2.bias": "blocks.9.norm2.bias", + "first_stage_model.decoder.up.3.block.2.norm2.weight": "blocks.9.norm2.weight", + "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.0.bias": "blocks.10.norm1.bias", + "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.0.weight": "blocks.10.norm1.weight", + "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.2.bias": "blocks.10.conv1.bias", + "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.2.weight": "blocks.10.conv1.weight", + "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.0.bias": "blocks.10.norm2.bias", + "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.0.weight": "blocks.10.norm2.weight", + "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.3.bias": "blocks.10.conv2.bias", + "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.3.weight": "blocks.10.conv2.weight", + "first_stage_model.decoder.up.3.upsample.conv.bias": "blocks.11.conv.bias", + "first_stage_model.decoder.up.3.upsample.conv.weight": "blocks.11.conv.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if "blocks.2.transformer_blocks.0" in rename_dict[name]: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ diff --git a/diffsynth/models/svd_vae_encoder.py b/diffsynth/models/svd_vae_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..3e84a59f9638c7b8429757711dc6ddf64d3a35e4 --- /dev/null +++ b/diffsynth/models/svd_vae_encoder.py @@ -0,0 +1,138 @@ +from .sd_vae_encoder import SDVAEEncoderStateDictConverter, SDVAEEncoder + + +class SVDVAEEncoder(SDVAEEncoder): + def __init__(self): + super().__init__() + self.scaling_factor = 0.13025 + + def state_dict_converter(self): + return SVDVAEEncoderStateDictConverter() + + +class SVDVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter): + def __init__(self): + super().__init__() + + def from_diffusers(self, state_dict): + return super().from_diffusers(state_dict) + + def from_civitai(self, state_dict): + rename_dict = { + "conditioner.embedders.3.encoder.encoder.conv_in.bias": "conv_in.bias", + "conditioner.embedders.3.encoder.encoder.conv_in.weight": "conv_in.weight", + "conditioner.embedders.3.encoder.encoder.conv_out.bias": "conv_out.bias", + "conditioner.embedders.3.encoder.encoder.conv_out.weight": "conv_out.weight", + "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias", + "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight", + "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias", + "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight", + "conditioner.embedders.3.encoder.encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias", + "conditioner.embedders.3.encoder.encoder.down.0.block.0.norm1.weight": "blocks.0.norm1.weight", + "conditioner.embedders.3.encoder.encoder.down.0.block.0.norm2.bias": "blocks.0.norm2.bias", + "conditioner.embedders.3.encoder.encoder.down.0.block.0.norm2.weight": "blocks.0.norm2.weight", + "conditioner.embedders.3.encoder.encoder.down.0.block.1.conv1.bias": "blocks.1.conv1.bias", + "conditioner.embedders.3.encoder.encoder.down.0.block.1.conv1.weight": "blocks.1.conv1.weight", + "conditioner.embedders.3.encoder.encoder.down.0.block.1.conv2.bias": "blocks.1.conv2.bias", + "conditioner.embedders.3.encoder.encoder.down.0.block.1.conv2.weight": "blocks.1.conv2.weight", + "conditioner.embedders.3.encoder.encoder.down.0.block.1.norm1.bias": "blocks.1.norm1.bias", + "conditioner.embedders.3.encoder.encoder.down.0.block.1.norm1.weight": "blocks.1.norm1.weight", + "conditioner.embedders.3.encoder.encoder.down.0.block.1.norm2.bias": "blocks.1.norm2.bias", + "conditioner.embedders.3.encoder.encoder.down.0.block.1.norm2.weight": "blocks.1.norm2.weight", + "conditioner.embedders.3.encoder.encoder.down.0.downsample.conv.bias": "blocks.2.conv.bias", + "conditioner.embedders.3.encoder.encoder.down.0.downsample.conv.weight": "blocks.2.conv.weight", + "conditioner.embedders.3.encoder.encoder.down.1.block.0.conv1.bias": "blocks.3.conv1.bias", + "conditioner.embedders.3.encoder.encoder.down.1.block.0.conv1.weight": "blocks.3.conv1.weight", + "conditioner.embedders.3.encoder.encoder.down.1.block.0.conv2.bias": "blocks.3.conv2.bias", + "conditioner.embedders.3.encoder.encoder.down.1.block.0.conv2.weight": "blocks.3.conv2.weight", + "conditioner.embedders.3.encoder.encoder.down.1.block.0.nin_shortcut.bias": "blocks.3.conv_shortcut.bias", + "conditioner.embedders.3.encoder.encoder.down.1.block.0.nin_shortcut.weight": "blocks.3.conv_shortcut.weight", + "conditioner.embedders.3.encoder.encoder.down.1.block.0.norm1.bias": "blocks.3.norm1.bias", + "conditioner.embedders.3.encoder.encoder.down.1.block.0.norm1.weight": "blocks.3.norm1.weight", + "conditioner.embedders.3.encoder.encoder.down.1.block.0.norm2.bias": "blocks.3.norm2.bias", + "conditioner.embedders.3.encoder.encoder.down.1.block.0.norm2.weight": "blocks.3.norm2.weight", + "conditioner.embedders.3.encoder.encoder.down.1.block.1.conv1.bias": "blocks.4.conv1.bias", + "conditioner.embedders.3.encoder.encoder.down.1.block.1.conv1.weight": "blocks.4.conv1.weight", + "conditioner.embedders.3.encoder.encoder.down.1.block.1.conv2.bias": "blocks.4.conv2.bias", + "conditioner.embedders.3.encoder.encoder.down.1.block.1.conv2.weight": "blocks.4.conv2.weight", + "conditioner.embedders.3.encoder.encoder.down.1.block.1.norm1.bias": "blocks.4.norm1.bias", + "conditioner.embedders.3.encoder.encoder.down.1.block.1.norm1.weight": "blocks.4.norm1.weight", + "conditioner.embedders.3.encoder.encoder.down.1.block.1.norm2.bias": "blocks.4.norm2.bias", + "conditioner.embedders.3.encoder.encoder.down.1.block.1.norm2.weight": "blocks.4.norm2.weight", + "conditioner.embedders.3.encoder.encoder.down.1.downsample.conv.bias": "blocks.5.conv.bias", + "conditioner.embedders.3.encoder.encoder.down.1.downsample.conv.weight": "blocks.5.conv.weight", + "conditioner.embedders.3.encoder.encoder.down.2.block.0.conv1.bias": "blocks.6.conv1.bias", + "conditioner.embedders.3.encoder.encoder.down.2.block.0.conv1.weight": "blocks.6.conv1.weight", + "conditioner.embedders.3.encoder.encoder.down.2.block.0.conv2.bias": "blocks.6.conv2.bias", + "conditioner.embedders.3.encoder.encoder.down.2.block.0.conv2.weight": "blocks.6.conv2.weight", + "conditioner.embedders.3.encoder.encoder.down.2.block.0.nin_shortcut.bias": "blocks.6.conv_shortcut.bias", + "conditioner.embedders.3.encoder.encoder.down.2.block.0.nin_shortcut.weight": "blocks.6.conv_shortcut.weight", + "conditioner.embedders.3.encoder.encoder.down.2.block.0.norm1.bias": "blocks.6.norm1.bias", + "conditioner.embedders.3.encoder.encoder.down.2.block.0.norm1.weight": "blocks.6.norm1.weight", + "conditioner.embedders.3.encoder.encoder.down.2.block.0.norm2.bias": "blocks.6.norm2.bias", + "conditioner.embedders.3.encoder.encoder.down.2.block.0.norm2.weight": "blocks.6.norm2.weight", + "conditioner.embedders.3.encoder.encoder.down.2.block.1.conv1.bias": "blocks.7.conv1.bias", + "conditioner.embedders.3.encoder.encoder.down.2.block.1.conv1.weight": "blocks.7.conv1.weight", + "conditioner.embedders.3.encoder.encoder.down.2.block.1.conv2.bias": "blocks.7.conv2.bias", + "conditioner.embedders.3.encoder.encoder.down.2.block.1.conv2.weight": "blocks.7.conv2.weight", + "conditioner.embedders.3.encoder.encoder.down.2.block.1.norm1.bias": "blocks.7.norm1.bias", + "conditioner.embedders.3.encoder.encoder.down.2.block.1.norm1.weight": "blocks.7.norm1.weight", + "conditioner.embedders.3.encoder.encoder.down.2.block.1.norm2.bias": "blocks.7.norm2.bias", + "conditioner.embedders.3.encoder.encoder.down.2.block.1.norm2.weight": "blocks.7.norm2.weight", + "conditioner.embedders.3.encoder.encoder.down.2.downsample.conv.bias": "blocks.8.conv.bias", + "conditioner.embedders.3.encoder.encoder.down.2.downsample.conv.weight": "blocks.8.conv.weight", + "conditioner.embedders.3.encoder.encoder.down.3.block.0.conv1.bias": "blocks.9.conv1.bias", + "conditioner.embedders.3.encoder.encoder.down.3.block.0.conv1.weight": "blocks.9.conv1.weight", + "conditioner.embedders.3.encoder.encoder.down.3.block.0.conv2.bias": "blocks.9.conv2.bias", + "conditioner.embedders.3.encoder.encoder.down.3.block.0.conv2.weight": "blocks.9.conv2.weight", + "conditioner.embedders.3.encoder.encoder.down.3.block.0.norm1.bias": "blocks.9.norm1.bias", + "conditioner.embedders.3.encoder.encoder.down.3.block.0.norm1.weight": "blocks.9.norm1.weight", + "conditioner.embedders.3.encoder.encoder.down.3.block.0.norm2.bias": "blocks.9.norm2.bias", + "conditioner.embedders.3.encoder.encoder.down.3.block.0.norm2.weight": "blocks.9.norm2.weight", + "conditioner.embedders.3.encoder.encoder.down.3.block.1.conv1.bias": "blocks.10.conv1.bias", + "conditioner.embedders.3.encoder.encoder.down.3.block.1.conv1.weight": "blocks.10.conv1.weight", + "conditioner.embedders.3.encoder.encoder.down.3.block.1.conv2.bias": "blocks.10.conv2.bias", + "conditioner.embedders.3.encoder.encoder.down.3.block.1.conv2.weight": "blocks.10.conv2.weight", + "conditioner.embedders.3.encoder.encoder.down.3.block.1.norm1.bias": "blocks.10.norm1.bias", + "conditioner.embedders.3.encoder.encoder.down.3.block.1.norm1.weight": "blocks.10.norm1.weight", + "conditioner.embedders.3.encoder.encoder.down.3.block.1.norm2.bias": "blocks.10.norm2.bias", + "conditioner.embedders.3.encoder.encoder.down.3.block.1.norm2.weight": "blocks.10.norm2.weight", + "conditioner.embedders.3.encoder.encoder.mid.attn_1.k.bias": "blocks.12.transformer_blocks.0.to_k.bias", + "conditioner.embedders.3.encoder.encoder.mid.attn_1.k.weight": "blocks.12.transformer_blocks.0.to_k.weight", + "conditioner.embedders.3.encoder.encoder.mid.attn_1.norm.bias": "blocks.12.norm.bias", + "conditioner.embedders.3.encoder.encoder.mid.attn_1.norm.weight": "blocks.12.norm.weight", + "conditioner.embedders.3.encoder.encoder.mid.attn_1.proj_out.bias": "blocks.12.transformer_blocks.0.to_out.bias", + "conditioner.embedders.3.encoder.encoder.mid.attn_1.proj_out.weight": "blocks.12.transformer_blocks.0.to_out.weight", + "conditioner.embedders.3.encoder.encoder.mid.attn_1.q.bias": "blocks.12.transformer_blocks.0.to_q.bias", + "conditioner.embedders.3.encoder.encoder.mid.attn_1.q.weight": "blocks.12.transformer_blocks.0.to_q.weight", + "conditioner.embedders.3.encoder.encoder.mid.attn_1.v.bias": "blocks.12.transformer_blocks.0.to_v.bias", + "conditioner.embedders.3.encoder.encoder.mid.attn_1.v.weight": "blocks.12.transformer_blocks.0.to_v.weight", + "conditioner.embedders.3.encoder.encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias", + "conditioner.embedders.3.encoder.encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight", + "conditioner.embedders.3.encoder.encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias", + "conditioner.embedders.3.encoder.encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight", + "conditioner.embedders.3.encoder.encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias", + "conditioner.embedders.3.encoder.encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight", + "conditioner.embedders.3.encoder.encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias", + "conditioner.embedders.3.encoder.encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight", + "conditioner.embedders.3.encoder.encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias", + "conditioner.embedders.3.encoder.encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight", + "conditioner.embedders.3.encoder.encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias", + "conditioner.embedders.3.encoder.encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight", + "conditioner.embedders.3.encoder.encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias", + "conditioner.embedders.3.encoder.encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight", + "conditioner.embedders.3.encoder.encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias", + "conditioner.embedders.3.encoder.encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight", + "conditioner.embedders.3.encoder.encoder.norm_out.bias": "conv_norm_out.bias", + "conditioner.embedders.3.encoder.encoder.norm_out.weight": "conv_norm_out.weight", + "conditioner.embedders.3.encoder.quant_conv.bias": "quant_conv.bias", + "conditioner.embedders.3.encoder.quant_conv.weight": "quant_conv.weight", + } + state_dict_ = {} + for name in state_dict: + if name in rename_dict: + param = state_dict[name] + if "transformer_blocks" in rename_dict[name]: + param = param.squeeze() + state_dict_[rename_dict[name]] = param + return state_dict_ diff --git a/diffsynth/models/tiler.py b/diffsynth/models/tiler.py new file mode 100644 index 0000000000000000000000000000000000000000..af37ff62a0fe08e0822ed26a50420d5dced4b79c --- /dev/null +++ b/diffsynth/models/tiler.py @@ -0,0 +1,106 @@ +import torch +from einops import rearrange, repeat + + +class TileWorker: + def __init__(self): + pass + + + def mask(self, height, width, border_width): + # Create a mask with shape (height, width). + # The centre area is filled with 1, and the border line is filled with values in range (0, 1]. + x = torch.arange(height).repeat(width, 1).T + y = torch.arange(width).repeat(height, 1) + mask = torch.stack([x + 1, height - x, y + 1, width - y]).min(dim=0).values + mask = (mask / border_width).clip(0, 1) + return mask + + + def tile(self, model_input, tile_size, tile_stride, tile_device, tile_dtype): + # Convert a tensor (b, c, h, w) to (b, c, tile_size, tile_size, tile_num) + batch_size, channel, _, _ = model_input.shape + model_input = model_input.to(device=tile_device, dtype=tile_dtype) + unfold_operator = torch.nn.Unfold( + kernel_size=(tile_size, tile_size), + stride=(tile_stride, tile_stride) + ) + model_input = unfold_operator(model_input) + model_input = model_input.view((batch_size, channel, tile_size, tile_size, -1)) + + return model_input + + + def tiled_inference(self, forward_fn, model_input, tile_batch_size, inference_device, inference_dtype, tile_device, tile_dtype): + # Call y=forward_fn(x) for each tile + tile_num = model_input.shape[-1] + model_output_stack = [] + + for tile_id in range(0, tile_num, tile_batch_size): + + # process input + tile_id_ = min(tile_id + tile_batch_size, tile_num) + x = model_input[:, :, :, :, tile_id: tile_id_] + x = x.to(device=inference_device, dtype=inference_dtype) + x = rearrange(x, "b c h w n -> (n b) c h w") + + # process output + y = forward_fn(x) + y = rearrange(y, "(n b) c h w -> b c h w n", n=tile_id_-tile_id) + y = y.to(device=tile_device, dtype=tile_dtype) + model_output_stack.append(y) + + model_output = torch.concat(model_output_stack, dim=-1) + return model_output + + + def io_scale(self, model_output, tile_size): + # Determine the size modification happend in forward_fn + # We only consider the same scale on height and width. + io_scale = model_output.shape[2] / tile_size + return io_scale + + + def untile(self, model_output, height, width, tile_size, tile_stride, border_width, tile_device, tile_dtype): + # The reversed function of tile + mask = self.mask(tile_size, tile_size, border_width) + mask = mask.to(device=tile_device, dtype=tile_dtype) + mask = rearrange(mask, "h w -> 1 1 h w 1") + model_output = model_output * mask + + fold_operator = torch.nn.Fold( + output_size=(height, width), + kernel_size=(tile_size, tile_size), + stride=(tile_stride, tile_stride) + ) + mask = repeat(mask[0, 0, :, :, 0], "h w -> 1 (h w) n", n=model_output.shape[-1]) + model_output = rearrange(model_output, "b c h w n -> b (c h w) n") + model_output = fold_operator(model_output) / fold_operator(mask) + + return model_output + + + def tiled_forward(self, forward_fn, model_input, tile_size, tile_stride, tile_batch_size=1, tile_device="cpu", tile_dtype=torch.float32, border_width=None): + # Prepare + inference_device, inference_dtype = model_input.device, model_input.dtype + height, width = model_input.shape[2], model_input.shape[3] + border_width = int(tile_stride*0.5) if border_width is None else border_width + + # tile + model_input = self.tile(model_input, tile_size, tile_stride, tile_device, tile_dtype) + + # inference + model_output = self.tiled_inference(forward_fn, model_input, tile_batch_size, inference_device, inference_dtype, tile_device, tile_dtype) + + # resize + io_scale = self.io_scale(model_output, tile_size) + height, width = int(height*io_scale), int(width*io_scale) + tile_size, tile_stride = int(tile_size*io_scale), int(tile_stride*io_scale) + border_width = int(border_width*io_scale) + + # untile + model_output = self.untile(model_output, height, width, tile_size, tile_stride, border_width, tile_device, tile_dtype) + + # Done! + model_output = model_output.to(device=inference_device, dtype=inference_dtype) + return model_output \ No newline at end of file diff --git a/diffsynth/pipelines/__init__.py b/diffsynth/pipelines/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c4752884505456e791d699c83ea9c61623908fc6 --- /dev/null +++ b/diffsynth/pipelines/__init__.py @@ -0,0 +1,8 @@ +from .stable_diffusion import SDImagePipeline +from .stable_diffusion_xl import SDXLImagePipeline +from .stable_diffusion_video import SDVideoPipeline, SDVideoPipelineRunner +from .stable_diffusion_xl_video import SDXLVideoPipeline +from .stable_video_diffusion import SVDVideoPipeline +from .hunyuan_dit import HunyuanDiTImagePipeline +from .stable_diffusion_3 import SD3ImagePipeline +from .kwai_kolors import KolorsImagePipeline diff --git a/diffsynth/pipelines/dancer.py b/diffsynth/pipelines/dancer.py new file mode 100644 index 0000000000000000000000000000000000000000..986f4cacd4767f0a94e1c9966e2ad215998487df --- /dev/null +++ b/diffsynth/pipelines/dancer.py @@ -0,0 +1,174 @@ +import torch +from ..models import SDUNet, SDMotionModel, SDXLUNet, SDXLMotionModel +from ..models.sd_unet import PushBlock, PopBlock +from ..controlnets import MultiControlNetManager + + +def lets_dance( + unet: SDUNet, + motion_modules: SDMotionModel = None, + controlnet: MultiControlNetManager = None, + sample = None, + timestep = None, + encoder_hidden_states = None, + ipadapter_kwargs_list = {}, + controlnet_frames = None, + unet_batch_size = 1, + controlnet_batch_size = 1, + cross_frame_attention = False, + tiled=False, + tile_size=64, + tile_stride=32, + device = "cuda", + vram_limit_level = 0, +): + # 1. ControlNet + # This part will be repeated on overlapping frames if animatediff_batch_size > animatediff_stride. + # I leave it here because I intend to do something interesting on the ControlNets. + controlnet_insert_block_id = 30 + if controlnet is not None and controlnet_frames is not None: + res_stacks = [] + # process controlnet frames with batch + for batch_id in range(0, sample.shape[0], controlnet_batch_size): + batch_id_ = min(batch_id + controlnet_batch_size, sample.shape[0]) + res_stack = controlnet( + sample[batch_id: batch_id_], + timestep, + encoder_hidden_states[batch_id: batch_id_], + controlnet_frames[:, batch_id: batch_id_], + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride + ) + if vram_limit_level >= 1: + res_stack = [res.cpu() for res in res_stack] + res_stacks.append(res_stack) + # concat the residual + additional_res_stack = [] + for i in range(len(res_stacks[0])): + res = torch.concat([res_stack[i] for res_stack in res_stacks], dim=0) + additional_res_stack.append(res) + else: + additional_res_stack = None + + # 2. time + time_emb = unet.time_proj(timestep[None]).to(sample.dtype) + time_emb = unet.time_embedding(time_emb) + + # 3. pre-process + height, width = sample.shape[2], sample.shape[3] + hidden_states = unet.conv_in(sample) + text_emb = encoder_hidden_states + res_stack = [hidden_states.cpu() if vram_limit_level>=1 else hidden_states] + + # 4. blocks + for block_id, block in enumerate(unet.blocks): + # 4.1 UNet + if isinstance(block, PushBlock): + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + if vram_limit_level>=1: + res_stack[-1] = res_stack[-1].cpu() + elif isinstance(block, PopBlock): + if vram_limit_level>=1: + res_stack[-1] = res_stack[-1].to(device) + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + else: + hidden_states_input = hidden_states + hidden_states_output = [] + for batch_id in range(0, sample.shape[0], unet_batch_size): + batch_id_ = min(batch_id + unet_batch_size, sample.shape[0]) + hidden_states, _, _, _ = block( + hidden_states_input[batch_id: batch_id_], + time_emb, + text_emb[batch_id: batch_id_], + res_stack, + cross_frame_attention=cross_frame_attention, + ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, {}), + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride + ) + hidden_states_output.append(hidden_states) + hidden_states = torch.concat(hidden_states_output, dim=0) + # 4.2 AnimateDiff + if motion_modules is not None: + if block_id in motion_modules.call_block_id: + motion_module_id = motion_modules.call_block_id[block_id] + hidden_states, time_emb, text_emb, res_stack = motion_modules.motion_modules[motion_module_id]( + hidden_states, time_emb, text_emb, res_stack, + batch_size=1 + ) + # 4.3 ControlNet + if block_id == controlnet_insert_block_id and additional_res_stack is not None: + hidden_states += additional_res_stack.pop().to(device) + if vram_limit_level>=1: + res_stack = [(res.to(device) + additional_res.to(device)).cpu() for res, additional_res in zip(res_stack, additional_res_stack)] + else: + res_stack = [res + additional_res for res, additional_res in zip(res_stack, additional_res_stack)] + + # 5. output + hidden_states = unet.conv_norm_out(hidden_states) + hidden_states = unet.conv_act(hidden_states) + hidden_states = unet.conv_out(hidden_states) + + return hidden_states + + + + +def lets_dance_xl( + unet: SDXLUNet, + motion_modules: SDXLMotionModel = None, + controlnet: MultiControlNetManager = None, + sample = None, + add_time_id = None, + add_text_embeds = None, + timestep = None, + encoder_hidden_states = None, + ipadapter_kwargs_list = {}, + controlnet_frames = None, + unet_batch_size = 1, + controlnet_batch_size = 1, + cross_frame_attention = False, + tiled=False, + tile_size=64, + tile_stride=32, + device = "cuda", + vram_limit_level = 0, +): + # 2. time + t_emb = unet.time_proj(timestep[None]).to(sample.dtype) + t_emb = unet.time_embedding(t_emb) + + time_embeds = unet.add_time_proj(add_time_id) + time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1)) + add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1) + add_embeds = add_embeds.to(sample.dtype) + add_embeds = unet.add_time_embedding(add_embeds) + + time_emb = t_emb + add_embeds + + # 3. pre-process + height, width = sample.shape[2], sample.shape[3] + hidden_states = unet.conv_in(sample) + text_emb = encoder_hidden_states if unet.text_intermediate_proj is None else unet.text_intermediate_proj(encoder_hidden_states) + res_stack = [hidden_states] + + # 4. blocks + for block_id, block in enumerate(unet.blocks): + hidden_states, time_emb, text_emb, res_stack = block( + hidden_states, time_emb, text_emb, res_stack, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, + ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, {}) + ) + # 4.2 AnimateDiff + if motion_modules is not None: + if block_id in motion_modules.call_block_id: + motion_module_id = motion_modules.call_block_id[block_id] + hidden_states, time_emb, text_emb, res_stack = motion_modules.motion_modules[motion_module_id]( + hidden_states, time_emb, text_emb, res_stack, + batch_size=1 + ) + + # 5. output + hidden_states = unet.conv_norm_out(hidden_states) + hidden_states = unet.conv_act(hidden_states) + hidden_states = unet.conv_out(hidden_states) + + return hidden_states \ No newline at end of file diff --git a/diffsynth/pipelines/hunyuan_dit.py b/diffsynth/pipelines/hunyuan_dit.py new file mode 100644 index 0000000000000000000000000000000000000000..10767273bce5251eba732ef461a3e0a85dc99c2a --- /dev/null +++ b/diffsynth/pipelines/hunyuan_dit.py @@ -0,0 +1,298 @@ +from ..models.hunyuan_dit import HunyuanDiT +from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder +from ..models.sdxl_vae_encoder import SDXLVAEEncoder +from ..models.sdxl_vae_decoder import SDXLVAEDecoder +from ..models import ModelManager +from ..prompts import HunyuanDiTPrompter +from ..schedulers import EnhancedDDIMScheduler +import torch +from tqdm import tqdm +from PIL import Image +import numpy as np + + + +class ImageSizeManager: + def __init__(self): + pass + + + def _to_tuple(self, x): + if isinstance(x, int): + return x, x + else: + return x + + + def get_fill_resize_and_crop(self, src, tgt): + th, tw = self._to_tuple(tgt) + h, w = self._to_tuple(src) + + tr = th / tw # base 分辨率 + r = h / w # 目标分辨率 + + # resize + if r > tr: + resize_height = th + resize_width = int(round(th / h * w)) + else: + resize_width = tw + resize_height = int(round(tw / w * h)) # 根据base分辨率，将目标分辨率resize下来 + + crop_top = int(round((th - resize_height) / 2.0)) + crop_left = int(round((tw - resize_width) / 2.0)) + + return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) + + + def get_meshgrid(self, start, *args): + if len(args) == 0: + # start is grid_size + num = self._to_tuple(start) + start = (0, 0) + stop = num + elif len(args) == 1: + # start is start, args[0] is stop, step is 1 + start = self._to_tuple(start) + stop = self._to_tuple(args[0]) + num = (stop[0] - start[0], stop[1] - start[1]) + elif len(args) == 2: + # start is start, args[0] is stop, args[1] is num + start = self._to_tuple(start) # 左上角 eg: 12,0 + stop = self._to_tuple(args[0]) # 右下角 eg: 20,32 + num = self._to_tuple(args[1]) # 目标大小 eg: 32,124 + else: + raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}") + + grid_h = np.linspace(start[0], stop[0], num[0], endpoint=False, dtype=np.float32) # 12-20 中间差值32份 0-32 中间差值124份 + grid_w = np.linspace(start[1], stop[1], num[1], endpoint=False, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) # [2, W, H] + return grid + + + def get_2d_rotary_pos_embed(self, embed_dim, start, *args, use_real=True): + grid = self.get_meshgrid(start, *args) # [2, H, w] + grid = grid.reshape([2, 1, *grid.shape[1:]]) # 返回一个采样矩阵 分辨率与目标分辨率一致 + pos_embed = self.get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real) + return pos_embed + + + def get_2d_rotary_pos_embed_from_grid(self, embed_dim, grid, use_real=False): + assert embed_dim % 4 == 0 + + # use half of dimensions to encode grid_h + emb_h = self.get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real) # (H*W, D/4) + emb_w = self.get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real) # (H*W, D/4) + + if use_real: + cos = torch.cat([emb_h[0], emb_w[0]], dim=1) # (H*W, D/2) + sin = torch.cat([emb_h[1], emb_w[1]], dim=1) # (H*W, D/2) + return cos, sin + else: + emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D/2) + return emb + + + def get_1d_rotary_pos_embed(self, dim: int, pos, theta: float = 10000.0, use_real=False): + if isinstance(pos, int): + pos = np.arange(pos) + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # [D/2] + t = torch.from_numpy(pos).to(freqs.device) # type: ignore # [S] + freqs = torch.outer(t, freqs).float() # type: ignore # [S, D/2] + if use_real: + freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D] + freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D] + return freqs_cos, freqs_sin + else: + freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2] + return freqs_cis + + + def calc_rope(self, height, width): + patch_size = 2 + head_size = 88 + th = height // 8 // patch_size + tw = width // 8 // patch_size + base_size = 512 // 8 // patch_size + start, stop = self.get_fill_resize_and_crop((th, tw), base_size) + sub_args = [start, stop, (th, tw)] + rope = self.get_2d_rotary_pos_embed(head_size, *sub_args) + return rope + + + +class HunyuanDiTImagePipeline(torch.nn.Module): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__() + self.scheduler = EnhancedDDIMScheduler(prediction_type="v_prediction", beta_start=0.00085, beta_end=0.03) + self.prompter = HunyuanDiTPrompter() + self.device = device + self.torch_dtype = torch_dtype + self.image_size_manager = ImageSizeManager() + # models + self.text_encoder: HunyuanDiTCLIPTextEncoder = None + self.text_encoder_t5: HunyuanDiTT5TextEncoder = None + self.dit: HunyuanDiT = None + self.vae_decoder: SDXLVAEDecoder = None + self.vae_encoder: SDXLVAEEncoder = None + + + def fetch_main_models(self, model_manager: ModelManager): + self.text_encoder = model_manager.hunyuan_dit_clip_text_encoder + self.text_encoder_t5 = model_manager.hunyuan_dit_t5_text_encoder + self.dit = model_manager.hunyuan_dit + self.vae_decoder = model_manager.vae_decoder + self.vae_encoder = model_manager.vae_encoder + + + def fetch_prompter(self, model_manager: ModelManager): + self.prompter.load_from_model_manager(model_manager) + + + @staticmethod + def from_model_manager(model_manager: ModelManager): + pipe = HunyuanDiTImagePipeline( + device=model_manager.device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_main_models(model_manager) + pipe.fetch_prompter(model_manager) + return pipe + + + def preprocess_image(self, image): + image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0) + return image + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0] + image = image.cpu().permute(1, 2, 0).numpy() + image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8")) + return image + + + def prepare_extra_input(self, height=1024, width=1024, tiled=False, tile_size=64, tile_stride=32, batch_size=1): + if tiled: + height, width = tile_size * 16, tile_size * 16 + image_meta_size = torch.as_tensor([width, height, width, height, 0, 0]).to(device=self.device) + freqs_cis_img = self.image_size_manager.calc_rope(height, width) + image_meta_size = torch.stack([image_meta_size] * batch_size) + return { + "size_emb": image_meta_size, + "freq_cis_img": (freqs_cis_img[0].to(dtype=self.torch_dtype, device=self.device), freqs_cis_img[1].to(dtype=self.torch_dtype, device=self.device)), + "tiled": tiled, + "tile_size": tile_size, + "tile_stride": tile_stride + } + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + cfg_scale=7.5, + clip_skip=1, + clip_skip_2=1, + input_image=None, + reference_images=[], + reference_strengths=[0.4], + denoising_strength=1.0, + height=1024, + width=1024, + num_inference_steps=20, + tiled=False, + tile_size=64, + tile_stride=32, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + noise = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype) + if input_image is not None: + image = self.preprocess_image(input_image).to(device=self.device, dtype=torch.float32) + latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(self.torch_dtype) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = noise.clone() + + # Prepare reference latents + reference_latents = [] + for reference_image in reference_images: + reference_image = self.preprocess_image(reference_image).to(device=self.device, dtype=self.torch_dtype) + reference_latents.append(self.vae_encoder(reference_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(self.torch_dtype)) + + # Encode prompts + prompt_emb_posi, attention_mask_posi, prompt_emb_t5_posi, attention_mask_t5_posi = self.prompter.encode_prompt( + self.text_encoder, + self.text_encoder_t5, + prompt, + clip_skip=clip_skip, + clip_skip_2=clip_skip_2, + positive=True, + device=self.device + ) + if cfg_scale != 1.0: + prompt_emb_nega, attention_mask_nega, prompt_emb_t5_nega, attention_mask_t5_nega = self.prompter.encode_prompt( + self.text_encoder, + self.text_encoder_t5, + negative_prompt, + clip_skip=clip_skip, + clip_skip_2=clip_skip_2, + positive=False, + device=self.device + ) + + # Prepare positional id + extra_input = self.prepare_extra_input(height, width, tiled, tile_size) + + # Denoise + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = torch.tensor([timestep]).to(dtype=self.torch_dtype, device=self.device) + + # In-context reference + for reference_latents_, reference_strength in zip(reference_latents, reference_strengths): + if progress_id < num_inference_steps * reference_strength: + noisy_reference_latents = self.scheduler.add_noise(reference_latents_, noise, self.scheduler.timesteps[progress_id]) + self.dit( + noisy_reference_latents, + prompt_emb_posi, prompt_emb_t5_posi, attention_mask_posi, attention_mask_t5_posi, + timestep, + **extra_input, + to_cache=True + ) + # Positive side + noise_pred_posi = self.dit( + latents, + prompt_emb_posi, prompt_emb_t5_posi, attention_mask_posi, attention_mask_t5_posi, + timestep, + **extra_input, + ) + if cfg_scale != 1.0: + # Negative side + noise_pred_nega = self.dit( + latents, + prompt_emb_nega, prompt_emb_t5_nega, attention_mask_nega, attention_mask_t5_nega, + timestep, + **extra_input + ) + # Classifier-free guidance + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + image = self.decode_image(latents.to(torch.float32), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + return image diff --git a/diffsynth/pipelines/kwai_kolors.py b/diffsynth/pipelines/kwai_kolors.py new file mode 100644 index 0000000000000000000000000000000000000000..10cdd69300c73b6ef9dd053618d0bfcfe4b7ac80 --- /dev/null +++ b/diffsynth/pipelines/kwai_kolors.py @@ -0,0 +1,168 @@ +from ..models import ModelManager, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder, SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder +from ..models.kolors_text_encoder import ChatGLMModel +from ..prompts import KolorsPrompter +from ..schedulers import EnhancedDDIMScheduler +from .dancer import lets_dance_xl +import torch +from tqdm import tqdm +from PIL import Image +import numpy as np + + +class KolorsImagePipeline(torch.nn.Module): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__() + self.scheduler = EnhancedDDIMScheduler(beta_end=0.014, num_train_timesteps=1100) + self.prompter = KolorsPrompter() + self.device = device + self.torch_dtype = torch_dtype + # models + self.text_encoder: ChatGLMModel = None + self.unet: SDXLUNet = None + self.vae_decoder: SDXLVAEDecoder = None + self.vae_encoder: SDXLVAEEncoder = None + self.ipadapter_image_encoder: IpAdapterXLCLIPImageEmbedder = None + self.ipadapter: SDXLIpAdapter = None + + + def fetch_main_models(self, model_manager: ModelManager): + self.text_encoder = model_manager.kolors_text_encoder + self.unet = model_manager.kolors_unet + self.vae_decoder = model_manager.vae_decoder + self.vae_encoder = model_manager.vae_encoder + + + def fetch_ipadapter(self, model_manager: ModelManager): + if "ipadapter_xl" in model_manager.model: + self.ipadapter = model_manager.ipadapter_xl + if "ipadapter_xl_image_encoder" in model_manager.model: + self.ipadapter_image_encoder = model_manager.ipadapter_xl_image_encoder + + + def fetch_prompter(self, model_manager: ModelManager): + self.prompter.load_from_model_manager(model_manager) + + + @staticmethod + def from_model_manager(model_manager: ModelManager): + pipe = KolorsImagePipeline( + device=model_manager.device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_main_models(model_manager) + pipe.fetch_prompter(model_manager) + pipe.fetch_ipadapter(model_manager) + return pipe + + + def preprocess_image(self, image): + image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0) + return image + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0] + image = image.cpu().permute(1, 2, 0).numpy() + image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8")) + return image + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + cfg_scale=7.5, + clip_skip=2, + input_image=None, + ipadapter_images=None, + ipadapter_scale=1.0, + ipadapter_use_instant_style=False, + denoising_strength=1.0, + height=1024, + width=1024, + num_inference_steps=20, + tiled=False, + tile_size=64, + tile_stride=32, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if input_image is not None: + image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) + latents = self.vae_encoder(image.to(torch.float32), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(self.torch_dtype) + noise = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype) + + # Encode prompts + add_prompt_emb_posi, prompt_emb_posi = self.prompter.encode_prompt( + self.text_encoder, + prompt, + clip_skip=clip_skip, + device=self.device, + positive=True, + ) + if cfg_scale != 1.0: + add_prompt_emb_nega, prompt_emb_nega = self.prompter.encode_prompt( + self.text_encoder, + negative_prompt, + clip_skip=clip_skip, + device=self.device, + positive=False, + ) + + # Prepare positional id + add_time_id = torch.tensor([height, width, 0, 0, height, width], device=self.device) + + # IP-Adapter + if ipadapter_images is not None: + if ipadapter_use_instant_style: + self.ipadapter.set_less_adapter() + else: + self.ipadapter.set_full_adapter() + ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images) + ipadapter_kwargs_list_posi = self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale) + ipadapter_kwargs_list_nega = self.ipadapter(torch.zeros_like(ipadapter_image_encoding)) + else: + ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {}, {} + + # Denoise + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = torch.IntTensor((timestep,))[0].to(self.device) + + # Classifier-free guidance + noise_pred_posi = lets_dance_xl( + self.unet, + sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_posi, + add_time_id=add_time_id, add_text_embeds=add_prompt_emb_posi, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, + ipadapter_kwargs_list=ipadapter_kwargs_list_posi, + ) + if cfg_scale != 1.0: + noise_pred_nega = lets_dance_xl( + self.unet, + sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_nega, + add_time_id=add_time_id, add_text_embeds=add_prompt_emb_nega, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, + ipadapter_kwargs_list=ipadapter_kwargs_list_nega, + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + latents = self.scheduler.step(noise_pred, timestep, latents) + + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + image = self.decode_image(latents.to(torch.float32), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + return image diff --git a/diffsynth/pipelines/stable_diffusion.py b/diffsynth/pipelines/stable_diffusion.py new file mode 100644 index 0000000000000000000000000000000000000000..d3184bf003e228e18dc6f056cc824172f05c8f68 --- /dev/null +++ b/diffsynth/pipelines/stable_diffusion.py @@ -0,0 +1,167 @@ +from ..models import ModelManager, SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder, SDIpAdapter, IpAdapterCLIPImageEmbedder +from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator +from ..prompts import SDPrompter +from ..schedulers import EnhancedDDIMScheduler +from .dancer import lets_dance +from typing import List +import torch +from tqdm import tqdm +from PIL import Image +import numpy as np + + +class SDImagePipeline(torch.nn.Module): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__() + self.scheduler = EnhancedDDIMScheduler() + self.prompter = SDPrompter() + self.device = device + self.torch_dtype = torch_dtype + # models + self.text_encoder: SDTextEncoder = None + self.unet: SDUNet = None + self.vae_decoder: SDVAEDecoder = None + self.vae_encoder: SDVAEEncoder = None + self.controlnet: MultiControlNetManager = None + self.ipadapter_image_encoder: IpAdapterCLIPImageEmbedder = None + self.ipadapter: SDIpAdapter = None + + + def fetch_main_models(self, model_manager: ModelManager): + self.text_encoder = model_manager.text_encoder + self.unet = model_manager.unet + self.vae_decoder = model_manager.vae_decoder + self.vae_encoder = model_manager.vae_encoder + + + def fetch_controlnet_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[]): + controlnet_units = [] + for config in controlnet_config_units: + controlnet_unit = ControlNetUnit( + Annotator(config.processor_id, device=self.device), + model_manager.get_model_with_model_path(config.model_path), + config.scale + ) + controlnet_units.append(controlnet_unit) + self.controlnet = MultiControlNetManager(controlnet_units) + + + def fetch_ipadapter(self, model_manager: ModelManager): + if "ipadapter" in model_manager.model: + self.ipadapter = model_manager.ipadapter + if "ipadapter_image_encoder" in model_manager.model: + self.ipadapter_image_encoder = model_manager.ipadapter_image_encoder + + + def fetch_prompter(self, model_manager: ModelManager): + self.prompter.load_from_model_manager(model_manager) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[]): + pipe = SDImagePipeline( + device=model_manager.device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_main_models(model_manager) + pipe.fetch_prompter(model_manager) + pipe.fetch_controlnet_models(model_manager, controlnet_config_units) + pipe.fetch_ipadapter(model_manager) + return pipe + + + def preprocess_image(self, image): + image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0) + return image + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0] + image = image.cpu().permute(1, 2, 0).numpy() + image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8")) + return image + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + cfg_scale=7.5, + clip_skip=1, + input_image=None, + ipadapter_images=None, + ipadapter_scale=1.0, + controlnet_image=None, + denoising_strength=1.0, + height=512, + width=512, + num_inference_steps=20, + tiled=False, + tile_size=64, + tile_stride=32, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if input_image is not None: + image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) + latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + noise = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype) + + # Encode prompts + prompt_emb_posi = self.prompter.encode_prompt(self.text_encoder, prompt, clip_skip=clip_skip, device=self.device, positive=True) + prompt_emb_nega = self.prompter.encode_prompt(self.text_encoder, negative_prompt, clip_skip=clip_skip, device=self.device, positive=False) + + # IP-Adapter + if ipadapter_images is not None: + ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images) + ipadapter_kwargs_list_posi = self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale) + ipadapter_kwargs_list_nega = self.ipadapter(torch.zeros_like(ipadapter_image_encoding)) + else: + ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {}, {} + + # Prepare ControlNets + if controlnet_image is not None: + controlnet_image = self.controlnet.process_image(controlnet_image).to(device=self.device, dtype=self.torch_dtype) + controlnet_image = controlnet_image.unsqueeze(1) + + # Denoise + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = torch.IntTensor((timestep,))[0].to(self.device) + + # Classifier-free guidance + noise_pred_posi = lets_dance( + self.unet, motion_modules=None, controlnet=self.controlnet, + sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_posi, controlnet_frames=controlnet_image, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, + ipadapter_kwargs_list=ipadapter_kwargs_list_posi, + device=self.device, vram_limit_level=0 + ) + noise_pred_nega = lets_dance( + self.unet, motion_modules=None, controlnet=self.controlnet, + sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_nega, controlnet_frames=controlnet_image, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, + ipadapter_kwargs_list=ipadapter_kwargs_list_nega, + device=self.device, vram_limit_level=0 + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + + # DDIM + latents = self.scheduler.step(noise_pred, timestep, latents) + + # UI + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + return image diff --git a/diffsynth/pipelines/stable_diffusion_3.py b/diffsynth/pipelines/stable_diffusion_3.py new file mode 100644 index 0000000000000000000000000000000000000000..3309b61b7090ead265c88ed644e3936d2409af9f --- /dev/null +++ b/diffsynth/pipelines/stable_diffusion_3.py @@ -0,0 +1,130 @@ +from ..models import ModelManager, SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3, SD3DiT, SD3VAEDecoder, SD3VAEEncoder +from ..prompts import SD3Prompter +from ..schedulers import FlowMatchScheduler +import torch +from tqdm import tqdm +from PIL import Image +import numpy as np + + +class SD3ImagePipeline(torch.nn.Module): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__() + self.scheduler = FlowMatchScheduler() + self.prompter = SD3Prompter() + self.device = device + self.torch_dtype = torch_dtype + # models + self.text_encoder_1: SD3TextEncoder1 = None + self.text_encoder_2: SD3TextEncoder2 = None + self.text_encoder_3: SD3TextEncoder3 = None + self.dit: SD3DiT = None + self.vae_decoder: SD3VAEDecoder = None + self.vae_encoder: SD3VAEEncoder = None + + + def fetch_main_models(self, model_manager: ModelManager): + self.text_encoder_1 = model_manager.sd3_text_encoder_1 + self.text_encoder_2 = model_manager.sd3_text_encoder_2 + if "sd3_text_encoder_3" in model_manager.model: + self.text_encoder_3 = model_manager.sd3_text_encoder_3 + self.dit = model_manager.sd3_dit + self.vae_decoder = model_manager.sd3_vae_decoder + self.vae_encoder = model_manager.sd3_vae_encoder + + + def fetch_prompter(self, model_manager: ModelManager): + self.prompter.load_from_model_manager(model_manager) + + + @staticmethod + def from_model_manager(model_manager: ModelManager): + pipe = SD3ImagePipeline( + device=model_manager.device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_main_models(model_manager) + pipe.fetch_prompter(model_manager) + return pipe + + + def preprocess_image(self, image): + image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0) + return image + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0] + image = image.cpu().permute(1, 2, 0).numpy() + image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8")) + return image + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + cfg_scale=7.5, + input_image=None, + denoising_strength=1.0, + height=1024, + width=1024, + num_inference_steps=20, + tiled=False, + tile_size=128, + tile_stride=64, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if input_image is not None: + image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) + latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + noise = torch.randn((1, 16, height//8, width//8), device=self.device, dtype=self.torch_dtype) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = torch.randn((1, 16, height//8, width//8), device=self.device, dtype=self.torch_dtype) + + # Encode prompts + prompt_emb_posi, pooled_prompt_emb_posi = self.prompter.encode_prompt( + self.text_encoder_1, self.text_encoder_2, self.text_encoder_3, + prompt, + device=self.device, positive=True + ) + prompt_emb_nega, pooled_prompt_emb_nega = self.prompter.encode_prompt( + self.text_encoder_1, self.text_encoder_2, self.text_encoder_3, + negative_prompt, + device=self.device, positive=False + ) + + # Denoise + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = torch.Tensor((timestep,)).to(self.device) + + # Classifier-free guidance + noise_pred_posi = self.dit( + latents, timestep, prompt_emb_posi, pooled_prompt_emb_posi, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride + ) + noise_pred_nega = self.dit( + latents, timestep, prompt_emb_nega, pooled_prompt_emb_nega, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + + # DDIM + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + # UI + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + return image diff --git a/diffsynth/pipelines/stable_diffusion_video.py b/diffsynth/pipelines/stable_diffusion_video.py new file mode 100644 index 0000000000000000000000000000000000000000..7894d23c91c741f2f5dcfc527893ca1ea700a4d5 --- /dev/null +++ b/diffsynth/pipelines/stable_diffusion_video.py @@ -0,0 +1,356 @@ +from ..models import ModelManager, SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder, SDMotionModel +from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator +from ..prompts import SDPrompter +from ..schedulers import EnhancedDDIMScheduler +from ..data import VideoData, save_frames, save_video +from .dancer import lets_dance +from ..processors.sequencial_processor import SequencialProcessor +from typing import List +import torch, os, json +from tqdm import tqdm +from PIL import Image +import numpy as np + + +def lets_dance_with_long_video( + unet: SDUNet, + motion_modules: SDMotionModel = None, + controlnet: MultiControlNetManager = None, + sample = None, + timestep = None, + encoder_hidden_states = None, + controlnet_frames = None, + animatediff_batch_size = 16, + animatediff_stride = 8, + unet_batch_size = 1, + controlnet_batch_size = 1, + cross_frame_attention = False, + device = "cuda", + vram_limit_level = 0, +): + num_frames = sample.shape[0] + hidden_states_output = [(torch.zeros(sample[0].shape, dtype=sample[0].dtype), 0) for i in range(num_frames)] + + for batch_id in range(0, num_frames, animatediff_stride): + batch_id_ = min(batch_id + animatediff_batch_size, num_frames) + + # process this batch + hidden_states_batch = lets_dance( + unet, motion_modules, controlnet, + sample[batch_id: batch_id_].to(device), + timestep, + encoder_hidden_states[batch_id: batch_id_].to(device), + controlnet_frames=controlnet_frames[:, batch_id: batch_id_].to(device) if controlnet_frames is not None else None, + unet_batch_size=unet_batch_size, controlnet_batch_size=controlnet_batch_size, + cross_frame_attention=cross_frame_attention, + device=device, vram_limit_level=vram_limit_level + ).cpu() + + # update hidden_states + for i, hidden_states_updated in zip(range(batch_id, batch_id_), hidden_states_batch): + bias = max(1 - abs(i - (batch_id + batch_id_ - 1) / 2) / ((batch_id_ - batch_id - 1 + 1e-2) / 2), 1e-2) + hidden_states, num = hidden_states_output[i] + hidden_states = hidden_states * (num / (num + bias)) + hidden_states_updated * (bias / (num + bias)) + hidden_states_output[i] = (hidden_states, num + bias) + + if batch_id_ == num_frames: + break + + # output + hidden_states = torch.stack([h for h, _ in hidden_states_output]) + return hidden_states + + +class SDVideoPipeline(torch.nn.Module): + + def __init__(self, device="cuda", torch_dtype=torch.float16, use_animatediff=True): + super().__init__() + self.scheduler = EnhancedDDIMScheduler(beta_schedule="linear" if use_animatediff else "scaled_linear") + self.prompter = SDPrompter() + self.device = device + self.torch_dtype = torch_dtype + # models + self.text_encoder: SDTextEncoder = None + self.unet: SDUNet = None + self.vae_decoder: SDVAEDecoder = None + self.vae_encoder: SDVAEEncoder = None + self.controlnet: MultiControlNetManager = None + self.motion_modules: SDMotionModel = None + + + def fetch_main_models(self, model_manager: ModelManager): + self.text_encoder = model_manager.text_encoder + self.unet = model_manager.unet + self.vae_decoder = model_manager.vae_decoder + self.vae_encoder = model_manager.vae_encoder + + + def fetch_controlnet_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[]): + controlnet_units = [] + for config in controlnet_config_units: + controlnet_unit = ControlNetUnit( + Annotator(config.processor_id, device=self.device), + model_manager.get_model_with_model_path(config.model_path), + config.scale + ) + controlnet_units.append(controlnet_unit) + self.controlnet = MultiControlNetManager(controlnet_units) + + + def fetch_motion_modules(self, model_manager: ModelManager): + if "motion_modules" in model_manager.model: + self.motion_modules = model_manager.motion_modules + + + def fetch_prompter(self, model_manager: ModelManager): + self.prompter.load_from_model_manager(model_manager) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[]): + pipe = SDVideoPipeline( + device=model_manager.device, + torch_dtype=model_manager.torch_dtype, + use_animatediff="motion_modules" in model_manager.model + ) + pipe.fetch_main_models(model_manager) + pipe.fetch_motion_modules(model_manager) + pipe.fetch_prompter(model_manager) + pipe.fetch_controlnet_models(model_manager, controlnet_config_units) + return pipe + + + def preprocess_image(self, image): + image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0) + return image + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0] + image = image.cpu().permute(1, 2, 0).numpy() + image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8")) + return image + + + def decode_images(self, latents, tiled=False, tile_size=64, tile_stride=32): + images = [ + self.decode_image(latents[frame_id: frame_id+1], tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + for frame_id in range(latents.shape[0]) + ] + return images + + + def encode_images(self, processed_images, tiled=False, tile_size=64, tile_stride=32): + latents = [] + for image in processed_images: + image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) + latent = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).cpu() + latents.append(latent) + latents = torch.concat(latents, dim=0) + return latents + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + cfg_scale=7.5, + clip_skip=1, + num_frames=None, + input_frames=None, + controlnet_frames=None, + denoising_strength=1.0, + height=512, + width=512, + num_inference_steps=20, + animatediff_batch_size = 16, + animatediff_stride = 8, + unet_batch_size = 1, + controlnet_batch_size = 1, + cross_frame_attention = False, + smoother=None, + smoother_progress_ids=[], + vram_limit_level=0, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if self.motion_modules is None: + noise = torch.randn((1, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype).repeat(num_frames, 1, 1, 1) + else: + noise = torch.randn((num_frames, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype) + if input_frames is None or denoising_strength == 1.0: + latents = noise + else: + latents = self.encode_images(input_frames) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + + # Encode prompts + prompt_emb_posi = self.prompter.encode_prompt(self.text_encoder, prompt, clip_skip=clip_skip, device=self.device, positive=True).cpu() + prompt_emb_nega = self.prompter.encode_prompt(self.text_encoder, negative_prompt, clip_skip=clip_skip, device=self.device, positive=False).cpu() + prompt_emb_posi = prompt_emb_posi.repeat(num_frames, 1, 1) + prompt_emb_nega = prompt_emb_nega.repeat(num_frames, 1, 1) + + # Prepare ControlNets + if controlnet_frames is not None: + if isinstance(controlnet_frames[0], list): + controlnet_frames_ = [] + for processor_id in range(len(controlnet_frames)): + controlnet_frames_.append( + torch.stack([ + self.controlnet.process_image(controlnet_frame, processor_id=processor_id).to(self.torch_dtype) + for controlnet_frame in progress_bar_cmd(controlnet_frames[processor_id]) + ], dim=1) + ) + controlnet_frames = torch.concat(controlnet_frames_, dim=0) + else: + controlnet_frames = torch.stack([ + self.controlnet.process_image(controlnet_frame).to(self.torch_dtype) + for controlnet_frame in progress_bar_cmd(controlnet_frames) + ], dim=1) + + # Denoise + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = torch.IntTensor((timestep,))[0].to(self.device) + + # Classifier-free guidance + noise_pred_posi = lets_dance_with_long_video( + self.unet, motion_modules=self.motion_modules, controlnet=self.controlnet, + sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_posi, controlnet_frames=controlnet_frames, + animatediff_batch_size=animatediff_batch_size, animatediff_stride=animatediff_stride, + unet_batch_size=unet_batch_size, controlnet_batch_size=controlnet_batch_size, + cross_frame_attention=cross_frame_attention, + device=self.device, vram_limit_level=vram_limit_level + ) + noise_pred_nega = lets_dance_with_long_video( + self.unet, motion_modules=self.motion_modules, controlnet=self.controlnet, + sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_nega, controlnet_frames=controlnet_frames, + animatediff_batch_size=animatediff_batch_size, animatediff_stride=animatediff_stride, + unet_batch_size=unet_batch_size, controlnet_batch_size=controlnet_batch_size, + cross_frame_attention=cross_frame_attention, + device=self.device, vram_limit_level=vram_limit_level + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + + # DDIM and smoother + if smoother is not None and progress_id in smoother_progress_ids: + rendered_frames = self.scheduler.step(noise_pred, timestep, latents, to_final=True) + rendered_frames = self.decode_images(rendered_frames) + rendered_frames = smoother(rendered_frames, original_frames=input_frames) + target_latents = self.encode_images(rendered_frames) + noise_pred = self.scheduler.return_to_timestep(timestep, latents, target_latents) + latents = self.scheduler.step(noise_pred, timestep, latents) + + # UI + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + output_frames = self.decode_images(latents) + + # Post-process + if smoother is not None and (num_inference_steps in smoother_progress_ids or -1 in smoother_progress_ids): + output_frames = smoother(output_frames, original_frames=input_frames) + + return output_frames + + + +class SDVideoPipelineRunner: + def __init__(self, in_streamlit=False): + self.in_streamlit = in_streamlit + + + def load_pipeline(self, model_list, textual_inversion_folder, device, lora_alphas, controlnet_units): + # Load models + model_manager = ModelManager(torch_dtype=torch.float16, device=device) + model_manager.load_textual_inversions(textual_inversion_folder) + model_manager.load_models(model_list, lora_alphas=lora_alphas) + pipe = SDVideoPipeline.from_model_manager( + model_manager, + [ + ControlNetConfigUnit( + processor_id=unit["processor_id"], + model_path=unit["model_path"], + scale=unit["scale"] + ) for unit in controlnet_units + ] + ) + return model_manager, pipe + + + def load_smoother(self, model_manager, smoother_configs): + smoother = SequencialProcessor.from_model_manager(model_manager, smoother_configs) + return smoother + + + def synthesize_video(self, model_manager, pipe, seed, smoother, **pipeline_inputs): + torch.manual_seed(seed) + if self.in_streamlit: + import streamlit as st + progress_bar_st = st.progress(0.0) + output_video = pipe(**pipeline_inputs, smoother=smoother, progress_bar_st=progress_bar_st) + progress_bar_st.progress(1.0) + else: + output_video = pipe(**pipeline_inputs, smoother=smoother) + model_manager.to("cpu") + return output_video + + + def load_video(self, video_file, image_folder, height, width, start_frame_id, end_frame_id): + video = VideoData(video_file=video_file, image_folder=image_folder, height=height, width=width) + if start_frame_id is None: + start_frame_id = 0 + if end_frame_id is None: + end_frame_id = len(video) + frames = [video[i] for i in range(start_frame_id, end_frame_id)] + return frames + + + def add_data_to_pipeline_inputs(self, data, pipeline_inputs): + pipeline_inputs["input_frames"] = self.load_video(**data["input_frames"]) + pipeline_inputs["num_frames"] = len(pipeline_inputs["input_frames"]) + pipeline_inputs["width"], pipeline_inputs["height"] = pipeline_inputs["input_frames"][0].size + if len(data["controlnet_frames"]) > 0: + pipeline_inputs["controlnet_frames"] = [self.load_video(**unit) for unit in data["controlnet_frames"]] + return pipeline_inputs + + + def save_output(self, video, output_folder, fps, config): + os.makedirs(output_folder, exist_ok=True) + save_frames(video, os.path.join(output_folder, "frames")) + save_video(video, os.path.join(output_folder, "video.mp4"), fps=fps) + config["pipeline"]["pipeline_inputs"]["input_frames"] = [] + config["pipeline"]["pipeline_inputs"]["controlnet_frames"] = [] + with open(os.path.join(output_folder, "config.json"), 'w') as file: + json.dump(config, file, indent=4) + + + def run(self, config): + if self.in_streamlit: + import streamlit as st + if self.in_streamlit: st.markdown("Loading videos ...") + config["pipeline"]["pipeline_inputs"] = self.add_data_to_pipeline_inputs(config["data"], config["pipeline"]["pipeline_inputs"]) + if self.in_streamlit: st.markdown("Loading videos ... done!") + if self.in_streamlit: st.markdown("Loading models ...") + model_manager, pipe = self.load_pipeline(**config["models"]) + if self.in_streamlit: st.markdown("Loading models ... done!") + if "smoother_configs" in config: + if self.in_streamlit: st.markdown("Loading smoother ...") + smoother = self.load_smoother(model_manager, config["smoother_configs"]) + if self.in_streamlit: st.markdown("Loading smoother ... done!") + else: + smoother = None + if self.in_streamlit: st.markdown("Synthesizing videos ...") + output_video = self.synthesize_video(model_manager, pipe, config["pipeline"]["seed"], smoother, **config["pipeline"]["pipeline_inputs"]) + if self.in_streamlit: st.markdown("Synthesizing videos ... done!") + if self.in_streamlit: st.markdown("Saving videos ...") + self.save_output(output_video, config["data"]["output_folder"], config["data"]["fps"], config) + if self.in_streamlit: st.markdown("Saving videos ... done!") + if self.in_streamlit: st.markdown("Finished!") + video_file = open(os.path.join(os.path.join(config["data"]["output_folder"], "video.mp4")), 'rb') + if self.in_streamlit: st.video(video_file.read()) diff --git a/diffsynth/pipelines/stable_diffusion_xl.py b/diffsynth/pipelines/stable_diffusion_xl.py new file mode 100644 index 0000000000000000000000000000000000000000..aa4d73eb3b9b33b34d4db498e786121cdff0d66d --- /dev/null +++ b/diffsynth/pipelines/stable_diffusion_xl.py @@ -0,0 +1,180 @@ +from ..models import ModelManager, SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder, SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder +# TODO: SDXL ControlNet +from ..prompts import SDXLPrompter +from ..schedulers import EnhancedDDIMScheduler +from .dancer import lets_dance_xl +import torch +from tqdm import tqdm +from PIL import Image +import numpy as np + + +class SDXLImagePipeline(torch.nn.Module): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__() + self.scheduler = EnhancedDDIMScheduler() + self.prompter = SDXLPrompter() + self.device = device + self.torch_dtype = torch_dtype + # models + self.text_encoder: SDXLTextEncoder = None + self.text_encoder_2: SDXLTextEncoder2 = None + self.unet: SDXLUNet = None + self.vae_decoder: SDXLVAEDecoder = None + self.vae_encoder: SDXLVAEEncoder = None + self.ipadapter_image_encoder: IpAdapterXLCLIPImageEmbedder = None + self.ipadapter: SDXLIpAdapter = None + # TODO: SDXL ControlNet + + def fetch_main_models(self, model_manager: ModelManager): + self.text_encoder = model_manager.text_encoder + self.text_encoder_2 = model_manager.text_encoder_2 + self.unet = model_manager.unet + self.vae_decoder = model_manager.vae_decoder + self.vae_encoder = model_manager.vae_encoder + + + def fetch_controlnet_models(self, model_manager: ModelManager, **kwargs): + # TODO: SDXL ControlNet + pass + + + def fetch_ipadapter(self, model_manager: ModelManager): + if "ipadapter_xl" in model_manager.model: + self.ipadapter = model_manager.ipadapter_xl + if "ipadapter_xl_image_encoder" in model_manager.model: + self.ipadapter_image_encoder = model_manager.ipadapter_xl_image_encoder + + + def fetch_prompter(self, model_manager: ModelManager): + self.prompter.load_from_model_manager(model_manager) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, controlnet_config_units = [], **kwargs): + pipe = SDXLImagePipeline( + device=model_manager.device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_main_models(model_manager) + pipe.fetch_prompter(model_manager) + pipe.fetch_controlnet_models(model_manager, controlnet_config_units=controlnet_config_units) + pipe.fetch_ipadapter(model_manager) + return pipe + + + def preprocess_image(self, image): + image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0) + return image + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0] + image = image.cpu().permute(1, 2, 0).numpy() + image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8")) + return image + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + cfg_scale=7.5, + clip_skip=1, + clip_skip_2=2, + input_image=None, + ipadapter_images=None, + ipadapter_scale=1.0, + ipadapter_use_instant_style=False, + controlnet_image=None, + denoising_strength=1.0, + height=1024, + width=1024, + num_inference_steps=20, + tiled=False, + tile_size=64, + tile_stride=32, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if input_image is not None: + image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) + latents = self.vae_encoder(image.to(torch.float32), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(self.torch_dtype) + noise = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype) + + # Encode prompts + add_prompt_emb_posi, prompt_emb_posi = self.prompter.encode_prompt( + self.text_encoder, + self.text_encoder_2, + prompt, + clip_skip=clip_skip, clip_skip_2=clip_skip_2, + device=self.device, + positive=True, + ) + if cfg_scale != 1.0: + add_prompt_emb_nega, prompt_emb_nega = self.prompter.encode_prompt( + self.text_encoder, + self.text_encoder_2, + negative_prompt, + clip_skip=clip_skip, clip_skip_2=clip_skip_2, + device=self.device, + positive=False, + ) + + # Prepare positional id + add_time_id = torch.tensor([height, width, 0, 0, height, width], device=self.device) + + # IP-Adapter + if ipadapter_images is not None: + if ipadapter_use_instant_style: + self.ipadapter.set_less_adapter() + else: + self.ipadapter.set_full_adapter() + ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images) + ipadapter_kwargs_list_posi = self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale) + ipadapter_kwargs_list_nega = self.ipadapter(torch.zeros_like(ipadapter_image_encoding)) + else: + ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {}, {} + + # Denoise + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = torch.IntTensor((timestep,))[0].to(self.device) + + # Classifier-free guidance + noise_pred_posi = lets_dance_xl( + self.unet, + sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_posi, + add_time_id=add_time_id, add_text_embeds=add_prompt_emb_posi, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, + ipadapter_kwargs_list=ipadapter_kwargs_list_posi, + ) + if cfg_scale != 1.0: + noise_pred_nega = lets_dance_xl( + self.unet, + sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_nega, + add_time_id=add_time_id, add_text_embeds=add_prompt_emb_nega, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, + ipadapter_kwargs_list=ipadapter_kwargs_list_nega, + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + latents = self.scheduler.step(noise_pred, timestep, latents) + + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + image = self.decode_image(latents.to(torch.float32), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + return image diff --git a/diffsynth/pipelines/stable_diffusion_xl_video.py b/diffsynth/pipelines/stable_diffusion_xl_video.py new file mode 100644 index 0000000000000000000000000000000000000000..ceb4c674cb9577221c8b27529c886acc73f6136c --- /dev/null +++ b/diffsynth/pipelines/stable_diffusion_xl_video.py @@ -0,0 +1,190 @@ +from ..models import ModelManager, SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder, SDXLMotionModel +from .dancer import lets_dance_xl +# TODO: SDXL ControlNet +from ..prompts import SDXLPrompter +from ..schedulers import EnhancedDDIMScheduler +import torch +from tqdm import tqdm +from PIL import Image +import numpy as np + + +class SDXLVideoPipeline(torch.nn.Module): + + def __init__(self, device="cuda", torch_dtype=torch.float16, use_animatediff=True): + super().__init__() + self.scheduler = EnhancedDDIMScheduler(beta_schedule="linear" if use_animatediff else "scaled_linear") + self.prompter = SDXLPrompter() + self.device = device + self.torch_dtype = torch_dtype + # models + self.text_encoder: SDXLTextEncoder = None + self.text_encoder_2: SDXLTextEncoder2 = None + self.unet: SDXLUNet = None + self.vae_decoder: SDXLVAEDecoder = None + self.vae_encoder: SDXLVAEEncoder = None + # TODO: SDXL ControlNet + self.motion_modules: SDXLMotionModel = None + + + def fetch_main_models(self, model_manager: ModelManager): + self.text_encoder = model_manager.text_encoder + self.text_encoder_2 = model_manager.text_encoder_2 + self.unet = model_manager.unet + self.vae_decoder = model_manager.vae_decoder + self.vae_encoder = model_manager.vae_encoder + + + def fetch_controlnet_models(self, model_manager: ModelManager, **kwargs): + # TODO: SDXL ControlNet + pass + + + def fetch_motion_modules(self, model_manager: ModelManager): + if "motion_modules_xl" in model_manager.model: + self.motion_modules = model_manager.motion_modules_xl + + + def fetch_prompter(self, model_manager: ModelManager): + self.prompter.load_from_model_manager(model_manager) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, controlnet_config_units = [], **kwargs): + pipe = SDXLVideoPipeline( + device=model_manager.device, + torch_dtype=model_manager.torch_dtype, + use_animatediff="motion_modules_xl" in model_manager.model + ) + pipe.fetch_main_models(model_manager) + pipe.fetch_motion_modules(model_manager) + pipe.fetch_prompter(model_manager) + pipe.fetch_controlnet_models(model_manager, controlnet_config_units=controlnet_config_units) + return pipe + + + def preprocess_image(self, image): + image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0) + return image + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0] + image = image.cpu().permute(1, 2, 0).numpy() + image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8")) + return image + + + def decode_images(self, latents, tiled=False, tile_size=64, tile_stride=32): + images = [ + self.decode_image(latents[frame_id: frame_id+1], tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + for frame_id in range(latents.shape[0]) + ] + return images + + + def encode_images(self, processed_images, tiled=False, tile_size=64, tile_stride=32): + latents = [] + for image in processed_images: + image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) + latent = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).cpu() + latents.append(latent) + latents = torch.concat(latents, dim=0) + return latents + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + cfg_scale=7.5, + clip_skip=1, + clip_skip_2=2, + num_frames=None, + input_frames=None, + controlnet_frames=None, + denoising_strength=1.0, + height=512, + width=512, + num_inference_steps=20, + animatediff_batch_size = 16, + animatediff_stride = 8, + unet_batch_size = 1, + controlnet_batch_size = 1, + cross_frame_attention = False, + smoother=None, + smoother_progress_ids=[], + vram_limit_level=0, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if self.motion_modules is None: + noise = torch.randn((1, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype).repeat(num_frames, 1, 1, 1) + else: + noise = torch.randn((num_frames, 4, height//8, width//8), device="cuda", dtype=self.torch_dtype) + if input_frames is None or denoising_strength == 1.0: + latents = noise + else: + latents = self.encode_images(input_frames) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + + # Encode prompts + add_prompt_emb_posi, prompt_emb_posi = self.prompter.encode_prompt( + self.text_encoder, + self.text_encoder_2, + prompt, + clip_skip=clip_skip, clip_skip_2=clip_skip_2, + device=self.device, + positive=True, + ) + if cfg_scale != 1.0: + add_prompt_emb_nega, prompt_emb_nega = self.prompter.encode_prompt( + self.text_encoder, + self.text_encoder_2, + negative_prompt, + clip_skip=clip_skip, clip_skip_2=clip_skip_2, + device=self.device, + positive=False, + ) + + # Prepare positional id + add_time_id = torch.tensor([height, width, 0, 0, height, width], device=self.device) + + # Denoise + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = torch.IntTensor((timestep,))[0].to(self.device) + + # Classifier-free guidance + noise_pred_posi = lets_dance_xl( + self.unet, motion_modules=self.motion_modules, controlnet=None, + sample=latents, add_time_id=add_time_id, add_text_embeds=add_prompt_emb_posi, + timestep=timestep, encoder_hidden_states=prompt_emb_posi, controlnet_frames=controlnet_frames, + cross_frame_attention=cross_frame_attention, + device=self.device, vram_limit_level=vram_limit_level + ) + if cfg_scale != 1.0: + noise_pred_nega = lets_dance_xl( + self.unet, motion_modules=self.motion_modules, controlnet=None, + sample=latents, add_time_id=add_time_id, add_text_embeds=add_prompt_emb_nega, + timestep=timestep, encoder_hidden_states=prompt_emb_nega, controlnet_frames=controlnet_frames, + cross_frame_attention=cross_frame_attention, + device=self.device, vram_limit_level=vram_limit_level + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + latents = self.scheduler.step(noise_pred, timestep, latents) + + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + image = self.decode_images(latents.to(torch.float32)) + + return image diff --git a/diffsynth/pipelines/stable_video_diffusion.py b/diffsynth/pipelines/stable_video_diffusion.py new file mode 100644 index 0000000000000000000000000000000000000000..431303c5394b08a5ae3835bb0989d57083ab8029 --- /dev/null +++ b/diffsynth/pipelines/stable_video_diffusion.py @@ -0,0 +1,307 @@ +from ..models import ModelManager, SVDImageEncoder, SVDUNet, SVDVAEEncoder, SVDVAEDecoder +from ..schedulers import ContinuousODEScheduler +import torch +from tqdm import tqdm +from PIL import Image +import numpy as np +from einops import rearrange, repeat + + + +class SVDVideoPipeline(torch.nn.Module): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__() + self.scheduler = ContinuousODEScheduler() + self.device = device + self.torch_dtype = torch_dtype + # models + self.image_encoder: SVDImageEncoder = None + self.unet: SVDUNet = None + self.vae_encoder: SVDVAEEncoder = None + self.vae_decoder: SVDVAEDecoder = None + + + def fetch_main_models(self, model_manager: ModelManager): + self.image_encoder = model_manager.image_encoder + self.unet = model_manager.unet + self.vae_encoder = model_manager.vae_encoder + self.vae_decoder = model_manager.vae_decoder + + + @staticmethod + def from_model_manager(model_manager: ModelManager, **kwargs): + pipe = SVDVideoPipeline(device=model_manager.device, torch_dtype=model_manager.torch_dtype) + pipe.fetch_main_models(model_manager) + return pipe + + + def preprocess_image(self, image): + image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0) + return image + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0] + image = image.cpu().permute(1, 2, 0).numpy() + image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8")) + return image + + + def encode_image_with_clip(self, image): + image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) + image = SVDCLIPImageProcessor().resize_with_antialiasing(image, (224, 224)) + image = (image + 1.0) / 2.0 + mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).reshape(1, 3, 1, 1).to(device=self.device, dtype=self.torch_dtype) + std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).reshape(1, 3, 1, 1).to(device=self.device, dtype=self.torch_dtype) + image = (image - mean) / std + image_emb = self.image_encoder(image) + return image_emb + + + def encode_image_with_vae(self, image, noise_aug_strength): + image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) + noise = torch.randn(image.shape, device="cpu", dtype=self.torch_dtype).to(self.device) + image = image + noise_aug_strength * noise + image_emb = self.vae_encoder(image) / self.vae_encoder.scaling_factor + return image_emb + + + def encode_video_with_vae(self, video): + video = torch.concat([self.preprocess_image(frame) for frame in video], dim=0) + video = rearrange(video, "T C H W -> 1 C T H W") + video = video.to(device=self.device, dtype=self.torch_dtype) + latents = self.vae_encoder.encode_video(video) + latents = rearrange(latents[0], "C T H W -> T C H W") + return latents + + + def tensor2video(self, frames): + frames = rearrange(frames, "C T H W -> T H W C") + frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) + frames = [Image.fromarray(frame) for frame in frames] + return frames + + + def calculate_noise_pred( + self, + latents, + timestep, + add_time_id, + cfg_scales, + image_emb_vae_posi, image_emb_clip_posi, + image_emb_vae_nega, image_emb_clip_nega + ): + # Positive side + noise_pred_posi = self.unet( + torch.cat([latents, image_emb_vae_posi], dim=1), + timestep, image_emb_clip_posi, add_time_id + ) + # Negative side + noise_pred_nega = self.unet( + torch.cat([latents, image_emb_vae_nega], dim=1), + timestep, image_emb_clip_nega, add_time_id + ) + + # Classifier-free guidance + noise_pred = noise_pred_nega + cfg_scales * (noise_pred_posi - noise_pred_nega) + + return noise_pred + + + def post_process_latents(self, latents, post_normalize=True, contrast_enhance_scale=1.0): + if post_normalize: + mean, std = latents.mean(), latents.std() + latents = (latents - latents.mean(dim=[1, 2, 3], keepdim=True)) / latents.std(dim=[1, 2, 3], keepdim=True) * std + mean + latents = latents * contrast_enhance_scale + return latents + + + @torch.no_grad() + def __call__( + self, + input_image=None, + input_video=None, + mask_frames=[], + mask_frame_ids=[], + min_cfg_scale=1.0, + max_cfg_scale=3.0, + denoising_strength=1.0, + num_frames=25, + height=576, + width=1024, + fps=7, + motion_bucket_id=127, + noise_aug_strength=0.02, + num_inference_steps=20, + post_normalize=True, + contrast_enhance_scale=1.2, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength) + + # Prepare latent tensors + noise = torch.randn((num_frames, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype).to(self.device) + if denoising_strength == 1.0: + latents = noise.clone() + else: + latents = self.encode_video_with_vae(input_video) + latents = self.scheduler.add_noise(latents, noise, self.scheduler.timesteps[0]) + + # Prepare mask frames + if len(mask_frames) > 0: + mask_latents = self.encode_video_with_vae(mask_frames) + + # Encode image + image_emb_clip_posi = self.encode_image_with_clip(input_image) + image_emb_clip_nega = torch.zeros_like(image_emb_clip_posi) + image_emb_vae_posi = repeat(self.encode_image_with_vae(input_image, noise_aug_strength), "B C H W -> (B T) C H W", T=num_frames) + image_emb_vae_nega = torch.zeros_like(image_emb_vae_posi) + + # Prepare classifier-free guidance + cfg_scales = torch.linspace(min_cfg_scale, max_cfg_scale, num_frames) + cfg_scales = cfg_scales.reshape(num_frames, 1, 1, 1).to(device=self.device, dtype=self.torch_dtype) + + # Prepare positional id + add_time_id = torch.tensor([[fps-1, motion_bucket_id, noise_aug_strength]], device=self.device) + + # Denoise + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + + # Mask frames + for frame_id, mask_frame_id in enumerate(mask_frame_ids): + latents[mask_frame_id] = self.scheduler.add_noise(mask_latents[frame_id], noise[mask_frame_id], timestep) + + # Fetch model output + noise_pred = self.calculate_noise_pred( + latents, timestep, add_time_id, cfg_scales, + image_emb_vae_posi, image_emb_clip_posi, image_emb_vae_nega, image_emb_clip_nega + ) + + # Forward Euler + latents = self.scheduler.step(noise_pred, timestep, latents) + + # Update progress bar + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + latents = self.post_process_latents(latents, post_normalize=post_normalize, contrast_enhance_scale=contrast_enhance_scale) + video = self.vae_decoder.decode_video(latents, progress_bar=progress_bar_cmd) + video = self.tensor2video(video) + + return video + + + +class SVDCLIPImageProcessor: + def __init__(self): + pass + + def resize_with_antialiasing(self, input, size, interpolation="bicubic", align_corners=True): + h, w = input.shape[-2:] + factors = (h / size[0], w / size[1]) + + # First, we have to determine sigma + # Taken from skimage: https://github.com/scikit-image/scikit-image/blob/v0.19.2/skimage/transform/_warps.py#L171 + sigmas = ( + max((factors[0] - 1.0) / 2.0, 0.001), + max((factors[1] - 1.0) / 2.0, 0.001), + ) + + # Now kernel size. Good results are for 3 sigma, but that is kind of slow. Pillow uses 1 sigma + # https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Resample.c#L206 + # But they do it in the 2 passes, which gives better results. Let's try 2 sigmas for now + ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3)) + + # Make sure it is odd + if (ks[0] % 2) == 0: + ks = ks[0] + 1, ks[1] + + if (ks[1] % 2) == 0: + ks = ks[0], ks[1] + 1 + + input = self._gaussian_blur2d(input, ks, sigmas) + + output = torch.nn.functional.interpolate(input, size=size, mode=interpolation, align_corners=align_corners) + return output + + + def _compute_padding(self, kernel_size): + """Compute padding tuple.""" + # 4 or 6 ints: (padding_left, padding_right,padding_top,padding_bottom) + # https://pytorch.org/docs/stable/nn.html#torch.nn.functional.pad + if len(kernel_size) < 2: + raise AssertionError(kernel_size) + computed = [k - 1 for k in kernel_size] + + # for even kernels we need to do asymmetric padding :( + out_padding = 2 * len(kernel_size) * [0] + + for i in range(len(kernel_size)): + computed_tmp = computed[-(i + 1)] + + pad_front = computed_tmp // 2 + pad_rear = computed_tmp - pad_front + + out_padding[2 * i + 0] = pad_front + out_padding[2 * i + 1] = pad_rear + + return out_padding + + + def _filter2d(self, input, kernel): + # prepare kernel + b, c, h, w = input.shape + tmp_kernel = kernel[:, None, ...].to(device=input.device, dtype=input.dtype) + + tmp_kernel = tmp_kernel.expand(-1, c, -1, -1) + + height, width = tmp_kernel.shape[-2:] + + padding_shape: list[int] = self._compute_padding([height, width]) + input = torch.nn.functional.pad(input, padding_shape, mode="reflect") + + # kernel and input tensor reshape to align element-wise or batch-wise params + tmp_kernel = tmp_kernel.reshape(-1, 1, height, width) + input = input.view(-1, tmp_kernel.size(0), input.size(-2), input.size(-1)) + + # convolve the tensor with the kernel. + output = torch.nn.functional.conv2d(input, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1) + + out = output.view(b, c, h, w) + return out + + + def _gaussian(self, window_size: int, sigma): + if isinstance(sigma, float): + sigma = torch.tensor([[sigma]]) + + batch_size = sigma.shape[0] + + x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1) + + if window_size % 2 == 0: + x = x + 0.5 + + gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0))) + + return gauss / gauss.sum(-1, keepdim=True) + + + def _gaussian_blur2d(self, input, kernel_size, sigma): + if isinstance(sigma, tuple): + sigma = torch.tensor([sigma], dtype=input.dtype) + else: + sigma = sigma.to(dtype=input.dtype) + + ky, kx = int(kernel_size[0]), int(kernel_size[1]) + bs = sigma.shape[0] + kernel_x = self._gaussian(kx, sigma[:, 1].view(bs, 1)) + kernel_y = self._gaussian(ky, sigma[:, 0].view(bs, 1)) + out_x = self._filter2d(input, kernel_x[..., None, :]) + out = self._filter2d(out_x, kernel_y[..., None]) + + return out diff --git a/diffsynth/processors/FastBlend.py b/diffsynth/processors/FastBlend.py new file mode 100644 index 0000000000000000000000000000000000000000..fed33f4fdd215c8c9dc46f3b07d9453a12cc6b98 --- /dev/null +++ b/diffsynth/processors/FastBlend.py @@ -0,0 +1,142 @@ +from PIL import Image +import cupy as cp +import numpy as np +from tqdm import tqdm +from ..extensions.FastBlend.patch_match import PyramidPatchMatcher +from ..extensions.FastBlend.runners.fast import TableManager +from .base import VideoProcessor + + +class FastBlendSmoother(VideoProcessor): + def __init__( + self, + inference_mode="fast", batch_size=8, window_size=60, + minimum_patch_size=5, threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0, initialize="identity", tracking_window_size=0 + ): + self.inference_mode = inference_mode + self.batch_size = batch_size + self.window_size = window_size + self.ebsynth_config = { + "minimum_patch_size": minimum_patch_size, + "threads_per_block": threads_per_block, + "num_iter": num_iter, + "gpu_id": gpu_id, + "guide_weight": guide_weight, + "initialize": initialize, + "tracking_window_size": tracking_window_size + } + + @staticmethod + def from_model_manager(model_manager, **kwargs): + # TODO: fetch GPU ID from model_manager + return FastBlendSmoother(**kwargs) + + def inference_fast(self, frames_guide, frames_style): + table_manager = TableManager() + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + **self.ebsynth_config + ) + # left part + table_l = table_manager.build_remapping_table(frames_guide, frames_style, patch_match_engine, self.batch_size, desc="Fast Mode Step 1/4") + table_l = table_manager.remapping_table_to_blending_table(table_l) + table_l = table_manager.process_window_sum(frames_guide, table_l, patch_match_engine, self.window_size, self.batch_size, desc="Fast Mode Step 2/4") + # right part + table_r = table_manager.build_remapping_table(frames_guide[::-1], frames_style[::-1], patch_match_engine, self.batch_size, desc="Fast Mode Step 3/4") + table_r = table_manager.remapping_table_to_blending_table(table_r) + table_r = table_manager.process_window_sum(frames_guide[::-1], table_r, patch_match_engine, self.window_size, self.batch_size, desc="Fast Mode Step 4/4")[::-1] + # merge + frames = [] + for (frame_l, weight_l), frame_m, (frame_r, weight_r) in zip(table_l, frames_style, table_r): + weight_m = -1 + weight = weight_l + weight_m + weight_r + frame = frame_l * (weight_l / weight) + frame_m * (weight_m / weight) + frame_r * (weight_r / weight) + frames.append(frame) + frames = [frame.clip(0, 255).astype("uint8") for frame in frames] + frames = [Image.fromarray(frame) for frame in frames] + return frames + + def inference_balanced(self, frames_guide, frames_style): + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + **self.ebsynth_config + ) + output_frames = [] + # tasks + n = len(frames_style) + tasks = [] + for target in range(n): + for source in range(target - self.window_size, target + self.window_size + 1): + if source >= 0 and source < n and source != target: + tasks.append((source, target)) + # run + frames = [(None, 1) for i in range(n)] + for batch_id in tqdm(range(0, len(tasks), self.batch_size), desc="Balanced Mode"): + tasks_batch = tasks[batch_id: min(batch_id+self.batch_size, len(tasks))] + source_guide = np.stack([frames_guide[source] for source, target in tasks_batch]) + target_guide = np.stack([frames_guide[target] for source, target in tasks_batch]) + source_style = np.stack([frames_style[source] for source, target in tasks_batch]) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + for (source, target), result in zip(tasks_batch, target_style): + frame, weight = frames[target] + if frame is None: + frame = frames_style[target] + frames[target] = ( + frame * (weight / (weight + 1)) + result / (weight + 1), + weight + 1 + ) + if weight + 1 == min(n, target + self.window_size + 1) - max(0, target - self.window_size): + frame = frame.clip(0, 255).astype("uint8") + output_frames.append(Image.fromarray(frame)) + frames[target] = (None, 1) + return output_frames + + def inference_accurate(self, frames_guide, frames_style): + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + use_mean_target_style=True, + **self.ebsynth_config + ) + output_frames = [] + # run + n = len(frames_style) + for target in tqdm(range(n), desc="Accurate Mode"): + l, r = max(target - self.window_size, 0), min(target + self.window_size + 1, n) + remapped_frames = [] + for i in range(l, r, self.batch_size): + j = min(i + self.batch_size, r) + source_guide = np.stack([frames_guide[source] for source in range(i, j)]) + target_guide = np.stack([frames_guide[target]] * (j - i)) + source_style = np.stack([frames_style[source] for source in range(i, j)]) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + remapped_frames.append(target_style) + frame = np.concatenate(remapped_frames, axis=0).mean(axis=0) + frame = frame.clip(0, 255).astype("uint8") + output_frames.append(Image.fromarray(frame)) + return output_frames + + def release_vram(self): + mempool = cp.get_default_memory_pool() + pinned_mempool = cp.get_default_pinned_memory_pool() + mempool.free_all_blocks() + pinned_mempool.free_all_blocks() + + def __call__(self, rendered_frames, original_frames=None, **kwargs): + rendered_frames = [np.array(frame) for frame in rendered_frames] + original_frames = [np.array(frame) for frame in original_frames] + if self.inference_mode == "fast": + output_frames = self.inference_fast(original_frames, rendered_frames) + elif self.inference_mode == "balanced": + output_frames = self.inference_balanced(original_frames, rendered_frames) + elif self.inference_mode == "accurate": + output_frames = self.inference_accurate(original_frames, rendered_frames) + else: + raise ValueError("inference_mode must be fast, balanced or accurate") + self.release_vram() + return output_frames diff --git a/diffsynth/processors/PILEditor.py b/diffsynth/processors/PILEditor.py new file mode 100644 index 0000000000000000000000000000000000000000..01011d8724f61283550d503c5c20ae6fd0375ec7 --- /dev/null +++ b/diffsynth/processors/PILEditor.py @@ -0,0 +1,28 @@ +from PIL import ImageEnhance +from .base import VideoProcessor + + +class ContrastEditor(VideoProcessor): + def __init__(self, rate=1.5): + self.rate = rate + + @staticmethod + def from_model_manager(model_manager, **kwargs): + return ContrastEditor(**kwargs) + + def __call__(self, rendered_frames, **kwargs): + rendered_frames = [ImageEnhance.Contrast(i).enhance(self.rate) for i in rendered_frames] + return rendered_frames + + +class SharpnessEditor(VideoProcessor): + def __init__(self, rate=1.5): + self.rate = rate + + @staticmethod + def from_model_manager(model_manager, **kwargs): + return SharpnessEditor(**kwargs) + + def __call__(self, rendered_frames, **kwargs): + rendered_frames = [ImageEnhance.Sharpness(i).enhance(self.rate) for i in rendered_frames] + return rendered_frames diff --git a/diffsynth/processors/RIFE.py b/diffsynth/processors/RIFE.py new file mode 100644 index 0000000000000000000000000000000000000000..4186eb31496e9a1bf38df06eb64921226f07ee09 --- /dev/null +++ b/diffsynth/processors/RIFE.py @@ -0,0 +1,77 @@ +import torch +import numpy as np +from PIL import Image +from .base import VideoProcessor + + +class RIFESmoother(VideoProcessor): + def __init__(self, model, device="cuda", scale=1.0, batch_size=4, interpolate=True): + self.model = model + self.device = device + + # IFNet only does not support float16 + self.torch_dtype = torch.float32 + + # Other parameters + self.scale = scale + self.batch_size = batch_size + self.interpolate = interpolate + + @staticmethod + def from_model_manager(model_manager, **kwargs): + return RIFESmoother(model_manager.RIFE, device=model_manager.device, **kwargs) + + def process_image(self, image): + width, height = image.size + if width % 32 != 0 or height % 32 != 0: + width = (width + 31) // 32 + height = (height + 31) // 32 + image = image.resize((width, height)) + image = torch.Tensor(np.array(image, dtype=np.float32)[:, :, [2,1,0]] / 255).permute(2, 0, 1) + return image + + def process_images(self, images): + images = [self.process_image(image) for image in images] + images = torch.stack(images) + return images + + def decode_images(self, images): + images = (images[:, [2,1,0]].permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8) + images = [Image.fromarray(image) for image in images] + return images + + def process_tensors(self, input_tensor, scale=1.0, batch_size=4): + output_tensor = [] + for batch_id in range(0, input_tensor.shape[0], batch_size): + batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) + batch_input_tensor = input_tensor[batch_id: batch_id_] + batch_input_tensor = batch_input_tensor.to(device=self.device, dtype=self.torch_dtype) + flow, mask, merged = self.model(batch_input_tensor, [4/scale, 2/scale, 1/scale]) + output_tensor.append(merged[2].cpu()) + output_tensor = torch.concat(output_tensor, dim=0) + return output_tensor + + @torch.no_grad() + def __call__(self, rendered_frames, **kwargs): + # Preprocess + processed_images = self.process_images(rendered_frames) + + # Input + input_tensor = torch.cat((processed_images[:-2], processed_images[2:]), dim=1) + + # Interpolate + output_tensor = self.process_tensors(input_tensor, scale=self.scale, batch_size=self.batch_size) + + if self.interpolate: + # Blend + input_tensor = torch.cat((processed_images[1:-1], output_tensor), dim=1) + output_tensor = self.process_tensors(input_tensor, scale=self.scale, batch_size=self.batch_size) + processed_images[1:-1] = output_tensor + else: + processed_images[1:-1] = (processed_images[1:-1] + output_tensor) / 2 + + # To images + output_images = self.decode_images(processed_images) + if output_images[0].size != rendered_frames[0].size: + output_images = [image.resize(rendered_frames[0].size) for image in output_images] + return output_images diff --git a/diffsynth/processors/__init__.py b/diffsynth/processors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/diffsynth/processors/base.py b/diffsynth/processors/base.py new file mode 100644 index 0000000000000000000000000000000000000000..278a9c1b74044987cc116de35292a96de8b13737 --- /dev/null +++ b/diffsynth/processors/base.py @@ -0,0 +1,6 @@ +class VideoProcessor: + def __init__(self): + pass + + def __call__(self): + raise NotImplementedError diff --git a/diffsynth/processors/sequencial_processor.py b/diffsynth/processors/sequencial_processor.py new file mode 100644 index 0000000000000000000000000000000000000000..9b5bc9454f0b9d74f10bb4a6bff92db77f26325c --- /dev/null +++ b/diffsynth/processors/sequencial_processor.py @@ -0,0 +1,41 @@ +from .base import VideoProcessor + + +class AutoVideoProcessor(VideoProcessor): + def __init__(self): + pass + + @staticmethod + def from_model_manager(model_manager, processor_type, **kwargs): + if processor_type == "FastBlend": + from .FastBlend import FastBlendSmoother + return FastBlendSmoother.from_model_manager(model_manager, **kwargs) + elif processor_type == "Contrast": + from .PILEditor import ContrastEditor + return ContrastEditor.from_model_manager(model_manager, **kwargs) + elif processor_type == "Sharpness": + from .PILEditor import SharpnessEditor + return SharpnessEditor.from_model_manager(model_manager, **kwargs) + elif processor_type == "RIFE": + from .RIFE import RIFESmoother + return RIFESmoother.from_model_manager(model_manager, **kwargs) + else: + raise ValueError(f"invalid processor_type: {processor_type}") + + +class SequencialProcessor(VideoProcessor): + def __init__(self, processors=[]): + self.processors = processors + + @staticmethod + def from_model_manager(model_manager, configs): + processors = [ + AutoVideoProcessor.from_model_manager(model_manager, config["processor_type"], **config["config"]) + for config in configs + ] + return SequencialProcessor(processors) + + def __call__(self, rendered_frames, **kwargs): + for processor in self.processors: + rendered_frames = processor(rendered_frames, **kwargs) + return rendered_frames diff --git a/diffsynth/prompts/__init__.py b/diffsynth/prompts/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5309bb20233fb1aba0c4c38dae8f034c8947d003 --- /dev/null +++ b/diffsynth/prompts/__init__.py @@ -0,0 +1,5 @@ +from .sd_prompter import SDPrompter +from .sdxl_prompter import SDXLPrompter +from .sd3_prompter import SD3Prompter +from .hunyuan_dit_prompter import HunyuanDiTPrompter +from .kolors_prompter import KolorsPrompter diff --git a/diffsynth/prompts/hunyuan_dit_prompter.py b/diffsynth/prompts/hunyuan_dit_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..2f7d981ee8b0722f2d99fbe8b266a19aa7fd1ef8 --- /dev/null +++ b/diffsynth/prompts/hunyuan_dit_prompter.py @@ -0,0 +1,62 @@ +from .utils import Prompter +from transformers import BertModel, T5EncoderModel, BertTokenizer, AutoTokenizer +import warnings, os + + +class HunyuanDiTPrompter(Prompter): + def __init__( + self, + tokenizer_path=None, + tokenizer_t5_path=None + ): + if tokenizer_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_path = os.path.join(base_path, "tokenizer_configs/hunyuan_dit/tokenizer") + if tokenizer_t5_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_t5_path = os.path.join(base_path, "tokenizer_configs/hunyuan_dit/tokenizer_t5") + super().__init__() + self.tokenizer = BertTokenizer.from_pretrained(tokenizer_path) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + self.tokenizer_t5 = AutoTokenizer.from_pretrained(tokenizer_t5_path) + + + def encode_prompt_using_signle_model(self, prompt, text_encoder, tokenizer, max_length, clip_skip, device): + text_inputs = tokenizer( + prompt, + padding="max_length", + max_length=max_length, + truncation=True, + return_attention_mask=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + attention_mask = text_inputs.attention_mask.to(device) + prompt_embeds = text_encoder( + text_input_ids.to(device), + attention_mask=attention_mask, + clip_skip=clip_skip + ) + return prompt_embeds, attention_mask + + + def encode_prompt( + self, + text_encoder: BertModel, + text_encoder_t5: T5EncoderModel, + prompt, + clip_skip=1, + clip_skip_2=1, + positive=True, + device="cuda" + ): + prompt = self.process_prompt(prompt, positive=positive) + + # CLIP + prompt_emb, attention_mask = self.encode_prompt_using_signle_model(prompt, text_encoder, self.tokenizer, self.tokenizer.model_max_length, clip_skip, device) + + # T5 + prompt_emb_t5, attention_mask_t5 = self.encode_prompt_using_signle_model(prompt, text_encoder_t5, self.tokenizer_t5, self.tokenizer_t5.model_max_length, clip_skip_2, device) + + return prompt_emb, attention_mask, prompt_emb_t5, attention_mask_t5 diff --git a/diffsynth/prompts/kolors_prompter.py b/diffsynth/prompts/kolors_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..51fdcc875a5b42638a49c1039bb78558b0def445 --- /dev/null +++ b/diffsynth/prompts/kolors_prompter.py @@ -0,0 +1,347 @@ +from .utils import Prompter +import json, os, re +from typing import List, Optional, Union, Dict +from sentencepiece import SentencePieceProcessor +from transformers import PreTrainedTokenizer +from transformers.utils import PaddingStrategy +from transformers.tokenization_utils_base import EncodedInput, BatchEncoding +from ..models.kolors_text_encoder import ChatGLMModel + + +class SPTokenizer: + def __init__(self, model_path: str): + # reload tokenizer + assert os.path.isfile(model_path), model_path + self.sp_model = SentencePieceProcessor(model_file=model_path) + + # BOS / EOS token IDs + self.n_words: int = self.sp_model.vocab_size() + self.bos_id: int = self.sp_model.bos_id() + self.eos_id: int = self.sp_model.eos_id() + self.pad_id: int = self.sp_model.unk_id() + assert self.sp_model.vocab_size() == self.sp_model.get_piece_size() + + role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"] + special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens + self.special_tokens = {} + self.index_special_tokens = {} + for token in special_tokens: + self.special_tokens[token] = self.n_words + self.index_special_tokens[self.n_words] = token + self.n_words += 1 + self.role_special_token_expression = "|".join([re.escape(token) for token in role_special_tokens]) + + def tokenize(self, s: str, encode_special_tokens=False): + if encode_special_tokens: + last_index = 0 + t = [] + for match in re.finditer(self.role_special_token_expression, s): + if last_index < match.start(): + t.extend(self.sp_model.EncodeAsPieces(s[last_index:match.start()])) + t.append(s[match.start():match.end()]) + last_index = match.end() + if last_index < len(s): + t.extend(self.sp_model.EncodeAsPieces(s[last_index:])) + return t + else: + return self.sp_model.EncodeAsPieces(s) + + def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]: + assert type(s) is str + t = self.sp_model.encode(s) + if bos: + t = [self.bos_id] + t + if eos: + t = t + [self.eos_id] + return t + + def decode(self, t: List[int]) -> str: + text, buffer = "", [] + for token in t: + if token in self.index_special_tokens: + if buffer: + text += self.sp_model.decode(buffer) + buffer = [] + text += self.index_special_tokens[token] + else: + buffer.append(token) + if buffer: + text += self.sp_model.decode(buffer) + return text + + def decode_tokens(self, tokens: List[str]) -> str: + text = self.sp_model.DecodePieces(tokens) + return text + + def convert_token_to_id(self, token): + """ Converts a token (str) in an id using the vocab. """ + if token in self.special_tokens: + return self.special_tokens[token] + return self.sp_model.PieceToId(token) + + def convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + if index in self.index_special_tokens: + return self.index_special_tokens[index] + if index in [self.eos_id, self.bos_id, self.pad_id] or index < 0: + return "" + return self.sp_model.IdToPiece(index) + + + +class ChatGLMTokenizer(PreTrainedTokenizer): + vocab_files_names = {"vocab_file": "tokenizer.model"} + + model_input_names = ["input_ids", "attention_mask", "position_ids"] + + def __init__(self, vocab_file, padding_side="left", clean_up_tokenization_spaces=False, encode_special_tokens=False, + **kwargs): + self.name = "GLMTokenizer" + + self.vocab_file = vocab_file + self.tokenizer = SPTokenizer(vocab_file) + self.special_tokens = { + "": self.tokenizer.bos_id, + "": self.tokenizer.eos_id, + "": self.tokenizer.pad_id + } + self.encode_special_tokens = encode_special_tokens + super().__init__(padding_side=padding_side, clean_up_tokenization_spaces=clean_up_tokenization_spaces, + encode_special_tokens=encode_special_tokens, + **kwargs) + + def get_command(self, token): + if token in self.special_tokens: + return self.special_tokens[token] + assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}" + return self.tokenizer.special_tokens[token] + + @property + def unk_token(self) -> str: + return "" + + @property + def pad_token(self) -> str: + return "" + + @property + def pad_token_id(self): + return self.get_command("") + + @property + def eos_token(self) -> str: + return "" + + @property + def eos_token_id(self): + return self.get_command("") + + @property + def vocab_size(self): + return self.tokenizer.n_words + + def get_vocab(self): + """ Returns vocab as a dict """ + vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)} + vocab.update(self.added_tokens_encoder) + return vocab + + def _tokenize(self, text, **kwargs): + return self.tokenizer.tokenize(text, encode_special_tokens=self.encode_special_tokens) + + def _convert_token_to_id(self, token): + """ Converts a token (str) in an id using the vocab. """ + return self.tokenizer.convert_token_to_id(token) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + return self.tokenizer.convert_id_to_token(index) + + def convert_tokens_to_string(self, tokens: List[str]) -> str: + return self.tokenizer.decode_tokens(tokens) + + def save_vocabulary(self, save_directory, filename_prefix=None): + """ + Save the vocabulary and special tokens file to a directory. + + Args: + save_directory (`str`): + The directory in which to save the vocabulary. + filename_prefix (`str`, *optional*): + An optional prefix to add to the named of the saved files. + + Returns: + `Tuple(str)`: Paths to the files saved. + """ + if os.path.isdir(save_directory): + vocab_file = os.path.join( + save_directory, self.vocab_files_names["vocab_file"] + ) + else: + vocab_file = save_directory + + with open(self.vocab_file, 'rb') as fin: + proto_str = fin.read() + + with open(vocab_file, "wb") as writer: + writer.write(proto_str) + + return (vocab_file,) + + def get_prefix_tokens(self): + prefix_tokens = [self.get_command("[gMASK]"), self.get_command("sop")] + return prefix_tokens + + def build_single_message(self, role, metadata, message): + assert role in ["system", "user", "assistant", "observation"], role + role_tokens = [self.get_command(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n") + message_tokens = self.tokenizer.encode(message) + tokens = role_tokens + message_tokens + return tokens + + def build_chat_input(self, query, history=None, role="user"): + if history is None: + history = [] + input_ids = [] + for item in history: + content = item["content"] + if item["role"] == "system" and "tools" in item: + content = content + "\n" + json.dumps(item["tools"], indent=4, ensure_ascii=False) + input_ids.extend(self.build_single_message(item["role"], item.get("metadata", ""), content)) + input_ids.extend(self.build_single_message(role, "", query)) + input_ids.extend([self.get_command("<|assistant|>")]) + return self.batch_encode_plus([input_ids], return_tensors="pt", is_split_into_words=True) + + def build_inputs_with_special_tokens( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and + adding special tokens. A BERT sequence has the following format: + + - single sequence: `[CLS] X [SEP]` + - pair of sequences: `[CLS] A [SEP] B [SEP]` + + Args: + token_ids_0 (`List[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + """ + prefix_tokens = self.get_prefix_tokens() + token_ids_0 = prefix_tokens + token_ids_0 + if token_ids_1 is not None: + token_ids_0 = token_ids_0 + token_ids_1 + [self.get_command("")] + return token_ids_0 + + def _pad( + self, + encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], + max_length: Optional[int] = None, + padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, + pad_to_multiple_of: Optional[int] = None, + return_attention_mask: Optional[bool] = None, + ) -> dict: + """ + Pad encoded inputs (on left/right and up to predefined length or max length in the batch) + + Args: + encoded_inputs: + Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). + max_length: maximum length of the returned list and optionally padding length (see below). + Will truncate by taking into account the special tokens. + padding_strategy: PaddingStrategy to use for padding. + + - PaddingStrategy.LONGEST Pad to the longest sequence in the batch + - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) + - PaddingStrategy.DO_NOT_PAD: Do not pad + The tokenizer padding sides are defined in self.padding_side: + + - 'left': pads on the left of the sequences + - 'right': pads on the right of the sequences + pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. + This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability + `>= 7.5` (Volta). + return_attention_mask: + (optional) Set to False to avoid returning attention mask (default: set to model specifics) + """ + # Load from model defaults + assert self.padding_side == "left" + + required_input = encoded_inputs[self.model_input_names[0]] + seq_length = len(required_input) + + if padding_strategy == PaddingStrategy.LONGEST: + max_length = len(required_input) + + if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): + max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of + + needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length + + # Initialize attention mask if not present. + if "attention_mask" not in encoded_inputs: + encoded_inputs["attention_mask"] = [1] * seq_length + + if "position_ids" not in encoded_inputs: + encoded_inputs["position_ids"] = list(range(seq_length)) + + if needs_to_be_padded: + difference = max_length - len(required_input) + + if "attention_mask" in encoded_inputs: + encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] + if "position_ids" in encoded_inputs: + encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"] + encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input + + return encoded_inputs + + + +class KolorsPrompter(Prompter): + def __init__( + self, + tokenizer_path=None + ): + if tokenizer_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_path = os.path.join(base_path, "tokenizer_configs/kolors/tokenizer") + super().__init__() + self.tokenizer = ChatGLMTokenizer.from_pretrained(tokenizer_path) + + + def encode_prompt_using_ChatGLM(self, prompt, text_encoder, tokenizer, max_length, clip_skip, device): + text_inputs = tokenizer( + prompt, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ).to(device) + output = text_encoder( + input_ids=text_inputs['input_ids'] , + attention_mask=text_inputs['attention_mask'], + position_ids=text_inputs['position_ids'], + output_hidden_states=True + ) + prompt_emb = output.hidden_states[-clip_skip].permute(1, 0, 2).clone() + pooled_prompt_emb = output.hidden_states[-1][-1, :, :].clone() + return prompt_emb, pooled_prompt_emb + + + def encode_prompt( + self, + text_encoder: ChatGLMModel, + prompt, + clip_skip=2, + positive=True, + device="cuda" + ): + prompt = self.process_prompt(prompt, positive=positive) + prompt_emb, pooled_prompt_emb = self.encode_prompt_using_ChatGLM(prompt, text_encoder, self.tokenizer, 256, clip_skip, device) + + return pooled_prompt_emb, prompt_emb diff --git a/diffsynth/prompts/sd3_prompter.py b/diffsynth/prompts/sd3_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..c20de789bf52acb1c0865d6f906fd591ac9c7ead --- /dev/null +++ b/diffsynth/prompts/sd3_prompter.py @@ -0,0 +1,84 @@ +from .utils import Prompter +from transformers import CLIPTokenizer, T5TokenizerFast +import os, torch + + +class SD3Prompter(Prompter): + def __init__( + self, + tokenizer_1_path=None, + tokenizer_2_path=None, + tokenizer_3_path=None + ): + if tokenizer_1_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_1_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_1") + if tokenizer_2_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_2") + if tokenizer_3_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_3_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_3") + super().__init__() + self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_1_path) + self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path) + self.tokenizer_3 = T5TokenizerFast.from_pretrained(tokenizer_3_path) + + + def encode_prompt_using_clip(self, prompt, text_encoder, tokenizer, max_length, device): + input_ids = tokenizer( + prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True + ).input_ids.to(device) + pooled_prompt_emb, prompt_emb = text_encoder(input_ids) + return pooled_prompt_emb, prompt_emb + + + def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device): + input_ids = tokenizer( + prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True, + add_special_tokens=True, + ).input_ids.to(device) + prompt_emb = text_encoder(input_ids) + prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) + + return prompt_emb + + + def encode_prompt( + self, + text_encoder_1, + text_encoder_2, + text_encoder_3, + prompt, + positive=True, + device="cuda" + ): + prompt, pure_prompt = self.process_prompt(prompt, positive=positive, require_pure_prompt=True) + + # CLIP + pooled_prompt_emb_1, prompt_emb_1 = self.encode_prompt_using_clip(prompt, text_encoder_1, self.tokenizer, 77, device) + pooled_prompt_emb_2, prompt_emb_2 = self.encode_prompt_using_clip(pure_prompt, text_encoder_2, self.tokenizer_2, 77, device) + + # T5 + if text_encoder_3 is None: + prompt_emb_3 = torch.zeros((prompt_emb_1.shape[0], 256, 4096), dtype=prompt_emb_1.dtype, device=device) + else: + prompt_emb_3 = self.encode_prompt_using_t5(pure_prompt, text_encoder_3, self.tokenizer_3, 256, device) + prompt_emb_3 = prompt_emb_3.to(prompt_emb_1.dtype) # float32 -> float16 + + # Merge + prompt_emb = torch.cat([ + torch.nn.functional.pad(torch.cat([prompt_emb_1, prompt_emb_2], dim=-1), (0, 4096 - 768 - 1280)), + prompt_emb_3 + ], dim=-2) + pooled_prompt_emb = torch.cat([pooled_prompt_emb_1, pooled_prompt_emb_2], dim=-1) + + return prompt_emb, pooled_prompt_emb diff --git a/diffsynth/prompts/sd_prompter.py b/diffsynth/prompts/sd_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..9dea60f26fe3eb954881df9333c834dcd6d65b90 --- /dev/null +++ b/diffsynth/prompts/sd_prompter.py @@ -0,0 +1,21 @@ +from .utils import Prompter, tokenize_long_prompt +from transformers import CLIPTokenizer +from ..models import SDTextEncoder +import os + + +class SDPrompter(Prompter): + def __init__(self, tokenizer_path=None): + if tokenizer_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion/tokenizer") + super().__init__() + self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path) + + def encode_prompt(self, text_encoder: SDTextEncoder, prompt, clip_skip=1, device="cuda", positive=True): + prompt = self.process_prompt(prompt, positive=positive) + input_ids = tokenize_long_prompt(self.tokenizer, prompt).to(device) + prompt_emb = text_encoder(input_ids, clip_skip=clip_skip) + prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) + + return prompt_emb \ No newline at end of file diff --git a/diffsynth/prompts/sdxl_prompter.py b/diffsynth/prompts/sdxl_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..9c0a927da6f82d4d500a62a861f17c68870652df --- /dev/null +++ b/diffsynth/prompts/sdxl_prompter.py @@ -0,0 +1,53 @@ +from .utils import Prompter, tokenize_long_prompt +from transformers import CLIPTokenizer +from ..models import SDXLTextEncoder, SDXLTextEncoder2 +import torch, os + + +class SDXLPrompter(Prompter): + def __init__( + self, + tokenizer_path=None, + tokenizer_2_path=None + ): + if tokenizer_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion/tokenizer") + if tokenizer_2_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_xl/tokenizer_2") + super().__init__() + self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path) + self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path) + + def encode_prompt( + self, + text_encoder: SDXLTextEncoder, + text_encoder_2: SDXLTextEncoder2, + prompt, + clip_skip=1, + clip_skip_2=2, + positive=True, + device="cuda" + ): + prompt = self.process_prompt(prompt, positive=positive) + + # 1 + input_ids = tokenize_long_prompt(self.tokenizer, prompt).to(device) + prompt_emb_1 = text_encoder(input_ids, clip_skip=clip_skip) + + # 2 + input_ids_2 = tokenize_long_prompt(self.tokenizer_2, prompt).to(device) + add_text_embeds, prompt_emb_2 = text_encoder_2(input_ids_2, clip_skip=clip_skip_2) + + # Merge + if prompt_emb_1.shape[0] != prompt_emb_2.shape[0]: + max_batch_size = min(prompt_emb_1.shape[0], prompt_emb_2.shape[0]) + prompt_emb_1 = prompt_emb_1[: max_batch_size] + prompt_emb_2 = prompt_emb_2[: max_batch_size] + prompt_emb = torch.concatenate([prompt_emb_1, prompt_emb_2], dim=-1) + + # For very long prompt, we only use the first 77 tokens to compute `add_text_embeds`. + add_text_embeds = add_text_embeds[0:1] + prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) + return add_text_embeds, prompt_emb diff --git a/diffsynth/prompts/utils.py b/diffsynth/prompts/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..92cb0f4480d075267681ca35c0251a23104dafef --- /dev/null +++ b/diffsynth/prompts/utils.py @@ -0,0 +1,144 @@ +from transformers import CLIPTokenizer, AutoTokenizer +from ..models import ModelManager +import os + + +def tokenize_long_prompt(tokenizer, prompt): + # Get model_max_length from self.tokenizer + length = tokenizer.model_max_length + + # To avoid the warning. set self.tokenizer.model_max_length to +oo. + tokenizer.model_max_length = 99999999 + + # Tokenize it! + input_ids = tokenizer(prompt, return_tensors="pt").input_ids + + # Determine the real length. + max_length = (input_ids.shape[1] + length - 1) // length * length + + # Restore tokenizer.model_max_length + tokenizer.model_max_length = length + + # Tokenize it again with fixed length. + input_ids = tokenizer( + prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True + ).input_ids + + # Reshape input_ids to fit the text encoder. + num_sentence = input_ids.shape[1] // length + input_ids = input_ids.reshape((num_sentence, length)) + + return input_ids + + +class BeautifulPrompt: + def __init__(self, tokenizer_path=None, model=None): + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + self.model = model + self.template = 'Instruction: Give a simple description of the image to generate a drawing prompt.\nInput: {raw_prompt}\nOutput:' + + def __call__(self, raw_prompt): + model_input = self.template.format(raw_prompt=raw_prompt) + input_ids = self.tokenizer.encode(model_input, return_tensors='pt').to(self.model.device) + outputs = self.model.generate( + input_ids, + max_new_tokens=384, + do_sample=True, + temperature=0.9, + top_k=50, + top_p=0.95, + repetition_penalty=1.1, + num_return_sequences=1 + ) + prompt = raw_prompt + ", " + self.tokenizer.batch_decode( + outputs[:, input_ids.size(1):], + skip_special_tokens=True + )[0].strip() + return prompt + + +class Translator: + def __init__(self, tokenizer_path=None, model=None): + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + self.model = model + + def __call__(self, prompt): + input_ids = self.tokenizer.encode(prompt, return_tensors='pt').to(self.model.device) + output_ids = self.model.generate(input_ids) + prompt = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0] + return prompt + + +class Prompter: + def __init__(self): + self.tokenizer: CLIPTokenizer = None + self.keyword_dict = {} + self.translator: Translator = None + self.beautiful_prompt: BeautifulPrompt = None + + def load_textual_inversion(self, textual_inversion_dict): + self.keyword_dict = {} + additional_tokens = [] + for keyword in textual_inversion_dict: + tokens, _ = textual_inversion_dict[keyword] + additional_tokens += tokens + self.keyword_dict[keyword] = " " + " ".join(tokens) + " " + if self.tokenizer is not None: + self.tokenizer.add_tokens(additional_tokens) + + def load_beautiful_prompt(self, model, model_path): + model_folder = os.path.dirname(model_path) + self.beautiful_prompt = BeautifulPrompt(tokenizer_path=model_folder, model=model) + if model_folder.endswith("v2"): + self.beautiful_prompt.template = """Converts a simple image description into a prompt. \ +Prompts are formatted as multiple related tags separated by commas, plus you can use () to increase the weight, [] to decrease the weight, \ +or use a number to specify the weight. You should add appropriate words to make the images described in the prompt more aesthetically pleasing, \ +but make sure there is a correlation between the input and output.\n\ +### Input: {raw_prompt}\n### Output:""" + + def load_translator(self, model, model_path): + model_folder = os.path.dirname(model_path) + self.translator = Translator(tokenizer_path=model_folder, model=model) + + def load_from_model_manager(self, model_manager: ModelManager): + self.load_textual_inversion(model_manager.textual_inversion_dict) + if "translator" in model_manager.model: + self.load_translator(model_manager.model["translator"], model_manager.model_path["translator"]) + if "beautiful_prompt" in model_manager.model: + self.load_beautiful_prompt(model_manager.model["beautiful_prompt"], model_manager.model_path["beautiful_prompt"]) + + def add_textual_inversion_tokens(self, prompt): + for keyword in self.keyword_dict: + if keyword in prompt: + prompt = prompt.replace(keyword, self.keyword_dict[keyword]) + return prompt + + def del_textual_inversion_tokens(self, prompt): + for keyword in self.keyword_dict: + if keyword in prompt: + prompt = prompt.replace(keyword, "") + return prompt + + def process_prompt(self, prompt, positive=True, require_pure_prompt=False): + if isinstance(prompt, list): + prompt = [self.process_prompt(prompt_, positive=positive, require_pure_prompt=require_pure_prompt) for prompt_ in prompt] + if require_pure_prompt: + prompt, pure_prompt = [i[0] for i in prompt], [i[1] for i in prompt] + return prompt, pure_prompt + else: + return prompt + prompt, pure_prompt = self.add_textual_inversion_tokens(prompt), self.del_textual_inversion_tokens(prompt) + if positive and self.translator is not None: + prompt = self.translator(prompt) + print(f"Your prompt is translated: \"{prompt}\"") + if positive and self.beautiful_prompt is not None: + prompt = self.beautiful_prompt(prompt) + print(f"Your prompt is refined by BeautifulPrompt: \"{prompt}\"") + if require_pure_prompt: + return prompt, pure_prompt + else: + return prompt diff --git a/diffsynth/schedulers/__init__.py b/diffsynth/schedulers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0ec43257b687c9b5504e08e05763332755566ea5 --- /dev/null +++ b/diffsynth/schedulers/__init__.py @@ -0,0 +1,3 @@ +from .ddim import EnhancedDDIMScheduler +from .continuous_ode import ContinuousODEScheduler +from .flow_match import FlowMatchScheduler diff --git a/diffsynth/schedulers/continuous_ode.py b/diffsynth/schedulers/continuous_ode.py new file mode 100644 index 0000000000000000000000000000000000000000..e6cd8378ad0c09b5ed02c2b22feba5562bd22aff --- /dev/null +++ b/diffsynth/schedulers/continuous_ode.py @@ -0,0 +1,59 @@ +import torch + + +class ContinuousODEScheduler(): + + def __init__(self, num_inference_steps=100, sigma_max=700.0, sigma_min=0.002, rho=7.0): + self.sigma_max = sigma_max + self.sigma_min = sigma_min + self.rho = rho + self.set_timesteps(num_inference_steps) + + + def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0): + ramp = torch.linspace(1-denoising_strength, 1, num_inference_steps) + min_inv_rho = torch.pow(torch.tensor((self.sigma_min,)), (1 / self.rho)) + max_inv_rho = torch.pow(torch.tensor((self.sigma_max,)), (1 / self.rho)) + self.sigmas = torch.pow(max_inv_rho + ramp * (min_inv_rho - max_inv_rho), self.rho) + self.timesteps = torch.log(self.sigmas) * 0.25 + + + def step(self, model_output, timestep, sample, to_final=False): + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + sample *= (sigma*sigma + 1).sqrt() + estimated_sample = -sigma / (sigma*sigma + 1).sqrt() * model_output + 1 / (sigma*sigma + 1) * sample + if to_final or timestep_id + 1 >= len(self.timesteps): + prev_sample = estimated_sample + else: + sigma_ = self.sigmas[timestep_id + 1] + derivative = 1 / sigma * (sample - estimated_sample) + prev_sample = sample + derivative * (sigma_ - sigma) + prev_sample /= (sigma_*sigma_ + 1).sqrt() + return prev_sample + + + def return_to_timestep(self, timestep, sample, sample_stablized): + # This scheduler doesn't support this function. + pass + + + def add_noise(self, original_samples, noise, timestep): + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + sample = (original_samples + noise * sigma) / (sigma*sigma + 1).sqrt() + return sample + + + def training_target(self, sample, noise, timestep): + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + target = (-(sigma*sigma + 1).sqrt() / sigma + 1 / (sigma*sigma + 1).sqrt() / sigma) * sample + 1 / (sigma*sigma + 1).sqrt() * noise + return target + + + def training_weight(self, timestep): + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + weight = (1 + sigma*sigma).sqrt() / sigma + return weight diff --git a/diffsynth/schedulers/ddim.py b/diffsynth/schedulers/ddim.py new file mode 100644 index 0000000000000000000000000000000000000000..4b4d9806d9937da4d64955ebda8fb59475c907c6 --- /dev/null +++ b/diffsynth/schedulers/ddim.py @@ -0,0 +1,73 @@ +import torch, math + + +class EnhancedDDIMScheduler(): + + def __init__(self, num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", prediction_type="epsilon"): + self.num_train_timesteps = num_train_timesteps + if beta_schedule == "scaled_linear": + betas = torch.square(torch.linspace(math.sqrt(beta_start), math.sqrt(beta_end), num_train_timesteps, dtype=torch.float32)) + elif beta_schedule == "linear": + betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) + else: + raise NotImplementedError(f"{beta_schedule} is not implemented") + self.alphas_cumprod = torch.cumprod(1.0 - betas, dim=0).tolist() + self.set_timesteps(10) + self.prediction_type = prediction_type + + + def set_timesteps(self, num_inference_steps, denoising_strength=1.0): + # The timesteps are aligned to 999...0, which is different from other implementations, + # but I think this implementation is more reasonable in theory. + max_timestep = max(round(self.num_train_timesteps * denoising_strength) - 1, 0) + num_inference_steps = min(num_inference_steps, max_timestep + 1) + if num_inference_steps == 1: + self.timesteps = [max_timestep] + else: + step_length = max_timestep / (num_inference_steps - 1) + self.timesteps = [round(max_timestep - i*step_length) for i in range(num_inference_steps)] + + + def denoise(self, model_output, sample, alpha_prod_t, alpha_prod_t_prev): + if self.prediction_type == "epsilon": + weight_e = math.sqrt(1 - alpha_prod_t_prev) - math.sqrt(alpha_prod_t_prev * (1 - alpha_prod_t) / alpha_prod_t) + weight_x = math.sqrt(alpha_prod_t_prev / alpha_prod_t) + prev_sample = sample * weight_x + model_output * weight_e + elif self.prediction_type == "v_prediction": + weight_e = -math.sqrt(alpha_prod_t_prev * (1 - alpha_prod_t)) + math.sqrt(alpha_prod_t * (1 - alpha_prod_t_prev)) + weight_x = math.sqrt(alpha_prod_t * alpha_prod_t_prev) + math.sqrt((1 - alpha_prod_t) * (1 - alpha_prod_t_prev)) + prev_sample = sample * weight_x + model_output * weight_e + else: + raise NotImplementedError(f"{self.prediction_type} is not implemented") + return prev_sample + + + def step(self, model_output, timestep, sample, to_final=False): + alpha_prod_t = self.alphas_cumprod[timestep] + timestep_id = self.timesteps.index(timestep) + if to_final or timestep_id + 1 >= len(self.timesteps): + alpha_prod_t_prev = 1.0 + else: + timestep_prev = self.timesteps[timestep_id + 1] + alpha_prod_t_prev = self.alphas_cumprod[timestep_prev] + + return self.denoise(model_output, sample, alpha_prod_t, alpha_prod_t_prev) + + + def return_to_timestep(self, timestep, sample, sample_stablized): + alpha_prod_t = self.alphas_cumprod[timestep] + noise_pred = (sample - math.sqrt(alpha_prod_t) * sample_stablized) / math.sqrt(1 - alpha_prod_t) + return noise_pred + + + def add_noise(self, original_samples, noise, timestep): + sqrt_alpha_prod = math.sqrt(self.alphas_cumprod[timestep]) + sqrt_one_minus_alpha_prod = math.sqrt(1 - self.alphas_cumprod[timestep]) + noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise + return noisy_samples + + def training_target(self, sample, noise, timestep): + sqrt_alpha_prod = math.sqrt(self.alphas_cumprod[timestep]) + sqrt_one_minus_alpha_prod = math.sqrt(1 - self.alphas_cumprod[timestep]) + target = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample + return target diff --git a/diffsynth/schedulers/flow_match.py b/diffsynth/schedulers/flow_match.py new file mode 100644 index 0000000000000000000000000000000000000000..359f7ee391ace9653fc068e451cd5970bd559f37 --- /dev/null +++ b/diffsynth/schedulers/flow_match.py @@ -0,0 +1,47 @@ +import torch + + + +class FlowMatchScheduler(): + + def __init__(self, num_inference_steps=100, num_train_timesteps=1000, shift=3.0, sigma_max=1.0, sigma_min=0.003/1.002): + self.num_train_timesteps = num_train_timesteps + self.shift = shift + self.sigma_max = sigma_max + self.sigma_min = sigma_min + self.set_timesteps(num_inference_steps) + + + def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0): + sigma_start = self.sigma_min + (self.sigma_max - self.sigma_min) * denoising_strength + self.sigmas = torch.linspace(sigma_start, self.sigma_min, num_inference_steps) + self.sigmas = self.shift * self.sigmas / (1 + (self.shift - 1) * self.sigmas) + self.timesteps = self.sigmas * self.num_train_timesteps + + + def step(self, model_output, timestep, sample, to_final=False): + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + if to_final or timestep_id + 1 >= len(self.timesteps): + sigma_ = 0 + else: + sigma_ = self.sigmas[timestep_id + 1] + prev_sample = sample + model_output * (sigma_ - sigma) + return prev_sample + + + def return_to_timestep(self, timestep, sample, sample_stablized): + # This scheduler doesn't support this function. + pass + + + def add_noise(self, original_samples, noise, timestep): + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + sample = (1 - sigma) * original_samples + sigma * noise + return sample + + + def training_target(self, sample, noise, timestep): + target = noise - sample + return target diff --git a/diffsynth/tokenizer_configs/hunyuan_dit/tokenizer/special_tokens_map.json b/diffsynth/tokenizer_configs/hunyuan_dit/tokenizer/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..a8b3208c2884c4efb86e49300fdd3dc877220cdf --- /dev/null +++ b/diffsynth/tokenizer_configs/hunyuan_dit/tokenizer/special_tokens_map.json @@ -0,0 +1,7 @@ +{ + "cls_token": "[CLS]", + "mask_token": "[MASK]", + "pad_token": "[PAD]", + "sep_token": "[SEP]", + "unk_token": "[UNK]" +} diff --git a/diffsynth/tokenizer_configs/hunyuan_dit/tokenizer/tokenizer_config.json b/diffsynth/tokenizer_configs/hunyuan_dit/tokenizer/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..a14356073e11a885074a7cdbddc749463cefd911 --- /dev/null +++ b/diffsynth/tokenizer_configs/hunyuan_dit/tokenizer/tokenizer_config.json @@ -0,0 +1,16 @@ +{ + "cls_token": "[CLS]", + "do_basic_tokenize": true, + "do_lower_case": true, + "mask_token": "[MASK]", + "name_or_path": "hfl/chinese-roberta-wwm-ext", + "never_split": null, + "pad_token": "[PAD]", + "sep_token": "[SEP]", + "special_tokens_map_file": "/home/chenweifeng/.cache/huggingface/hub/models--hfl--chinese-roberta-wwm-ext/snapshots/5c58d0b8ec1d9014354d691c538661bf00bfdb44/special_tokens_map.json", + "strip_accents": null, + "tokenize_chinese_chars": true, + "tokenizer_class": "BertTokenizer", + "unk_token": "[UNK]", + "model_max_length": 77 +} diff --git a/diffsynth/tokenizer_configs/hunyuan_dit/tokenizer/vocab.txt b/diffsynth/tokenizer_configs/hunyuan_dit/tokenizer/vocab.txt new file mode 100644 index 0000000000000000000000000000000000000000..6246906805d02aca01714c71e4c8d77b69a7a131 --- /dev/null +++ b/diffsynth/tokenizer_configs/hunyuan_dit/tokenizer/vocab.txt @@ -0,0 +1,47020 @@ +[PAD] +[unused1] +[unused2] +[unused3] +[unused4] +[unused5] +[unused6] +[unused7] +[unused8] +[unused9] +[unused10] +[unused11] +[unused12] +[unused13] +[unused14] +[unused15] +[unused16] +[unused17] +[unused18] +[unused19] +[unused20] +[unused21] +[unused22] +[unused23] +[unused24] +[unused25] +[unused26] +[unused27] +[unused28] +[unused29] +[unused30] +[unused31] +[unused32] +[unused33] +[unused34] +[unused35] +[unused36] +[unused37] +[unused38] +[unused39] +[unused40] +[unused41] +[unused42] +[unused43] +[unused44] +[unused45] +[unused46] +[unused47] +[unused48] +[unused49] +[unused50] +[unused51] +[unused52] +[unused53] +[unused54] +[unused55] +[unused56] +[unused57] +[unused58] +[unused59] +[unused60] +[unused61] +[unused62] +[unused63] +[unused64] +[unused65] +[unused66] +[unused67] +[unused68] +[unused69] +[unused70] +[unused71] +[unused72] +[unused73] +[unused74] +[unused75] +[unused76] +[unused77] +[unused78] +[unused79] +[unused80] +[unused81] +[unused82] +[unused83] +[unused84] +[unused85] +[unused86] +[unused87] +[unused88] +[unused89] +[unused90] +[unused91] +[unused92] +[unused93] +[unused94] +[unused95] +[unused96] +[unused97] +[unused98] +[unused99] +[UNK] +[CLS] +[SEP] +[MASK] + + +! +" +# +$ +% +& +' +( +) +* ++ +, +- +. +/ +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +: +; +< += +> +? +@ +[ +\ +] +^ +_ +a +b +c +d +e +f +g +h +i +j +k +l +m +n +o +p +q +r +s +t +u +v +w +x +y +z +{ +| +} +~ +£ +¤ +¥ +§ +© +« +® +° +± +² +³ +µ +· +¹ +º +» +¼ +× +ß +æ +÷ +ø +đ +ŋ +ɔ +ə +ɡ +ʰ +ˇ +ˈ +ˊ +ˋ +ˍ +ː +˙ +˚ +ˢ +α +β +γ +δ +ε +η +θ +ι +κ +λ +μ +ν +ο +π +ρ +ς +σ +τ +υ +φ +χ +ψ +ω +а +б +в +г +д +е +ж +з +и +к +л +м +н +о +п +р +с +т +у +ф +х +ц +ч +ш +ы +ь +я +і +ا +ب +ة +ت +د +ر +س +ع +ل +م +ن +ه +و +ي +۩ +ก +ง +น +ม +ย +ร +อ +า +เ +๑ +་ +ღ +ᄀ +ᄁ +ᄂ +ᄃ +ᄅ +ᄆ +ᄇ +ᄈ +ᄉ +ᄋ +ᄌ +ᄎ +ᄏ +ᄐ +ᄑ +ᄒ +ᅡ +ᅢ +ᅣ +ᅥ +ᅦ +ᅧ +ᅨ +ᅩ +ᅪ +ᅬ +ᅭ +ᅮ +ᅯ +ᅲ +ᅳ +ᅴ +ᅵ +ᆨ +ᆫ +ᆯ +ᆷ +ᆸ +ᆺ +ᆻ +ᆼ +ᗜ +ᵃ +ᵉ +ᵍ +ᵏ +ᵐ +ᵒ +ᵘ +‖ +„ +† +• +‥ +‧ +
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+ "ļ": 248, + "ļ": 504, + "Ľ": 249, + "Ľ": 505, + "ľ": 250, + "ľ": 506, + "ľë": 39810, + "Ŀ": 251, + "Ŀ": 507, + "ŀ": 252, + "ŀ": 508, + "Ł": 253, + "Ł": 509, + "ŁãģĦ": 46023, + "ł": 254, + "ł": 510, + "łï¸ı": 27899, + "łï¸ı": 12715, + "łĪ": 43364, + "Ń": 255, + "Ń": 511 +} + \ No newline at end of file diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..ba447dc377a27f2c384894a560d58758d6a5edc8 --- /dev/null +++ b/environment.yml @@ -0,0 +1,21 @@ +name: DiffSynthStudio +channels: + - pytorch + - nvidia + - defaults +dependencies: + - python=3.9.16 + - pip=23.0.1 + - cudatoolkit + - pytorch + - cupy + - pip: + - transformers + - controlnet-aux==0.0.7 + - streamlit + - streamlit-drawable-canvas + - imageio + - imageio[ffmpeg] + - safetensors + - einops + - sentencepiece diff --git a/examples/Diffutoon/Diffutoon.ipynb b/examples/Diffutoon/Diffutoon.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..302e03d75789a54fc04b85dda43b259cdc045a4d --- /dev/null +++ b/examples/Diffutoon/Diffutoon.ipynb @@ -0,0 +1,512 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "8ObdI5jCB8xy" + }, + "source": [ + "# DiffSynth Studio\n", + "\n", + "Welcome to DiffSynth Studio! This is an example of Diffutoon." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XSkKX7O2BwuM" + }, + "source": [ + "## Install" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "msCpt0pLnT8W", + "outputId": "35d93b35-451b-4760-d1ee-ef7ff190916e" + }, + "outputs": [], + "source": [ + "!git clone https://github.com/Artiprocher/DiffSynth-Studio.git\n", + "!pip install -q transformers controlnet-aux==0.0.7 streamlit streamlit-drawable-canvas imageio imageio[ffmpeg] safetensors einops cupy-cuda12x\n", + "%cd /content/DiffSynth-Studio" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5eCu_rlKB3kK" + }, + "source": [ + "## Download Models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9znMkpVj3qZ1" + }, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "\n", + "def download_model(url, file_path):\n", + " model_file = requests.get(url, allow_redirects=True)\n", + " with open(file_path, \"wb\") as f:\n", + " f.write(model_file.content)\n", + "\n", + "download_model(\"https://civitai.com/api/download/models/229575\", \"models/stable_diffusion/aingdiffusion_v12.safetensors\")\n", + "download_model(\"https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15_v2.ckpt\", \"models/AnimateDiff/mm_sd_v15_v2.ckpt\")\n", + "download_model(\"https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart.pth\", \"models/ControlNet/control_v11p_sd15_lineart.pth\")\n", + "download_model(\"https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile.pth\", \"models/ControlNet/control_v11f1e_sd15_tile.pth\")\n", + "download_model(\"https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth.pth\", \"models/ControlNet/control_v11f1p_sd15_depth.pth\")\n", + "download_model(\"https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge.pth\", \"models/ControlNet/control_v11p_sd15_softedge.pth\")\n", + "download_model(\"https://huggingface.co/lllyasviel/Annotators/resolve/main/dpt_hybrid-midas-501f0c75.pt\", \"models/Annotators/dpt_hybrid-midas-501f0c75.pt\")\n", + "download_model(\"https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetHED.pth\", \"models/Annotators/ControlNetHED.pth\")\n", + "download_model(\"https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model.pth\", \"models/Annotators/sk_model.pth\")\n", + "download_model(\"https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model2.pth\", \"models/Annotators/sk_model2.pth\")\n", + "download_model(\"https://civitai.com/api/download/models/25820?type=Model&format=PickleTensor&size=full&fp=fp16\", \"models/textual_inversion/verybadimagenegative_v1.3.pt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iwOq2lWtKVYS" + }, + "source": [ + "## Run Diffutoon" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tII_XRY-PJeo" + }, + "source": [ + "### Config Template" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vsd2alA3PrGe" + }, + "outputs": [], + "source": [ + "config_stage_1_template = {\n", + " \"models\": {\n", + " \"model_list\": [\n", + " \"models/stable_diffusion/aingdiffusion_v12.safetensors\",\n", + " \"models/ControlNet/control_v11p_sd15_softedge.pth\",\n", + " \"models/ControlNet/control_v11f1p_sd15_depth.pth\"\n", + " ],\n", + " \"textual_inversion_folder\": \"models/textual_inversion\",\n", + " \"device\": \"cuda\",\n", + " \"lora_alphas\": [],\n", + " \"controlnet_units\": [\n", + " {\n", + " \"processor_id\": \"softedge\",\n", + " \"model_path\": \"models/ControlNet/control_v11p_sd15_softedge.pth\",\n", + " \"scale\": 0.5\n", + " },\n", + " {\n", + " \"processor_id\": \"depth\",\n", + " \"model_path\": \"models/ControlNet/control_v11f1p_sd15_depth.pth\",\n", + " \"scale\": 0.5\n", + " }\n", + " ]\n", + " },\n", + " \"data\": {\n", + " \"input_frames\": {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 512,\n", + " \"width\": 512,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + " },\n", + " \"controlnet_frames\": [\n", + " {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 512,\n", + " \"width\": 512,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + " },\n", + " {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 512,\n", + " \"width\": 512,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + " }\n", + " ],\n", + " \"output_folder\": \"data/examples/diffutoon_edit/color_video\",\n", + " \"fps\": 25\n", + " },\n", + " \"smoother_configs\": [\n", + " {\n", + " \"processor_type\": \"FastBlend\",\n", + " \"config\": {}\n", + " }\n", + " ],\n", + " \"pipeline\": {\n", + " \"seed\": 0,\n", + " \"pipeline_inputs\": {\n", + " \"prompt\": \"best quality, perfect anime illustration, orange clothes, night, a girl is dancing, smile, solo, black silk stockings\",\n", + " \"negative_prompt\": \"verybadimagenegative_v1.3\",\n", + " \"cfg_scale\": 7.0,\n", + " \"clip_skip\": 1,\n", + " \"denoising_strength\": 0.9,\n", + " \"num_inference_steps\": 20,\n", + " \"animatediff_batch_size\": 8,\n", + " \"animatediff_stride\": 4,\n", + " \"unet_batch_size\": 8,\n", + " \"controlnet_batch_size\": 8,\n", + " \"cross_frame_attention\": True,\n", + " \"smoother_progress_ids\": [-1],\n", + " # The following parameters will be overwritten. You don't need to modify them.\n", + " \"input_frames\": [],\n", + " \"num_frames\": 30,\n", + " \"width\": 512,\n", + " \"height\": 512,\n", + " \"controlnet_frames\": []\n", + " }\n", + " }\n", + "}\n", + "\n", + "config_stage_2_template = {\n", + " \"models\": {\n", + " \"model_list\": [\n", + " \"models/stable_diffusion/aingdiffusion_v12.safetensors\",\n", + " \"models/AnimateDiff/mm_sd_v15_v2.ckpt\",\n", + " \"models/ControlNet/control_v11f1e_sd15_tile.pth\",\n", + " \"models/ControlNet/control_v11p_sd15_lineart.pth\"\n", + " ],\n", + " \"textual_inversion_folder\": \"models/textual_inversion\",\n", + " \"device\": \"cuda\",\n", + " \"lora_alphas\": [],\n", + " \"controlnet_units\": [\n", + " {\n", + " \"processor_id\": \"tile\",\n", + " \"model_path\": \"models/ControlNet/control_v11f1e_sd15_tile.pth\",\n", + " \"scale\": 0.5\n", + " },\n", + " {\n", + " \"processor_id\": \"lineart\",\n", + " \"model_path\": \"models/ControlNet/control_v11p_sd15_lineart.pth\",\n", + " \"scale\": 0.5\n", + " }\n", + " ]\n", + " },\n", + " \"data\": {\n", + " \"input_frames\": {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 1024,\n", + " \"width\": 1024,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + " },\n", + " \"controlnet_frames\": [\n", + " {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 1024,\n", + " \"width\": 1024,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + " },\n", + " {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 1024,\n", + " \"width\": 1024,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + " }\n", + " ],\n", + " \"output_folder\": \"/content/output\",\n", + " \"fps\": 25\n", + " },\n", + " \"pipeline\": {\n", + " \"seed\": 0,\n", + " \"pipeline_inputs\": {\n", + " \"prompt\": \"best quality, perfect anime illustration, light, a girl is dancing, smile, solo\",\n", + " \"negative_prompt\": \"verybadimagenegative_v1.3\",\n", + " \"cfg_scale\": 7.0,\n", + " \"clip_skip\": 2,\n", + " \"denoising_strength\": 1.0,\n", + " \"num_inference_steps\": 10,\n", + " \"animatediff_batch_size\": 16,\n", + " \"animatediff_stride\": 8,\n", + " \"unet_batch_size\": 1,\n", + " \"controlnet_batch_size\": 1,\n", + " \"cross_frame_attention\": False,\n", + " # The following parameters will be overwritten. You don't need to modify them.\n", + " \"input_frames\": [],\n", + " \"num_frames\": 30,\n", + " \"width\": 1536,\n", + " \"height\": 1536,\n", + " \"controlnet_frames\": []\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "113QAmNHP6T_" + }, + "source": [ + "### Upload Input Video\n", + "\n", + "Before you run the following code, please upload your input video to `/content/input_video.mp4`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CyqAsj1o5U9B" + }, + "source": [ + "### Toon Shading\n", + "\n", + "Render your video in an anime style.\n", + "\n", + "We highly recommend you to use a higher resolution for better visual quality. The default resolution of Diffutoon is 1536x1536, which requires 22GB VRAM. If you don't have enough VRAM, 1024x1024 is also acceptable.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "761nbrgeKMvj", + "outputId": "c0d47d5f-16e9-4a65-e664-9bd5fc491111" + }, + "outputs": [], + "source": [ + "from diffsynth import SDVideoPipelineRunner\n", + "\n", + "\n", + "config = config_stage_2_template.copy()\n", + "config[\"data\"][\"input_frames\"] = {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 1024,\n", + " \"width\": 1024,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + "}\n", + "config[\"data\"][\"controlnet_frames\"] = [config[\"data\"][\"input_frames\"], config[\"data\"][\"input_frames\"]]\n", + "config[\"data\"][\"output_folder\"] = \"/content/toon_video\"\n", + "config[\"data\"][\"fps\"] = 25\n", + "\n", + "runner = SDVideoPipelineRunner()\n", + "runner.run(config)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9wujhGUmDIwY" + }, + "source": [ + "Let's see the video!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 420 + }, + "id": "TBNAigacAq6h", + "outputId": "8f57c3b4-982b-4643-f3dc-53c51bd85a4b" + }, + "outputs": [], + "source": [ + "from IPython.display import HTML\n", + "from base64 import b64encode\n", + "\n", + "mp4 = open(\"/content/toon_video/video.mp4\", \"rb\").read()\n", + "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", + "HTML(\"\"\"\n", + "\n", + "\"\"\" % data_url)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "48hQfX--5YGi" + }, + "source": [ + "### Toon Shading with Editing Signals" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bAQ9Zq-3-MH6" + }, + "source": [ + "In stage 1, input your prompt, and diffutoon will generate the editing signals in the format of low-resolution color video." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BtDzYgIq5bgg", + "outputId": "bb27b7b9-7979-4409-f476-f25f0a164ef4" + }, + "outputs": [], + "source": [ + "from diffsynth import SDVideoPipelineRunner\n", + "\n", + "\n", + "config_stage_1 = config_stage_1_template.copy()\n", + "config_stage_1[\"data\"][\"input_frames\"] = {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 512,\n", + " \"width\": 512,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + "}\n", + "config_stage_1[\"data\"][\"controlnet_frames\"] = [config_stage_1[\"data\"][\"input_frames\"], config_stage_1[\"data\"][\"input_frames\"]]\n", + "config_stage_1[\"data\"][\"output_folder\"] = \"/content/color_video\"\n", + "config_stage_1[\"data\"][\"fps\"] = 25\n", + "config_stage_1[\"pipeline\"][\"pipeline_inputs\"][\"prompt\"] = \"best quality, perfect anime illustration, orange clothes, night, a girl is dancing, smile, solo, black silk stockings\"\n", + "\n", + "runner = SDVideoPipelineRunner()\n", + "runner.run(config_stage_1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D9_AWwhi-pA9" + }, + "source": [ + "In stage 2, diffutoon will rerender the whole video according to the editing signals." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JFysCk7y51i_", + "outputId": "475050d3-c72e-4e08-b55c-d59ed86b5497" + }, + "outputs": [], + "source": [ + "from diffsynth import SDVideoPipelineRunner\n", + "\n", + "\n", + "config_stage_2 = config_stage_2_template.copy()\n", + "config_stage_2[\"data\"][\"input_frames\"] = {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 1024,\n", + " \"width\": 1024,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + "}\n", + "config_stage_2[\"data\"][\"controlnet_frames\"][0] = {\n", + " \"video_file\": \"/content/color_video/video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": config_stage_2[\"data\"][\"input_frames\"][\"height\"],\n", + " \"width\": config_stage_2[\"data\"][\"input_frames\"][\"width\"],\n", + " \"start_frame_id\": None,\n", + " \"end_frame_id\": None\n", + "}\n", + "config_stage_2[\"data\"][\"controlnet_frames\"][1] = config[\"data\"][\"input_frames\"]\n", + "config_stage_2[\"data\"][\"output_folder\"] = \"/content/edit_video\"\n", + "config_stage_2[\"data\"][\"fps\"] = 25\n", + "\n", + "runner = SDVideoPipelineRunner()\n", + "runner.run(config)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HIPrCAIS_Im0" + }, + "source": [ + "Let's see the video!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 420 + }, + "id": "Y2nz7rew-7VI", + "outputId": "fbcbadc6-4045-4aac-dfb0-80bacec003bf" + }, + "outputs": [], + "source": [ + "from IPython.display import HTML\n", + "from base64 import b64encode\n", + "\n", + "mp4 = open(\"/content/edit_video/video.mp4\", \"rb\").read()\n", + "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", + "HTML(\"\"\"\n", + "\n", + "\"\"\" % data_url)" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [ + "tII_XRY-PJeo" + ], + "gpuType": "T4", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/Diffutoon/README.md b/examples/Diffutoon/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8b2caf0600f96cadcf7014648a464ba1886a65eb --- /dev/null +++ b/examples/Diffutoon/README.md @@ -0,0 +1,21 @@ +# Diffutoon + +[Diffutoon](https://arxiv.org/abs/2401.16224) is a toon shading approach. This approach is adept for rendering high-resoluton videos with rapid motion. + +## Example: Toon Shading (Diffutoon) + +Directly render realistic videos in a flatten style. In this example, you can easily modify the parameters in the config dict. See [`diffutoon_toon_shading.py`](./diffutoon_toon_shading.py). We also provide [an example on Colab](https://colab.research.google.com/github/Artiprocher/DiffSynth-Studio/blob/main/examples/Diffutoon.ipynb). + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-4709-be5e-b39af82404dd + +## Example: Toon Shading with Editing Signals (Diffutoon) + +This example supports video editing signals. See [`diffutoon_toon_shading_with_editing_signals.py`](./diffutoon_toon_shading_with_editing_signals.py). The editing feature is also supported in the [Colab example](https://colab.research.google.com/github/Artiprocher/DiffSynth-Studio/blob/main/examples/Diffutoon/Diffutoon.ipynb). + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/20528af5-5100-474a-8cdc-440b9efdd86c + +## Example: Toon Shading (in native Python code) + +This example is provided for developers. If you don't want to use the config to manage parameters, you can see [`sd_toon_shading.py`](./sd_toon_shading.py) to learn how to use it in native Python code. + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/607c199b-6140-410b-a111-3e4ffb01142c diff --git a/examples/Diffutoon/diffutoon_toon_shading.py b/examples/Diffutoon/diffutoon_toon_shading.py new file mode 100644 index 0000000000000000000000000000000000000000..2406a807acf528fa9e8a9c55e688008ab170330c --- /dev/null +++ b/examples/Diffutoon/diffutoon_toon_shading.py @@ -0,0 +1,100 @@ +from diffsynth import SDVideoPipelineRunner, download_models + + +# Download models (automatically) +# `models/stable_diffusion/aingdiffusion_v12.safetensors`: [link](https://civitai.com/api/download/models/229575) +# `models/AnimateDiff/mm_sd_v15_v2.ckpt`: [link](https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15_v2.ckpt) +# `models/ControlNet/control_v11p_sd15_lineart.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart.pth) +# `models/ControlNet/control_v11f1e_sd15_tile.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile.pth) +# `models/Annotators/sk_model.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model.pth) +# `models/Annotators/sk_model2.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model2.pth) +# `models/textual_inversion/verybadimagenegative_v1.3.pt`: [link](https://civitai.com/api/download/models/25820?type=Model&format=PickleTensor&size=full&fp=fp16) +download_models([ + "AingDiffusion_v12", + "AnimateDiff_v2", + "ControlNet_v11p_sd15_lineart", + "ControlNet_v11f1e_sd15_tile", + "TextualInversion_VeryBadImageNegative_v1.3" +]) +# The original video in the example is https://www.bilibili.com/video/BV1iG411a7sQ/. + +config = { + "models": { + "model_list": [ + "models/stable_diffusion/aingdiffusion_v12.safetensors", + "models/AnimateDiff/mm_sd_v15_v2.ckpt", + "models/ControlNet/control_v11f1e_sd15_tile.pth", + "models/ControlNet/control_v11p_sd15_lineart.pth" + ], + "textual_inversion_folder": "models/textual_inversion", + "device": "cuda", + "lora_alphas": [], + "controlnet_units": [ + { + "processor_id": "tile", + "model_path": "models/ControlNet/control_v11f1e_sd15_tile.pth", + "scale": 0.5 + }, + { + "processor_id": "lineart", + "model_path": "models/ControlNet/control_v11p_sd15_lineart.pth", + "scale": 0.5 + } + ] + }, + "data": { + "input_frames": { + "video_file": "data/examples/diffutoon/input_video.mp4", + "image_folder": None, + "height": 1536, + "width": 1536, + "start_frame_id": 0, + "end_frame_id": 30 + }, + "controlnet_frames": [ + { + "video_file": "data/examples/diffutoon/input_video.mp4", + "image_folder": None, + "height": 1536, + "width": 1536, + "start_frame_id": 0, + "end_frame_id": 30 + }, + { + "video_file": "data/examples/diffutoon/input_video.mp4", + "image_folder": None, + "height": 1536, + "width": 1536, + "start_frame_id": 0, + "end_frame_id": 30 + } + ], + "output_folder": "output", + "fps": 30 + }, + "pipeline": { + "seed": 0, + "pipeline_inputs": { + "prompt": "best quality, perfect anime illustration, light, a girl is dancing, smile, solo", + "negative_prompt": "verybadimagenegative_v1.3", + "cfg_scale": 7.0, + "clip_skip": 2, + "denoising_strength": 1.0, + "num_inference_steps": 10, + "animatediff_batch_size": 16, + "animatediff_stride": 8, + "unet_batch_size": 1, + "controlnet_batch_size": 1, + "cross_frame_attention": False, + # The following parameters will be overwritten. You don't need to modify them. + "input_frames": [], + "num_frames": 30, + "width": 1536, + "height": 1536, + "controlnet_frames": [] + } + } +} + +runner = SDVideoPipelineRunner() +runner.run(config) diff --git a/examples/Diffutoon/diffutoon_toon_shading_with_editing_signals.py b/examples/Diffutoon/diffutoon_toon_shading_with_editing_signals.py new file mode 100644 index 0000000000000000000000000000000000000000..d07236cf1e37d1b222e924577b5329dad2abf608 --- /dev/null +++ b/examples/Diffutoon/diffutoon_toon_shading_with_editing_signals.py @@ -0,0 +1,204 @@ +from diffsynth import SDVideoPipelineRunner, download_models +import os + + +# Download models (automatically) +# `models/stable_diffusion/aingdiffusion_v12.safetensors`: [link](https://civitai.com/api/download/models/229575) +# `models/AnimateDiff/mm_sd_v15_v2.ckpt`: [link](https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15_v2.ckpt) +# `models/ControlNet/control_v11p_sd15_lineart.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart.pth) +# `models/ControlNet/control_v11f1e_sd15_tile.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile.pth) +# `models/ControlNet/control_v11f1p_sd15_depth.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth.pth) +# `models/ControlNet/control_v11p_sd15_softedge.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge.pth) +# `models/Annotators/dpt_hybrid-midas-501f0c75.pt`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/dpt_hybrid-midas-501f0c75.pt) +# `models/Annotators/ControlNetHED.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetHED.pth) +# `models/Annotators/sk_model.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model.pth) +# `models/Annotators/sk_model2.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model2.pth) +# `models/textual_inversion/verybadimagenegative_v1.3.pt`: [link](https://civitai.com/api/download/models/25820?type=Model&format=PickleTensor&size=full&fp=fp16) +download_models([ + "AingDiffusion_v12", + "AnimateDiff_v2", + "ControlNet_v11p_sd15_lineart", + "ControlNet_v11f1e_sd15_tile", + "ControlNet_v11f1p_sd15_depth", + "ControlNet_v11p_sd15_softedge", + "TextualInversion_VeryBadImageNegative_v1.3" +]) +# The original video in the example is https://www.bilibili.com/video/BV1zu4y1s7Ec/. + +config_stage_1 = { + "models": { + "model_list": [ + "models/stable_diffusion/aingdiffusion_v12.safetensors", + "models/ControlNet/control_v11p_sd15_softedge.pth", + "models/ControlNet/control_v11f1p_sd15_depth.pth" + ], + "textual_inversion_folder": "models/textual_inversion", + "device": "cuda", + "lora_alphas": [], + "controlnet_units": [ + { + "processor_id": "softedge", + "model_path": "models/ControlNet/control_v11p_sd15_softedge.pth", + "scale": 0.5 + }, + { + "processor_id": "depth", + "model_path": "models/ControlNet/control_v11f1p_sd15_depth.pth", + "scale": 0.5 + } + ] + }, + "data": { + "input_frames": { + "video_file": "data/examples/diffutoon_edit/input_video.mp4", + "image_folder": None, + "height": 512, + "width": 512, + "start_frame_id": 0, + "end_frame_id": 30 + }, + "controlnet_frames": [ + { + "video_file": "data/examples/diffutoon_edit/input_video.mp4", + "image_folder": None, + "height": 512, + "width": 512, + "start_frame_id": 0, + "end_frame_id": 30 + }, + { + "video_file": "data/examples/diffutoon_edit/input_video.mp4", + "image_folder": None, + "height": 512, + "width": 512, + "start_frame_id": 0, + "end_frame_id": 30 + } + ], + "output_folder": "output/color_video", + "fps": 25 + }, + "smoother_configs": [ + { + "processor_type": "FastBlend", + "config": {} + } + ], + "pipeline": { + "seed": 0, + "pipeline_inputs": { + "prompt": "best quality, perfect anime illustration, orange clothes, night, a girl is dancing, smile, solo, black silk stockings", + "negative_prompt": "verybadimagenegative_v1.3", + "cfg_scale": 7.0, + "clip_skip": 1, + "denoising_strength": 0.9, + "num_inference_steps": 20, + "animatediff_batch_size": 8, + "animatediff_stride": 4, + "unet_batch_size": 8, + "controlnet_batch_size": 8, + "cross_frame_attention": True, + "smoother_progress_ids": [-1], + # The following parameters will be overwritten. You don't need to modify them. + "input_frames": [], + "num_frames": 30, + "width": 512, + "height": 512, + "controlnet_frames": [] + } + } +} + + +config_stage_2 = { + "models": { + "model_list": [ + "models/stable_diffusion/aingdiffusion_v12.safetensors", + "models/AnimateDiff/mm_sd_v15_v2.ckpt", + "models/ControlNet/control_v11f1e_sd15_tile.pth", + "models/ControlNet/control_v11p_sd15_lineart.pth" + ], + "textual_inversion_folder": "models/textual_inversion", + "device": "cuda", + "lora_alphas": [], + "controlnet_units": [ + { + "processor_id": "tile", + "model_path": "models/ControlNet/control_v11f1e_sd15_tile.pth", + "scale": 0.5 + }, + { + "processor_id": "lineart", + "model_path": "models/ControlNet/control_v11p_sd15_lineart.pth", + "scale": 0.5 + } + ] + }, + "data": { + "input_frames": { + "video_file": "data/examples/diffutoon_edit/input_video.mp4", + "image_folder": None, + "height": 1536, + "width": 1536, + "start_frame_id": 0, + "end_frame_id": 30 + }, + "controlnet_frames": [ + { + "video_file": "data/examples/diffutoon_edit/input_video.mp4", + "image_folder": None, + "height": 1536, + "width": 1536, + "start_frame_id": 0, + "end_frame_id": 30 + }, + { + "video_file": "data/examples/diffutoon_edit/input_video.mp4", + "image_folder": None, + "height": 1536, + "width": 1536, + "start_frame_id": 0, + "end_frame_id": 30 + } + ], + "output_folder": "output/edited_video", + "fps": 30 + }, + "pipeline": { + "seed": 0, + "pipeline_inputs": { + "prompt": "best quality, perfect anime illustration, light, a girl is dancing, smile, solo", + "negative_prompt": "verybadimagenegative_v1.3", + "cfg_scale": 7.0, + "clip_skip": 2, + "denoising_strength": 1.0, + "num_inference_steps": 10, + "animatediff_batch_size": 16, + "animatediff_stride": 8, + "unet_batch_size": 1, + "controlnet_batch_size": 1, + "cross_frame_attention": False, + # The following parameters will be overwritten. You don't need to modify them. + "input_frames": [], + "num_frames": 30, + "width": 1536, + "height": 1536, + "controlnet_frames": [] + } + } +} + + +runner = SDVideoPipelineRunner() +runner.run(config_stage_1) + +# Replace the color video with the synthesized video +config_stage_2["data"]["controlnet_frames"][0] = { + "video_file": os.path.join(config_stage_1["data"]["output_folder"], "video.mp4"), + "image_folder": None, + "height": config_stage_2["data"]["input_frames"]["height"], + "width": config_stage_2["data"]["input_frames"]["width"], + "start_frame_id": None, + "end_frame_id": None +} +runner.run(config_stage_2) diff --git a/examples/Diffutoon/sd_toon_shading.py b/examples/Diffutoon/sd_toon_shading.py new file mode 100644 index 0000000000000000000000000000000000000000..1e2169f0ec533cb6c66e37377390d0618a26bf46 --- /dev/null +++ b/examples/Diffutoon/sd_toon_shading.py @@ -0,0 +1,66 @@ +from diffsynth import ModelManager, SDVideoPipeline, ControlNetConfigUnit, VideoData, save_video, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion/flat2DAnimerge_v45Sharp.safetensors`: [link](https://civitai.com/api/download/models/266360?type=Model&format=SafeTensor&size=pruned&fp=fp16) +# `models/AnimateDiff/mm_sd_v15_v2.ckpt`: [link](https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15_v2.ckpt) +# `models/ControlNet/control_v11p_sd15_lineart.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart.pth) +# `models/ControlNet/control_v11f1e_sd15_tile.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile.pth) +# `models/Annotators/sk_model.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model.pth) +# `models/Annotators/sk_model2.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model2.pth) +# `models/textual_inversion/verybadimagenegative_v1.3.pt`: [link](https://civitai.com/api/download/models/25820?type=Model&format=PickleTensor&size=full&fp=fp16) +download_models([ + "Flat2DAnimerge_v45Sharp", + "AnimateDiff_v2", + "ControlNet_v11p_sd15_lineart", + "ControlNet_v11f1e_sd15_tile", + "TextualInversion_VeryBadImageNegative_v1.3" +]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_textual_inversions("models/textual_inversion") +model_manager.load_models([ + "models/stable_diffusion/flat2DAnimerge_v45Sharp.safetensors", + "models/AnimateDiff/mm_sd_v15_v2.ckpt", + "models/ControlNet/control_v11p_sd15_lineart.pth", + "models/ControlNet/control_v11f1e_sd15_tile.pth", +]) +pipe = SDVideoPipeline.from_model_manager( + model_manager, + [ + ControlNetConfigUnit( + processor_id="lineart", + model_path="models/ControlNet/control_v11p_sd15_lineart.pth", + scale=0.5 + ), + ControlNetConfigUnit( + processor_id="tile", + model_path="models/ControlNet/control_v11f1e_sd15_tile.pth", + scale=0.5 + ) + ] +) + +# Load video (we only use 60 frames for quick testing) +# The original video is here: https://www.bilibili.com/video/BV19w411A7YJ/ +video = VideoData( + video_file="data/examples/bilibili/BV19w411A7YJ.mp4", + height=1024, width=1024) +input_video = [video[i] for i in range(40*60, 41*60)] + +# Toon shading (20G VRAM) +torch.manual_seed(0) +output_video = pipe( + prompt="best quality, perfect anime illustration, light, a girl is dancing, smile, solo", + negative_prompt="verybadimagenegative_v1.3", + cfg_scale=3, clip_skip=2, + controlnet_frames=input_video, num_frames=len(input_video), + num_inference_steps=10, height=1024, width=1024, + animatediff_batch_size=32, animatediff_stride=16, + vram_limit_level=0, +) + +# Save video +save_video(output_video, "output_video.mp4", fps=60) diff --git a/examples/ExVideo/ExVideo_ema.py b/examples/ExVideo/ExVideo_ema.py new file mode 100644 index 0000000000000000000000000000000000000000..81d4c399ca6e252c0b4e799d61900b854b66ad6e --- /dev/null +++ b/examples/ExVideo/ExVideo_ema.py @@ -0,0 +1,64 @@ +import torch, os, argparse +from safetensors.torch import save_file + + +def load_pl_state_dict(file_path): + print(f"loading {file_path}") + state_dict = torch.load(file_path, map_location="cpu") + trainable_param_names = set(state_dict["trainable_param_names"]) + if "module" in state_dict: + state_dict = state_dict["module"] + if "state_dict" in state_dict: + state_dict = state_dict["state_dict"] + state_dict_ = {} + for name, param in state_dict.items(): + if name.startswith("_forward_module."): + name = name[len("_forward_module."):] + if name.startswith("unet."): + name = name[len("unet."):] + if name in trainable_param_names: + state_dict_[name] = param + return state_dict_ + + +def ckpt_to_epochs(ckpt_name): + return int(ckpt_name.split("=")[1].split("-")[0]) + + +def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--output_path", + type=str, + default="./", + help="Path to save the model.", + ) + parser.add_argument( + "--gamma", + type=float, + default=0.9, + help="Gamma in EMA.", + ) + args = parser.parse_args() + return args + + +if __name__ == '__main__': + # args + args = parse_args() + folder = args.output_path + gamma = args.gamma + + # EMA + ckpt_list = sorted([(ckpt_to_epochs(ckpt_name), ckpt_name) for ckpt_name in os.listdir(folder) if os.path.isdir(f"{folder}/{ckpt_name}")]) + state_dict_ema = None + for epochs, ckpt_name in ckpt_list: + state_dict = load_pl_state_dict(f"{folder}/{ckpt_name}/checkpoint/mp_rank_00_model_states.pt") + if state_dict_ema is None: + state_dict_ema = {name: param.float() for name, param in state_dict.items()} + else: + for name, param in state_dict.items(): + state_dict_ema[name] = state_dict_ema[name] * gamma + param.float() * (1 - gamma) + save_path = ckpt_name.replace(".ckpt", "-ema.safetensors") + print(f"save to {folder}/{save_path}") + save_file(state_dict_ema, f"{folder}/{save_path}") diff --git a/examples/ExVideo/ExVideo_svd_test.py b/examples/ExVideo/ExVideo_svd_test.py new file mode 100644 index 0000000000000000000000000000000000000000..29a1910a44ad1dbb493e38653a6a54785cd1c576 --- /dev/null +++ b/examples/ExVideo/ExVideo_svd_test.py @@ -0,0 +1,114 @@ +from diffsynth import save_video, ModelManager, SVDVideoPipeline, HunyuanDiTImagePipeline, download_models +from diffsynth import ModelManager +import torch, os + +# The models will be downloaded automatically. +# You can also use the following urls to download them manually. + +# Download models (from Huggingface) +# Text-to-image model: +# `models/HunyuanDiT/t2i/clip_text_encoder/pytorch_model.bin`: [link](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/resolve/main/t2i/clip_text_encoder/pytorch_model.bin) +# `models/HunyuanDiT/t2i/mt5/pytorch_model.bin`: [link](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/resolve/main/t2i/mt5/pytorch_model.bin) +# `models/HunyuanDiT/t2i/model/pytorch_model_ema.pt`: [link](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/resolve/main/t2i/model/pytorch_model_ema.pt) +# `models/HunyuanDiT/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin`: [link](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/resolve/main/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin) +# Stable Video Diffusion model: +# `models/stable_video_diffusion/svd_xt.safetensors`: [link](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/svd_xt.safetensors) +# ExVideo extension blocks: +# `models/stable_video_diffusion/model.fp16.safetensors`: [link](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1/resolve/main/model.fp16.safetensors) + +# Download models (from Modelscope) +# Text-to-image model: +# `models/HunyuanDiT/t2i/clip_text_encoder/pytorch_model.bin`: [link](https://www.modelscope.cn/api/v1/models/modelscope/HunyuanDiT/repo?Revision=master&FilePath=t2i%2Fclip_text_encoder%2Fpytorch_model.bin) +# `models/HunyuanDiT/t2i/mt5/pytorch_model.bin`: [link](https://www.modelscope.cn/api/v1/models/modelscope/HunyuanDiT/repo?Revision=master&FilePath=t2i%2Fmt5%2Fpytorch_model.bin) +# `models/HunyuanDiT/t2i/model/pytorch_model_ema.pt`: [link](https://www.modelscope.cn/api/v1/models/modelscope/HunyuanDiT/repo?Revision=master&FilePath=t2i%2Fmodel%2Fpytorch_model_ema.pt) +# `models/HunyuanDiT/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin`: [link](https://www.modelscope.cn/api/v1/models/modelscope/HunyuanDiT/repo?Revision=master&FilePath=t2i%2Fsdxl-vae-fp16-fix%2Fdiffusion_pytorch_model.bin) +# Stable Video Diffusion model: +# `models/stable_video_diffusion/svd_xt.safetensors`: [link](https://www.modelscope.cn/api/v1/models/AI-ModelScope/stable-video-diffusion-img2vid-xt/repo?Revision=master&FilePath=svd_xt.safetensors) +# ExVideo extension blocks: +# `models/stable_video_diffusion/model.fp16.safetensors`: [link](https://modelscope.cn/api/v1/models/ECNU-CILab/ExVideo-SVD-128f-v1/repo?Revision=master&FilePath=model.fp16.safetensors) + + +def generate_image(): + # Load models + os.environ["TOKENIZERS_PARALLELISM"] = "True" + download_models(["HunyuanDiT"]) + model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/HunyuanDiT/t2i/clip_text_encoder/pytorch_model.bin", + "models/HunyuanDiT/t2i/mt5/pytorch_model.bin", + "models/HunyuanDiT/t2i/model/pytorch_model_ema.pt", + "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", + ]) + pipe = HunyuanDiTImagePipeline.from_model_manager(model_manager) + + # Generate an image + torch.manual_seed(0) + image = pipe( + prompt="bonfire, on the stone", + negative_prompt="错误的眼睛，糟糕的人脸，毁容，糟糕的艺术，变形，多余的肢体，模糊的颜色，模糊，重复，病态，残缺，", + num_inference_steps=50, height=1024, width=1024, + ) + model_manager.to("cpu") + return image + + +def generate_video(image): + # Load models + download_models(["stable-video-diffusion-img2vid-xt", "ExVideo-SVD-128f-v1"]) + model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/stable_video_diffusion/svd_xt.safetensors", + "models/stable_video_diffusion/model.fp16.safetensors", + ]) + pipe = SVDVideoPipeline.from_model_manager(model_manager) + + # Generate a video + torch.manual_seed(1) + video = pipe( + input_image=image.resize((512, 512)), + num_frames=128, fps=30, height=512, width=512, + motion_bucket_id=127, + num_inference_steps=50, + min_cfg_scale=2, max_cfg_scale=2, contrast_enhance_scale=1.2 + ) + model_manager.to("cpu") + return video + + +def upscale_video(image, video): + # Load models + download_models(["stable-video-diffusion-img2vid-xt", "ExVideo-SVD-128f-v1"]) + model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/stable_video_diffusion/svd_xt.safetensors", + "models/stable_video_diffusion/model.fp16.safetensors", + ]) + pipe = SVDVideoPipeline.from_model_manager(model_manager) + + # Generate a video + torch.manual_seed(2) + video = pipe( + input_image=image.resize((1024, 1024)), + input_video=[frame.resize((1024, 1024)) for frame in video], denoising_strength=0.5, + num_frames=128, fps=30, height=1024, width=1024, + motion_bucket_id=127, + num_inference_steps=25, + min_cfg_scale=2, max_cfg_scale=2, contrast_enhance_scale=1.2 + ) + model_manager.to("cpu") + return video + + +# We use Hunyuan DiT to generate the first frame. 10GB VRAM is required. +# If you want to use your own image, +# please use `image = Image.open("your_image_file.png")` to replace the following code. +image = generate_image() +image.save("image.png") + +# Now, generate a video with resolution of 512. 20GB VRAM is required. +video = generate_video(image) +save_video(video, "video_512.mp4", fps=30) + +# Upscale the video. 52GB VRAM is required. +video = upscale_video(image, video) +save_video(video, "video_1024.mp4", fps=30) diff --git a/examples/ExVideo/ExVideo_svd_train.py b/examples/ExVideo/ExVideo_svd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..b3cb72c03cde49189ab4c6bfd1a9ac614e9139c4 --- /dev/null +++ b/examples/ExVideo/ExVideo_svd_train.py @@ -0,0 +1,362 @@ +import torch, json, os, imageio, argparse +from torchvision.transforms import v2 +import numpy as np +from einops import rearrange, repeat +import lightning as pl +from diffsynth import ModelManager, SVDImageEncoder, SVDUNet, SVDVAEEncoder, ContinuousODEScheduler, load_state_dict +from diffsynth.pipelines.stable_video_diffusion import SVDCLIPImageProcessor +from diffsynth.models.svd_unet import TemporalAttentionBlock + + + +class TextVideoDataset(torch.utils.data.Dataset): + def __init__(self, base_path, metadata_path, steps_per_epoch=10000, training_shapes=[(128, 1, 128, 512, 512)]): + with open(metadata_path, "r") as f: + metadata = json.load(f) + self.path = [os.path.join(base_path, i["path"]) for i in metadata] + self.steps_per_epoch = steps_per_epoch + self.training_shapes = training_shapes + + self.frame_process = [] + for max_num_frames, interval, num_frames, height, width in training_shapes: + self.frame_process.append(v2.Compose([ + v2.Resize(size=max(height, width), antialias=True), + v2.CenterCrop(size=(height, width)), + v2.Normalize(mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5]), + ])) + + + def load_frames_using_imageio(self, file_path, max_num_frames, start_frame_id, interval, num_frames, frame_process): + reader = imageio.get_reader(file_path) + if reader.count_frames() < max_num_frames or reader.count_frames() - 1 < start_frame_id + (num_frames - 1) * interval: + reader.close() + return None + + frames = [] + for frame_id in range(num_frames): + frame = reader.get_data(start_frame_id + frame_id * interval) + frame = torch.tensor(frame, dtype=torch.float32) + frame = rearrange(frame, "H W C -> 1 C H W") + frame = frame_process(frame) + frames.append(frame) + reader.close() + + frames = torch.concat(frames, dim=0) + frames = rearrange(frames, "T C H W -> C T H W") + + return frames + + + def load_video(self, file_path, training_shape_id): + data = {} + max_num_frames, interval, num_frames, height, width = self.training_shapes[training_shape_id] + frame_process = self.frame_process[training_shape_id] + start_frame_id = torch.randint(0, max_num_frames - (num_frames - 1) * interval, (1,))[0] + frames = self.load_frames_using_imageio(file_path, max_num_frames, start_frame_id, interval, num_frames, frame_process) + if frames is None: + return None + else: + data[f"frames_{training_shape_id}"] = frames + return data + + + def __getitem__(self, index): + video_data = {} + for training_shape_id in range(len(self.training_shapes)): + while True: + data_id = torch.randint(0, len(self.path), (1,))[0] + data_id = (data_id + index) % len(self.path) # For fixed seed. + video_file = self.path[data_id] + try: + data = self.load_video(video_file, training_shape_id) + except: + data = None + if data is not None: + break + video_data.update(data) + return video_data + + + def __len__(self): + return self.steps_per_epoch + + + +class MotionBucketManager: + def __init__(self): + self.thresholds = [ + 0.000000000, 0.012205946, 0.015117834, 0.018080613, 0.020614484, 0.021959992, 0.024088068, 0.026323952, + 0.028277775, 0.029968588, 0.031836554, 0.033596724, 0.035121530, 0.037200287, 0.038914755, 0.040696491, + 0.042368013, 0.044265781, 0.046311017, 0.048243891, 0.050294187, 0.052142400, 0.053634230, 0.055612389, + 0.057594258, 0.059410289, 0.061283995, 0.063603796, 0.065192916, 0.067146860, 0.069066539, 0.070390493, + 0.072588451, 0.073959745, 0.075889029, 0.077695683, 0.079783581, 0.082162730, 0.084092639, 0.085958421, + 0.087700523, 0.089684933, 0.091688842, 0.093335517, 0.094987206, 0.096664011, 0.098314710, 0.100262381, + 0.101984538, 0.103404313, 0.105280340, 0.106974818, 0.109028399, 0.111164779, 0.113065213, 0.114362158, + 0.116407216, 0.118063427, 0.119524263, 0.121835820, 0.124242283, 0.126202747, 0.128989249, 0.131672353, + 0.133417681, 0.135567948, 0.137313649, 0.139189199, 0.140912935, 0.143525436, 0.145718485, 0.148315132, + 0.151039496, 0.153218940, 0.155252382, 0.157651082, 0.159966752, 0.162195817, 0.164811596, 0.167341709, + 0.170251891, 0.172651157, 0.175550997, 0.178372145, 0.181039348, 0.183565900, 0.186599866, 0.190071866, + 0.192574754, 0.195026234, 0.198099136, 0.200210452, 0.202522039, 0.205410406, 0.208610669, 0.211623028, + 0.214723110, 0.218520239, 0.222194016, 0.225363150, 0.229384825, 0.233422622, 0.237012610, 0.240735114, + 0.243622541, 0.247465774, 0.252190471, 0.257356376, 0.261856794, 0.266556412, 0.271076709, 0.277361482, + 0.281250387, 0.286582440, 0.291158527, 0.296712339, 0.303008437, 0.311793238, 0.318485111, 0.326999635, + 0.332138240, 0.341770738, 0.354188830, 0.365194678, 0.379234344, 0.401538879, 0.416078776, 0.440871328, + ] + + def get_motion_score(self, frames): + score = frames.std(dim=2).mean(dim=[1, 2, 3]).tolist() + return score + + def get_bucket_id(self, motion_score): + for bucket_id in range(len(self.thresholds) - 1): + if self.thresholds[bucket_id + 1] > motion_score: + return bucket_id + return len(self.thresholds) - 1 + + def __call__(self, frames): + scores = self.get_motion_score(frames) + bucket_ids = [self.get_bucket_id(score) for score in scores] + return bucket_ids + + + +class LightningModel(pl.LightningModule): + def __init__(self, learning_rate=1e-5, svd_ckpt_path=None, add_positional_conv=128, contrast_enhance_scale=1.01): + super().__init__() + model_manager = ModelManager(torch_dtype=torch.float16, device=self.device) + model_manager.load_stable_video_diffusion(state_dict=load_state_dict(svd_ckpt_path), add_positional_conv=add_positional_conv) + + self.image_encoder: SVDImageEncoder = model_manager.image_encoder + self.image_encoder.eval() + self.image_encoder.requires_grad_(False) + + self.unet: SVDUNet = model_manager.unet + self.unet.train() + self.unet.requires_grad_(False) + for block in self.unet.blocks: + if isinstance(block, TemporalAttentionBlock): + block.requires_grad_(True) + + self.vae_encoder: SVDVAEEncoder = model_manager.vae_encoder + self.vae_encoder.eval() + self.vae_encoder.requires_grad_(False) + + self.noise_scheduler = ContinuousODEScheduler(num_inference_steps=1000) + self.learning_rate = learning_rate + + self.motion_bucket_manager = MotionBucketManager() + self.contrast_enhance_scale = contrast_enhance_scale + + + def encode_image_with_clip(self, image): + image = SVDCLIPImageProcessor().resize_with_antialiasing(image, (224, 224)) + image = (image + 1.0) / 2.0 + mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).reshape(1, 3, 1, 1).to(device=self.device, dtype=self.dtype) + std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).reshape(1, 3, 1, 1).to(device=self.device, dtype=self.dtype) + image = (image - mean) / std + image_emb = self.image_encoder(image) + return image_emb + + + def encode_video_with_vae(self, video): + video = video.to(device=self.device, dtype=self.dtype) + video = video.unsqueeze(0) + latents = self.vae_encoder.encode_video(video) + latents = rearrange(latents[0], "C T H W -> T C H W") + return latents + + + def tensor2video(self, frames): + frames = rearrange(frames, "C T H W -> T H W C") + frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) + return frames + + + def calculate_loss(self, frames): + with torch.no_grad(): + # Call video encoder + latents = self.encode_video_with_vae(frames) + image_emb_vae = repeat(latents[0] / self.vae_encoder.scaling_factor, "C H W -> T C H W", T=frames.shape[1]) + image_emb_clip = self.encode_image_with_clip(frames[:,0].unsqueeze(0)) + + # Call scheduler + timestep = torch.randint(0, len(self.noise_scheduler.timesteps), (1,))[0] + timestep = self.noise_scheduler.timesteps[timestep] + noise = torch.randn_like(latents) + noisy_latents = self.noise_scheduler.add_noise(latents, noise, timestep) + + # Prepare positional id + fps = 30 + motion_bucket_id = self.motion_bucket_manager(frames.unsqueeze(0))[0] + noise_aug_strength = 0 + add_time_id = torch.tensor([[fps-1, motion_bucket_id, noise_aug_strength]], device=self.device) + + # Calculate loss + latents_input = torch.cat([noisy_latents, image_emb_vae], dim=1) + model_pred = self.unet(latents_input, timestep, image_emb_clip, add_time_id, use_gradient_checkpointing=True) + latents_output = self.noise_scheduler.step(model_pred.float(), timestep, noisy_latents.float(), to_final=True) + loss = torch.nn.functional.mse_loss(latents_output, latents.float() * self.contrast_enhance_scale, reduction="mean") + + # Re-weighting + reweighted_loss = loss * self.noise_scheduler.training_weight(timestep) + return loss, reweighted_loss + + + def training_step(self, batch, batch_idx): + # Loss + frames = batch["frames_0"][0] + loss, reweighted_loss = self.calculate_loss(frames) + + # Record log + self.log("train_loss", loss, prog_bar=True) + self.log("reweighted_train_loss", reweighted_loss, prog_bar=True) + return reweighted_loss + + + def configure_optimizers(self): + trainable_modules = [] + for block in self.unet.blocks: + if isinstance(block, TemporalAttentionBlock): + trainable_modules += block.parameters() + optimizer = torch.optim.AdamW(trainable_modules, lr=self.learning_rate) + return optimizer + + + def on_save_checkpoint(self, checkpoint): + trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.unet.named_parameters())) + trainable_param_names = [named_param[0] for named_param in trainable_param_names] + checkpoint["trainable_param_names"] = trainable_param_names + + + +def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--pretrained_path", + type=str, + default=None, + required=True, + help="Path to pretrained model. For example, `models/stable_video_diffusion/svd_xt.safetensors`.", + ) + parser.add_argument( + "--resume_from_checkpoint", + type=str, + default=None, + required=False, + help="Path to checkpoint, in case your training program is stopped unexpectedly and you want to resume.", + ) + parser.add_argument( + "--dataset_path", + type=str, + default=None, + required=True, + help="The path of the Dataset.", + ) + parser.add_argument( + "--output_path", + type=str, + default="./", + help="Path to save the model.", + ) + parser.add_argument( + "--steps_per_epoch", + type=int, + default=500, + help="Number of steps per epoch.", + ) + parser.add_argument( + "--num_frames", + type=int, + default=128, + help="Number of frames.", + ) + parser.add_argument( + "--height", + type=int, + default=512, + help="Image height.", + ) + parser.add_argument( + "--width", + type=int, + default=512, + help="Image width.", + ) + parser.add_argument( + "--dataloader_num_workers", + type=int, + default=2, + help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=1e-5, + help="Learning rate.", + ) + parser.add_argument( + "--accumulate_grad_batches", + type=int, + default=1, + help="The number of batches in gradient accumulation.", + ) + parser.add_argument( + "--max_epochs", + type=int, + default=1, + help="Number of epochs.", + ) + parser.add_argument( + "--contrast_enhance_scale", + type=float, + default=1.01, + help="Avoid generating gray videos.", + ) + args = parser.parse_args() + return args + + +if __name__ == '__main__': + # args + args = parse_args() + + # dataset and data loader + dataset = TextVideoDataset( + args.dataset_path, + os.path.join(args.dataset_path, "metadata.json"), + training_shapes=[(args.num_frames, 1, args.num_frames, args.height, args.width)], + steps_per_epoch=args.steps_per_epoch, + ) + train_loader = torch.utils.data.DataLoader( + dataset, + shuffle=True, + # We don't support batch_size > 1, + # because sometimes our GPU cannot process even one video. + batch_size=1, + num_workers=args.dataloader_num_workers + ) + + # model + model = LightningModel( + learning_rate=args.learning_rate, + svd_ckpt_path=args.pretrained_path, + add_positional_conv=args.num_frames, + contrast_enhance_scale=args.contrast_enhance_scale + ) + + # train + trainer = pl.Trainer( + max_epochs=args.max_epochs, + accelerator="gpu", + devices="auto", + strategy="deepspeed_stage_2", + precision="16-mixed", + default_root_dir=args.output_path, + accumulate_grad_batches=args.accumulate_grad_batches, + callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)] + ) + trainer.fit( + model=model, + train_dataloaders=train_loader, + ckpt_path=args.resume_from_checkpoint + ) diff --git a/examples/ExVideo/README.md b/examples/ExVideo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..cb57d4831115f90cda67b43e6d814ea698fd5880 --- /dev/null +++ b/examples/ExVideo/README.md @@ -0,0 +1,81 @@ +# ExVideo + +ExVideo is a post-tuning technique aimed at enhancing the capability of video generation models. We have extended Stable Video Diffusion to achieve the generation of long videos up to 128 frames. + +* [Project Page](https://ecnu-cilab.github.io/ExVideoProjectPage/) +* [Technical report](https://arxiv.org/abs/2406.14130) +* [Demo](https://huggingface.co/spaces/modelscope/ExVideo-SVD-128f-v1) +* Extended models + * [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1) + * [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1) + +## Example: Text-to-video via extended Stable Video Diffusion + +Generate a video using a text-to-image model and our image-to-video model. See [ExVideo_svd_test.py](./ExVideo_svd_test.py). + +https://github.com/modelscope/DiffSynth-Studio/assets/35051019/d97f6aa9-8064-4b5b-9d49-ed6001bb9acc + +## Train + +* Step 1: Install additional packages + +``` +pip install lightning deepspeed +``` + +* Step 2: Download base model (from [HuggingFace](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/svd_xt.safetensors) or [ModelScope](https://www.modelscope.cn/api/v1/models/AI-ModelScope/stable-video-diffusion-img2vid-xt/repo?Revision=master&FilePath=svd_xt.safetensors)) to `models/stable_video_diffusion/svd_xt.safetensors`. + +* Step 3: Prepare datasets + +``` +path/to/your/dataset +├── metadata.json +└── videos + ├── video_1.mp4 + ├── video_2.mp4 + └── video_3.mp4 +``` + +where the `metadata.json` is + +``` +[ + { + "path": "videos/video_1.mp4" + }, + { + "path": "videos/video_2.mp4" + }, + { + "path": "videos/video_3.mp4" + } +] +``` + +* Step 4: Run + +``` +CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python -u ExVideo_svd_train.py \ + --pretrained_path "models/stable_video_diffusion/svd_xt.safetensors" \ + --dataset_path "path/to/your/dataset" \ + --output_path "path/to/save/models" \ + --steps_per_epoch 8000 \ + --num_frames 128 \ + --height 512 \ + --width 512 \ + --dataloader_num_workers 2 \ + --learning_rate 1e-5 \ + --max_epochs 100 +``` + +* Step 5: Post-process checkpoints + +Calculate Exponential Moving Average (EMA) and package it using `safetensors`. + +``` +python ExVideo_ema.py --output_path "path/to/save/models/lightning_logs/version_xx" --gamma 0.9 +``` + +* Step 6: Enjoy your model + +The EMA model is at `path/to/save/models/lightning_logs/version_xx/checkpoints/epoch=xx-step=yyy-ema.safetensors`. Load it in [ExVideo_svd_test.py](./ExVideo_svd_test.py) and then enjoy your model. diff --git a/examples/Ip-Adapter/README.md b/examples/Ip-Adapter/README.md new file mode 100644 index 0000000000000000000000000000000000000000..86f9dabe92ad54e2aeb5bcb3b334593d09785d60 --- /dev/null +++ b/examples/Ip-Adapter/README.md @@ -0,0 +1,44 @@ +# IP-Adapter + +IP-Adapter is a interesting model, which can adopt the content or style of another image to generate a new image. + +## Example: Content Controlling in Stable Diffusion + +Based on Stable Diffusion, we can transfer the object to another scene. See [`sd_ipadapter.py`](./sd_ipadapter.py). + +|First, we generate a car. The prompt is "masterpiece, best quality, a car".|Next, utilizing IP-Adapter, we move the car to the road. The prompt is "masterpiece, best quality, a car running on the road".| +|-|-| +|![car](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/8530a2f0-f610-4269-a22c-ac6c2f21fc18)|![car_on_the_road](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/b8ccddb2-c423-46d8-bd1a-327fcc074a36)| + +## Example: Content and Style Controlling in Stable Diffusion XL + +The IP-Adapter model based on Stable Diffusion XL is more powerful. You have the option to use the content or style. See [`sdxl_ipadapter.py`](./sdxl_ipadapter.py). + +* Content controlling (original usage of IP-Adapter) + +|First, we generate a rabbit.|Next, enable IP-Adapter and let the rabbit jump.|For comparision, disable IP-Adapter to see the generated image.| +|-|-|-| +|![rabbit](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/4b452634-ec57-414f-897a-f8c50c74a650)|![rabbit_to_jumping_rabbit](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/b93c5495-0b77-4d97-bcd3-3942858288f2)|![rabbit_to_jumping_rabbit_without_ipa](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/52f37195-65b3-4a38-8d9b-73df37311c15)| + + +* Style controlling (InstantStyle) + +|First, we generate a rabbit.|Next, enable InstantStyle and convert the rabbit to a cat.|For comparision, disable IP-Adapter to see the generated image.| +|-|-|-| +|![rabbit](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/4b452634-ec57-414f-897a-f8c50c74a650)|![rabbit_to_cat](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/a006b281-f643-4ea9-b0da-712289c96059)|![rabbit_to_cat_without_ipa](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/189bd11e-7a10-4c09-8554-0eebde9150fd)| + +## Example: Image Fusing (Experimental) + +Since IP-Adapter can control the content based on more than one image, we can do something interesting. See [`sdxl_ipadapter_multi_reference.py`](sdxl_ipadapter_multi_reference.py). + +We have two pokemons here: + +|Charizard|Pikachu| +|-|-| +|![](https://media.52poke.com/wiki/7/7e/006Charizard.png)|![](https://media.52poke.com/wiki/0/0d/025Pikachu.png)| + +Fuse! + +|Pikazard ???| +|-| +|![Pikazard](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/807cdb31-94f5-4cc2-a978-3c6a7ffedc5b)| diff --git a/examples/Ip-Adapter/sd_ipadapter.py b/examples/Ip-Adapter/sd_ipadapter.py new file mode 100644 index 0000000000000000000000000000000000000000..bd86de6902ce84a4a437863e4ea93923039c80a4 --- /dev/null +++ b/examples/Ip-Adapter/sd_ipadapter.py @@ -0,0 +1,38 @@ +from diffsynth import ModelManager, SDImagePipeline, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion/aingdiffusion_v12.safetensors`: [link](https://civitai.com/api/download/models/229575?type=Model&format=SafeTensor&size=full&fp=fp16) +# `models/IpAdapter/stable_diffusion/image_encoder/model.safetensors`: [link](https://huggingface.co/h94/IP-Adapter/resolve/main/models/image_encoder/model.safetensors) +# `models/IpAdapter/stable_diffusion/ip-adapter_sd15.bin`: [link](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15.bin) +# `models/textual_inversion/verybadimagenegative_v1.3.pt`: [link](https://civitai.com/api/download/models/25820?type=Model&format=PickleTensor&size=full&fp=fp16) +download_models(["AingDiffusion_v12", "IP-Adapter-SD", "TextualInversion_VeryBadImageNegative_v1.3"]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_textual_inversions("models/textual_inversion") +model_manager.load_models([ + "models/stable_diffusion/aingdiffusion_v12.safetensors", + "models/IpAdapter/stable_diffusion/image_encoder/model.safetensors", + "models/IpAdapter/stable_diffusion/ip-adapter_sd15.bin" +]) +pipe = SDImagePipeline.from_model_manager(model_manager) + +torch.manual_seed(1) +style_image = pipe( + prompt="masterpiece, best quality, a car", + negative_prompt="verybadimagenegative_v1.3", + cfg_scale=7, clip_skip=2, + height=512, width=512, num_inference_steps=50, +) +style_image.save("car.jpg") + +image = pipe( + prompt="masterpiece, best quality, a car running on the road", + negative_prompt="verybadimagenegative_v1.3", + cfg_scale=7, clip_skip=2, + height=512, width=512, num_inference_steps=50, + ipadapter_images=[style_image], ipadapter_scale=1.0 +) +image.save("car_on_the_road.jpg") diff --git a/examples/Ip-Adapter/sdxl_ipadapter.py b/examples/Ip-Adapter/sdxl_ipadapter.py new file mode 100644 index 0000000000000000000000000000000000000000..29f2da8134f7358c595f1ea7df555ecc463dd6b0 --- /dev/null +++ b/examples/Ip-Adapter/sdxl_ipadapter.py @@ -0,0 +1,61 @@ +from diffsynth import ModelManager, SDXLImagePipeline, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion_xl/sd_xl_base_1.0.safetensors`: [link](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors) +# `models/IpAdapter/stable_diffusion_xl/image_encoder/model.safetensors`: [link](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/model.safetensors) +# `models/IpAdapter/stable_diffusion_xl/ip-adapter_sdxl.bin`: [link](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl.safetensors) +download_models(["StableDiffusionXL_v1", "IP-Adapter-SDXL"]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models([ + "models/stable_diffusion_xl/sd_xl_base_1.0.safetensors", + "models/IpAdapter/stable_diffusion_xl/image_encoder/model.safetensors", + "models/IpAdapter/stable_diffusion_xl/ip-adapter_sdxl.bin" +]) +pipe = SDXLImagePipeline.from_model_manager(model_manager) + +torch.manual_seed(123456) +style_image = pipe( + prompt="a rabbit in a garden, colorful flowers", + negative_prompt="anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", + cfg_scale=5, + height=1024, width=1024, num_inference_steps=50, +) +style_image.save("rabbit.jpg") + +image = pipe( + prompt="a cat", + negative_prompt="", + cfg_scale=5, + height=1024, width=1024, num_inference_steps=50, + ipadapter_images=[style_image], ipadapter_use_instant_style=True +) +image.save("rabbit_to_cat.jpg") + +image = pipe( + prompt="a rabbit is jumping", + negative_prompt="", + cfg_scale=5, + height=1024, width=1024, num_inference_steps=50, + ipadapter_images=[style_image], ipadapter_use_instant_style=False, ipadapter_scale=0.5 +) +image.save("rabbit_to_jumping_rabbit.jpg") + +image = pipe( + prompt="a cat", + negative_prompt="", + cfg_scale=5, + height=1024, width=1024, num_inference_steps=50, +) +image.save("rabbit_to_cat_without_ipa.jpg") + +image = pipe( + prompt="a rabbit is jumping", + negative_prompt="", + cfg_scale=5, + height=1024, width=1024, num_inference_steps=50, +) +image.save("rabbit_to_jumping_rabbit_without_ipa.jpg") \ No newline at end of file diff --git a/examples/Ip-Adapter/sdxl_ipadapter_multi_reference.py b/examples/Ip-Adapter/sdxl_ipadapter_multi_reference.py new file mode 100644 index 0000000000000000000000000000000000000000..4094a70358b9bc5bd3f58d3466661ef31d93d74d --- /dev/null +++ b/examples/Ip-Adapter/sdxl_ipadapter_multi_reference.py @@ -0,0 +1,34 @@ +from diffsynth import ModelManager, SDXLImagePipeline, download_models +import torch, requests +from PIL import Image + + +# Download models (automatically) +# `models/stable_diffusion_xl/bluePencilXL_v200.safetensors`: [link](https://civitai.com/api/download/models/245614?type=Model&format=SafeTensor&size=pruned&fp=fp16) +# `models/IpAdapter/stable_diffusion_xl/image_encoder/model.safetensors`: [link](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/model.safetensors) +# `models/IpAdapter/stable_diffusion_xl/ip-adapter_sdxl.bin`: [link](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl.safetensors) +download_models(["BluePencilXL_v200", "IP-Adapter-SDXL"]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models([ + "models/stable_diffusion_xl/bluePencilXL_v200.safetensors", + "models/IpAdapter/stable_diffusion_xl/image_encoder/model.safetensors", + "models/IpAdapter/stable_diffusion_xl/ip-adapter_sdxl.bin" +]) +pipe = SDXLImagePipeline.from_model_manager(model_manager) + +image_1 = Image.open(requests.get("https://media.52poke.com/wiki/7/7e/006Charizard.png", stream=True).raw).convert("RGB").resize((1024, 1024)) +image_1.save("Charizard.jpg") +image_2 = Image.open(requests.get("https://media.52poke.com/wiki/0/0d/025Pikachu.png", stream=True).raw).convert("RGB").resize((1024, 1024)) +image_2.save("Pikachu.jpg") + +torch.manual_seed(0) +image = pipe( + prompt="a pokemon, maybe Charizard, maybe Pikachu", + negative_prompt="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry", + cfg_scale=5, + height=1024, width=1024, num_inference_steps=50, + ipadapter_images=[image_1, image_2], ipadapter_use_instant_style=False, ipadapter_scale=0.7 +) +image.save(f"Pikazard.jpg") diff --git a/examples/diffsynth/README.md b/examples/diffsynth/README.md new file mode 100644 index 0000000000000000000000000000000000000000..761ef8065aae230573937c79474c9ba0421b52f2 --- /dev/null +++ b/examples/diffsynth/README.md @@ -0,0 +1,7 @@ +# DiffSynth + +DiffSynth is the initial version of our video synthesis framework. In this framework, you can apply video deflickering algorithms to the latent space of diffusion models. You can refer to the [original repo](https://github.com/alibaba/EasyNLP/tree/master/diffusion/DiffSynth) for more details. + +We provide an example for video stylization. In this pipeline, the rendered video is completely different from the original video, thus we need a powerful deflickering algorithm. We use FastBlend to implement the deflickering module. Please see [`sd_video_rerender.py`](./sd_video_rerender.py). + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea diff --git a/examples/diffsynth/sd_video_rerender.py b/examples/diffsynth/sd_video_rerender.py new file mode 100644 index 0000000000000000000000000000000000000000..dc0095486a19d015c18c3e99f21c0c68d1b3bb2c --- /dev/null +++ b/examples/diffsynth/sd_video_rerender.py @@ -0,0 +1,64 @@ +from diffsynth import ModelManager, SDVideoPipeline, ControlNetConfigUnit, VideoData, save_video, download_models +from diffsynth.processors.FastBlend import FastBlendSmoother +from diffsynth.processors.PILEditor import ContrastEditor, SharpnessEditor +from diffsynth.processors.sequencial_processor import SequencialProcessor +import torch + + +# Download models (automatically) +# `models/stable_diffusion/dreamshaper_8.safetensors`: [link](https://civitai.com/api/download/models/128713?type=Model&format=SafeTensor&size=pruned&fp=fp16) +# `models/ControlNet/control_v11f1p_sd15_depth.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth.pth) +# `models/ControlNet/control_v11p_sd15_softedge.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge.pth) +# `models/Annotators/dpt_hybrid-midas-501f0c75.pt`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/dpt_hybrid-midas-501f0c75.pt) +# `models/Annotators/ControlNetHED.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetHED.pth) +download_models([ + "ControlNet_v11f1p_sd15_depth", + "ControlNet_v11p_sd15_softedge", + "DreamShaper_8" +]) + +# Load models +model_manager = ModelManager( + torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/stable_diffusion/dreamshaper_8.safetensors", + "models/ControlNet/control_v11f1p_sd15_depth.pth", + "models/ControlNet/control_v11p_sd15_softedge.pth", + ] +) +pipe = SDVideoPipeline.from_model_manager( + model_manager, + [ + ControlNetConfigUnit( + processor_id="depth", + model_path=rf"models/ControlNet/control_v11f1p_sd15_depth.pth", + scale=0.5 + ), + ControlNetConfigUnit( + processor_id="softedge", + model_path=rf"models/ControlNet/control_v11p_sd15_softedge.pth", + scale=0.5 + ) + ] +) +smoother = SequencialProcessor([FastBlendSmoother(), ContrastEditor(rate=1.1), SharpnessEditor(rate=1.1)]) + +# Load video +# Original video: https://pixabay.com/videos/flow-rocks-water-fluent-stones-159627/ +video = VideoData(video_file="data/examples/pixabay100/159627 (1080p).mp4", height=512, width=768) +input_video = [video[i] for i in range(128)] + +# Rerender +torch.manual_seed(0) +output_video = pipe( + prompt="winter, ice, snow, water, river", + negative_prompt="", cfg_scale=7, + input_frames=input_video, controlnet_frames=input_video, num_frames=len(input_video), + num_inference_steps=20, height=512, width=768, + animatediff_batch_size=8, animatediff_stride=4, unet_batch_size=8, + cross_frame_attention=True, + smoother=smoother, smoother_progress_ids=[4, 9, 14, 19] +) + +# Save images and video +save_video(output_video, "output_video.mp4", fps=30) diff --git a/examples/image_synthesis/README.md b/examples/image_synthesis/README.md new file mode 100644 index 0000000000000000000000000000000000000000..800952e9df67645efbb822f20c882037744234d6 --- /dev/null +++ b/examples/image_synthesis/README.md @@ -0,0 +1,91 @@ +# Image Synthesis + +Image synthesis is the base feature of DiffSynth Studio. We can generate images with very high resolution. + +### Example: Stable Diffusion + +Example script: [`sd_text_to_image.py`](./sd_text_to_image.py) + +|512*512|1024*1024|2048*2048|4096*4096| +|-|-|-|-| +|![512](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/55f679e9-7445-4605-9315-302e93d11370)|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/6fc84611-8da6-4a1f-8fee-9a34eba3b4a5)|![2048](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/9087a73c-9164-4c58-b2a0-effc694143fb)|![4096](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/edee9e71-fc39-4d1c-9ca9-fa52002c67ac)| + +### Example: Stable Diffusion XL + +Example script: [`sdxl_text_to_image.py`](./sdxl_text_to_image.py) + +|1024*1024|2048*2048| +|-|-| +|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/67687748-e738-438c-aee5-96096f09ac90)|![2048](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/584186bc-9855-4140-878e-99541f9a757f)| + +### Example: Stable Diffusion 3 + +Example script: [`sd3_text_to_image.py`](./sd3_text_to_image.py) + +LoRA Training: [`../train/stable_diffusion_3/`](../train/stable_diffusion_3/) + +|1024*1024|2048*2048| +|-|-| +|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/4df346db-6f91-420a-b4c1-26e205376098)|![image_2048](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/1386c802-e580-4101-939d-f1596802df9d)| + +### Example: Kolors + +Example script: [`kolors_text_to_image.py`](./kolors_text_to_image.py) + +LoRA Training: [`../train/kolors/`](../train/kolors/) + +|1024*1024|2048*2048| +|-|-| +|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/53ef6f41-da11-4701-8665-9f64392607bf)|![image_2048](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/66bb7a75-fe31-44e5-90eb-d3140ee4686d)| + +### Example: Hunyuan-DiT + +Example script: [`hunyuan_dit_text_to_image.py`](./hunyuan_dit_text_to_image.py) + +LoRA Training: [`../train/hunyuan_dit/`](../train/hunyuan_dit/) + +|1024*1024|2048*2048| +|-|-| +|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/60b022c8-df3f-4541-95ab-bf39f2fa8bb5)|![image_2048](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/87919ea8-d428-4963-8257-da05f3901bbb)| + +### Example: Stable Diffusion XL Turbo + +Example script: [`sdxl_turbo.py`](./sdxl_turbo.py) + +We highly recommend you to use this model in the WebUI. + +|"black car"|"red car"| +|-|-| +|![black_car](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/7fbfd803-68d4-44f3-8713-8c925fec47d0)|![black_car_to_red_car](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/aaf886e4-c33c-4fd8-98e2-29eef117ba00)| + +### Example: Prompt Processing + +If you are not native English user, we provide translation service for you. Our prompter can translate other language to English and refine it using "BeautifulPrompt" models. Please see [`sd_prompt_refining.py`](./sd_prompt_refining.py) for more details. + +Prompt: "一个漂亮的女孩". The [translation model](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) will translate it to English. + +|seed=0|seed=1|seed=2|seed=3| +|-|-|-|-| +|![0_](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/ebb25ca8-7ce1-4d9e-8081-59a867c70c4d)|![1_](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/a7e79853-3c1a-471a-9c58-c209ec4b76dd)|![2_](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/a292b959-a121-481f-b79c-61cc3346f810)|![3_](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/1c19b54e-5a6f-4d48-960b-a7b2b149bb4c)| + +Prompt: "一个漂亮的女孩". The [translation model](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) will translate it to English. Then the [refining model](https://huggingface.co/alibaba-pai/pai-bloom-1b1-text2prompt-sd) will refine the translated prompt for better visual quality. + +|seed=0|seed=1|seed=2|seed=3| +|-|-|-|-| +|![0](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/778b1bd9-44e0-46ac-a99c-712b3fc9aaa4)|![1](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/c03479b8-2082-4c6e-8e1c-3582b98686f6)|![2](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/edb33d21-3288-4a55-96ca-a4bfe1b50b00)|![3](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/7848cfc1-cad5-4848-8373-41d24e98e584)| + +### Example: Stable Diffusion 3 with Textual Inversions (Experimental) + +Since Stable Diffusion 3 utilizes the same text encoder as Stable Diffusion 1.x, it supports the textual inversions designed for Stable Diffusion 1.x. However, we found that the textual inversions may cause unpredictable effects to the model. We can only guarantee that these textual inversions can be loaded into the model. The example script is [`sd3_text_to_image_textual_inversion.py`](./sd3_text_to_image_textual_inversion.py) + +Prompt: "a girl, highly detailed, absurd res, perfect image". Without any textual inversions. + +|seed=0|seed=1|seed=2|seed=3| +|-|-|-|-| +|![image_without_textual_inversion_0](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/4e918bf8-6081-4f79-a043-87adc4047d92)|![image_without_textual_inversion_1](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/2e90a01f-6a83-46ba-99b6-ab085582a5b7)|![image_without_textual_inversion_2](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/83570a6f-cddd-4d0a-8b2f-f50388e2ca8a)|![image_without_textual_inversion_3](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/f4d0f2d4-80ee-4281-923e-77d87e3d37b1)| + +Prompt: "a girl, highly detailed, absurd res, perfect image". With [`verybadimagenegative_v1.3`](https://civitai.com/models/11772/verybadimagenegative) on the negative side. + +|seed=0|seed=1|seed=2|seed=3| +|-|-|-|-| +|![image_with_textual_inversion_0](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/1b3485ee-e7c1-4306-8f93-c9f32d1ac937)|![image_with_textual_inversion_1](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/5d7c6c4b-afdf-42b0-8e94-1959f1a44491)|![image_with_textual_inversion_2](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/92e93c4e-2781-41df-a246-2d2e9bde97c4)|![image_with_textual_inversion_3](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/070966a0-3d5c-48d8-8199-9d7c80408689)| diff --git a/examples/image_synthesis/hunyuan_dit_text_to_image.py b/examples/image_synthesis/hunyuan_dit_text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..7e4df91a510a0f0c30eb9c955926f9ee5acbd520 --- /dev/null +++ b/examples/image_synthesis/hunyuan_dit_text_to_image.py @@ -0,0 +1,42 @@ +from diffsynth import ModelManager, HunyuanDiTImagePipeline, download_models +import torch + + +# Download models (automatically) +# `models/HunyuanDiT/t2i/clip_text_encoder/pytorch_model.bin`: [link](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/resolve/main/t2i/clip_text_encoder/pytorch_model.bin) +# `models/HunyuanDiT/t2i/mt5/pytorch_model.bin`: [link](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/resolve/main/t2i/mt5/pytorch_model.bin) +# `models/HunyuanDiT/t2i/model/pytorch_model_ema.pt`: [link](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/resolve/main/t2i/model/pytorch_model_ema.pt) +# `models/HunyuanDiT/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin`: [link](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/resolve/main/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin) +download_models(["HunyuanDiT"]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models([ + "models/HunyuanDiT/t2i/clip_text_encoder/pytorch_model.bin", + "models/HunyuanDiT/t2i/mt5/pytorch_model.bin", + "models/HunyuanDiT/t2i/model/pytorch_model_ema.pt", + "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin" +]) +pipe = HunyuanDiTImagePipeline.from_model_manager(model_manager) + +prompt = "一幅充满诗意美感的全身肖像画，画中一位银发、蓝色眼睛、身穿蓝色连衣裙的少女漂浮在水下，周围是光彩的气泡，和煦的阳光透过水面折射进水下" +negative_prompt = "错误的眼睛，糟糕的人脸，毁容，糟糕的艺术，变形，多余的肢体，模糊的颜色，模糊，重复，病态，残缺，" + +# Enjoy! +torch.manual_seed(0) +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + num_inference_steps=50, height=1024, width=1024, +) +image.save("image_1024.png") + +# Highres fix +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + input_image=image.resize((2048, 2048)), + num_inference_steps=50, height=2048, width=2048, + denoising_strength=0.4, tiled=True, +) +image.save("image_2048.png") \ No newline at end of file diff --git a/examples/image_synthesis/kolors_text_to_image.py b/examples/image_synthesis/kolors_text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..b9cb58361ec6c44be59c2da51bd95d68d9d7c8e6 --- /dev/null +++ b/examples/image_synthesis/kolors_text_to_image.py @@ -0,0 +1,34 @@ +from diffsynth import ModelManager, KolorsImagePipeline, download_models +import torch + +# Download models +# https://huggingface.co/Kwai-Kolors/Kolors +download_models(["Kolors"]) +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/kolors/Kolors/text_encoder", + "models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors", + "models/kolors/Kolors/vae/diffusion_pytorch_model.safetensors" + ]) +pipe = KolorsImagePipeline.from_model_manager(model_manager) + +prompt = "一幅充满诗意美感的全身画，泛红的肤色，画中一位银色长发、蓝色眼睛、肤色红润、身穿蓝色吊带连衣裙的少女漂浮在水下，面向镜头，周围是光彩的气泡，和煦的阳光透过水面折射进水下" +negative_prompt = "半身，苍白的肤色，蜡黄的肤色，尸体，错误的眼睛，糟糕的人脸，毁容，糟糕的艺术，变形，多余的肢体，模糊的颜色，模糊，重复，病态，残缺，错误的手指，口红，腮红" + +torch.manual_seed(7) +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + num_inference_steps=50, + cfg_scale=4, +) +image.save(f"image_1024.jpg") + +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + input_image=image.resize((2048, 2048)), denoising_strength=0.4, height=2048, width=2048, + num_inference_steps=50, + cfg_scale=4, +) +image.save("image_2048.jpg") diff --git a/examples/image_synthesis/sd3_text_to_image.py b/examples/image_synthesis/sd3_text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..d00fcfaa65ce160cc14376db5355a0d01638e6e2 --- /dev/null +++ b/examples/image_synthesis/sd3_text_to_image.py @@ -0,0 +1,33 @@ +from diffsynth import ModelManager, SD3ImagePipeline, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion_3/sd3_medium_incl_clips.safetensors`: [link](https://huggingface.co/stabilityai/stable-diffusion-3-medium/resolve/main/sd3_medium_incl_clips.safetensors) +download_models(["StableDiffusion3_without_T5"]) +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=["models/stable_diffusion_3/sd3_medium_incl_clips.safetensors"]) +pipe = SD3ImagePipeline.from_model_manager(model_manager) + + +prompt = "masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait," +negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," + +torch.manual_seed(7) +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=7.5, + num_inference_steps=100, width=1024, height=1024, +) +image.save("image_1024.jpg") + +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=7.5, + input_image=image.resize((2048, 2048)), denoising_strength=0.5, + num_inference_steps=50, width=2048, height=2048, + tiled=True +) +image.save("image_2048.jpg") diff --git a/examples/image_synthesis/sd3_text_to_image_textual_inversion.py b/examples/image_synthesis/sd3_text_to_image_textual_inversion.py new file mode 100644 index 0000000000000000000000000000000000000000..1cf52566e7f5ac6e7dcce680fe297c970d74eda8 --- /dev/null +++ b/examples/image_synthesis/sd3_text_to_image_textual_inversion.py @@ -0,0 +1,32 @@ +from diffsynth import ModelManager, SD3ImagePipeline, download_models, load_state_dict +import torch + + +# Download models (automatically) +# `models/stable_diffusion_3/sd3_medium_incl_clips.safetensors`: [link](https://huggingface.co/stabilityai/stable-diffusion-3-medium/resolve/main/sd3_medium_incl_clips.safetensors) +# `models/textual_inversion/verybadimagenegative_v1.3.pt`: [link](https://civitai.com/api/download/models/25820?type=Model&format=PickleTensor&size=full&fp=fp16) +download_models(["StableDiffusion3_without_T5", "TextualInversion_VeryBadImageNegative_v1.3"]) +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_textual_inversions("models/textual_inversion") +model_manager.load_models(["models/stable_diffusion_3/sd3_medium_incl_clips.safetensors"]) +pipe = SD3ImagePipeline.from_model_manager(model_manager) + + +for seed in range(4): + torch.manual_seed(seed) + image = pipe( + prompt="a girl, highly detailed, absurd res, perfect image", + negative_prompt="verybadimagenegative_v1.3", + cfg_scale=4.5, + num_inference_steps=50, width=1024, height=1024, + ) + image.save(f"image_with_textual_inversion_{seed}.jpg") + + torch.manual_seed(seed) + image = pipe( + prompt="a girl, highly detailed, absurd res, perfect image", + negative_prompt="", + cfg_scale=4.5, + num_inference_steps=50, width=1024, height=1024, + ) + image.save(f"image_without_textual_inversion_{seed}.jpg") diff --git a/examples/image_synthesis/sd_prompt_refining.py b/examples/image_synthesis/sd_prompt_refining.py new file mode 100644 index 0000000000000000000000000000000000000000..7d9bae69aae834f05c4084668d01cc0b859def54 --- /dev/null +++ b/examples/image_synthesis/sd_prompt_refining.py @@ -0,0 +1,30 @@ +from diffsynth import ModelManager, SDXLImagePipeline, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion_xl/sd_xl_base_1.0.safetensors`: [link](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors) +# `models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd/`: [link](https://huggingface.co/alibaba-pai/pai-bloom-1b1-text2prompt-sd) +# `models/translator/opus-mt-zh-en/`: [link](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) +download_models(["StableDiffusionXL_v1", "BeautifulPrompt", "opus-mt-zh-en"]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models([ + "models/stable_diffusion_xl/sd_xl_base_1.0.safetensors", + "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd/model.safetensors", + "models/translator/opus-mt-zh-en/pytorch_model.bin" +]) +pipe = SDXLImagePipeline.from_model_manager(model_manager) + +prompt = "一个漂亮的女孩" +negative_prompt = "" + +for seed in range(4): + torch.manual_seed(seed) + image = pipe( + prompt=prompt, negative_prompt=negative_prompt, + height=1024, width=1024, + num_inference_steps=30 + ) + image.save(f"{seed}.jpg") diff --git a/examples/image_synthesis/sd_text_to_image.py b/examples/image_synthesis/sd_text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..4d553b2e6a680f312c2a0345fa3eb55ff76d7291 --- /dev/null +++ b/examples/image_synthesis/sd_text_to_image.py @@ -0,0 +1,75 @@ +from diffsynth import ModelManager, SDImagePipeline, ControlNetConfigUnit, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion/aingdiffusion_v12.safetensors`: [link](https://civitai.com/api/download/models/229575?type=Model&format=SafeTensor&size=full&fp=fp16) +# `models/ControlNet/control_v11p_sd15_lineart.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart.pth) +# `models/ControlNet/control_v11f1e_sd15_tile.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile.pth) +# `models/Annotators/sk_model.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model.pth) +# `models/Annotators/sk_model2.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model2.pth) +download_models(["AingDiffusion_v12", "ControlNet_v11p_sd15_lineart", "ControlNet_v11f1e_sd15_tile"]) + + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/stable_diffusion/aingdiffusion_v12.safetensors", + "models/ControlNet/control_v11f1e_sd15_tile.pth", + "models/ControlNet/control_v11p_sd15_lineart.pth" + ]) +pipe = SDImagePipeline.from_model_manager( + model_manager, + [ + ControlNetConfigUnit( + processor_id="tile", + model_path=rf"models/ControlNet/control_v11f1e_sd15_tile.pth", + scale=0.5 + ), + ControlNetConfigUnit( + processor_id="lineart", + model_path=rf"models/ControlNet/control_v11p_sd15_lineart.pth", + scale=0.7 + ), + ] +) + +prompt = "masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait," +negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," + +torch.manual_seed(0) +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=7.5, clip_skip=1, + height=512, width=512, num_inference_steps=80, +) +image.save("512.jpg") + +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=7.5, clip_skip=1, + input_image=image.resize((1024, 1024)), controlnet_image=image.resize((1024, 1024)), + height=1024, width=1024, num_inference_steps=40, denoising_strength=0.7, +) +image.save("1024.jpg") + +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=7.5, clip_skip=1, + input_image=image.resize((2048, 2048)), controlnet_image=image.resize((2048, 2048)), + height=2048, width=2048, num_inference_steps=20, denoising_strength=0.7, +) +image.save("2048.jpg") + +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=7.5, clip_skip=1, + input_image=image.resize((4096, 4096)), controlnet_image=image.resize((4096, 4096)), + height=4096, width=4096, num_inference_steps=10, denoising_strength=0.5, + tiled=True, tile_size=128, tile_stride=64 +) +image.save("4096.jpg") diff --git a/examples/image_synthesis/sdxl_text_to_image.py b/examples/image_synthesis/sdxl_text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..fcae3abd3ab7b9f7850eaef9fc04d5ca862664c6 --- /dev/null +++ b/examples/image_synthesis/sdxl_text_to_image.py @@ -0,0 +1,34 @@ +from diffsynth import ModelManager, SDXLImagePipeline, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion_xl/bluePencilXL_v200.safetensors`: [link](https://civitai.com/api/download/models/245614?type=Model&format=SafeTensor&size=pruned&fp=fp16) +download_models(["BluePencilXL_v200"]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models(["models/stable_diffusion_xl/bluePencilXL_v200.safetensors"]) +pipe = SDXLImagePipeline.from_model_manager(model_manager) + +prompt = "masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait," +negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," + +torch.manual_seed(0) +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=6, + height=1024, width=1024, num_inference_steps=60, +) +image.save("1024.jpg") + +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=6, + input_image=image.resize((2048, 2048)), + height=2048, width=2048, num_inference_steps=60, denoising_strength=0.5 +) +image.save("2048.jpg") + diff --git a/examples/image_synthesis/sdxl_turbo.py b/examples/image_synthesis/sdxl_turbo.py new file mode 100644 index 0000000000000000000000000000000000000000..c39fb088795c98860032bf7b6f5290e553809170 --- /dev/null +++ b/examples/image_synthesis/sdxl_turbo.py @@ -0,0 +1,31 @@ +from diffsynth import ModelManager, SDXLImagePipeline, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion_xl_turbo/sd_xl_turbo_1.0_fp16.safetensors`: [link](https://huggingface.co/stabilityai/sdxl-turbo/resolve/main/sd_xl_turbo_1.0_fp16.safetensors) +download_models(["StableDiffusionXL_Turbo"]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models(["models/stable_diffusion_xl_turbo/sd_xl_turbo_1.0_fp16.safetensors"]) +pipe = SDXLImagePipeline.from_model_manager(model_manager) + +# Text to image +torch.manual_seed(0) +image = pipe( + prompt="black car", + # Do not modify the following parameters! + cfg_scale=1, height=512, width=512, num_inference_steps=1, progress_bar_cmd=lambda x:x +) +image.save(f"black_car.jpg") + +# Image to image +torch.manual_seed(0) +image = pipe( + prompt="red car", + input_image=image, denoising_strength=0.7, + # Do not modify the following parameters! + cfg_scale=1, height=512, width=512, num_inference_steps=1, progress_bar_cmd=lambda x:x +) +image.save(f"black_car_to_red_car.jpg") diff --git a/examples/train/hunyuan_dit/README.md b/examples/train/hunyuan_dit/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a4f5d88bffaa07b57b93ec8901566e2e3c85b050 --- /dev/null +++ b/examples/train/hunyuan_dit/README.md @@ -0,0 +1,181 @@ +# Hunyuan DiT + +Hunyuan DiT is an image generation model based on DiT. We provide training and inference support for Hunyuan DiT. + +## Download models + +Four files will be used for constructing Hunyuan DiT. You can download them from [huggingface](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT) or [modelscope](https://www.modelscope.cn/models/modelscope/HunyuanDiT/summary). + +``` +models/HunyuanDiT/ +├── Put Hunyuan DiT checkpoints here.txt +└── t2i + ├── clip_text_encoder + │ └── pytorch_model.bin + ├── model + │ └── pytorch_model_ema.pt + ├── mt5 + │ └── pytorch_model.bin + └── sdxl-vae-fp16-fix + └── diffusion_pytorch_model.bin +``` + +You can use the following code to download these files: + +```python +from diffsynth import download_models + +download_models(["HunyuanDiT"]) +``` + +## Train + +### Install training dependency + +``` +pip install peft lightning pandas torchvision +``` + +### Prepare your dataset + +We provide an example dataset [here](https://modelscope.cn/datasets/buptwq/lora-stable-diffusion-finetune/files). You need to manage the training images as follows: + +``` +data/dog/ +└── train + ├── 00.jpg + ├── 01.jpg + ├── 02.jpg + ├── 03.jpg + ├── 04.jpg + └── metadata.csv +``` + +`metadata.csv`: + +``` +file_name,text +00.jpg,一只小狗 +01.jpg,一只小狗 +02.jpg,一只小狗 +03.jpg,一只小狗 +04.jpg,一只小狗 +``` + +### Train a LoRA model + +We provide a training script `train_hunyuan_dit_lora.py`. Before you run this training script, please copy it to the root directory of this project. + +If GPU memory >= 24GB, we recommmand to use the following settings. + +``` +CUDA_VISIBLE_DEVICES="0" python train_hunyuan_dit_lora.py \ + --pretrained_path models/HunyuanDiT/t2i \ + --dataset_path data/dog \ + --output_path ./models \ + --max_epochs 1 \ + --center_crop +``` + +If 12GB <= GPU memory <= 24GB, we recommand to enable gradient checkpointing. + +``` +CUDA_VISIBLE_DEVICES="0" python train_hunyuan_dit_lora.py \ + --pretrained_path models/HunyuanDiT/t2i \ + --dataset_path data/dog \ + --output_path ./models \ + --max_epochs 1 \ + --center_crop \ + --use_gradient_checkpointing +``` + +Optional arguments: +``` + -h, --help show this help message and exit + --pretrained_path PRETRAINED_PATH + Path to pretrained model. For example, `./HunyuanDiT/t2i`. + --dataset_path DATASET_PATH + The path of the Dataset. + --output_path OUTPUT_PATH + Path to save the model. + --steps_per_epoch STEPS_PER_EPOCH + Number of steps per epoch. + --height HEIGHT Image height. + --width WIDTH Image width. + --center_crop Whether to center crop the input images to the resolution. If not set, the images will be randomly cropped. The images will be resized to the resolution first before cropping. + --random_flip Whether to randomly flip images horizontally + --batch_size BATCH_SIZE + Batch size (per device) for the training dataloader. + --dataloader_num_workers DATALOADER_NUM_WORKERS + Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process. + --precision {32,16,16-mixed} + Training precision + --learning_rate LEARNING_RATE + Learning rate. + --lora_rank LORA_RANK + The dimension of the LoRA update matrices. + --lora_alpha LORA_ALPHA + The weight of the LoRA update matrices. + --use_gradient_checkpointing + Whether to use gradient checkpointing. + --accumulate_grad_batches ACCUMULATE_GRAD_BATCHES + The number of batches in gradient accumulation. + --training_strategy {auto,deepspeed_stage_1,deepspeed_stage_2,deepspeed_stage_3} + Training strategy + --max_epochs MAX_EPOCHS + Number of epochs. +``` + +### Inference with your own LoRA model + +After training, you can use your own LoRA model to generate new images. Here are some examples. + +```python +from diffsynth import ModelManager, HunyuanDiTImagePipeline +from peft import LoraConfig, inject_adapter_in_model +import torch + + +def load_lora(dit, lora_rank, lora_alpha, lora_path): + lora_config = LoraConfig( + r=lora_rank, + lora_alpha=lora_alpha, + init_lora_weights="gaussian", + target_modules=["to_q", "to_k", "to_v", "to_out"], + ) + dit = inject_adapter_in_model(lora_config, dit) + state_dict = torch.load(lora_path, map_location="cpu") + dit.load_state_dict(state_dict, strict=False) + return dit + + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models([ + "models/HunyuanDiT/t2i/clip_text_encoder/pytorch_model.bin", + "models/HunyuanDiT/t2i/mt5/pytorch_model.bin", + "models/HunyuanDiT/t2i/model/pytorch_model_ema.pt", + "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin" +]) +pipe = HunyuanDiTImagePipeline.from_model_manager(model_manager) + +# Generate an image with lora +pipe.dit = load_lora( + pipe.dit, + lora_rank=4, lora_alpha=4.0, # The two parameters should be consistent with those in your training script. + lora_path="path/to/your/lora/model/lightning_logs/version_x/checkpoints/epoch=x-step=xxx.ckpt" +) +torch.manual_seed(0) +image = pipe( + prompt="一只小狗蹦蹦跳跳，周围是姹紫嫣红的鲜花，远处是山脉", + negative_prompt="", + num_inference_steps=50, height=1024, width=1024, +) +image.save("image_with_lora.png") +``` + +Prompt: 一只小狗蹦蹦跳跳，周围是姹紫嫣红的鲜花，远处是山脉 + +|Without LoRA|With LoRA| +|-|-| +|![image_without_lora](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/1aa21de5-a992-4b66-b14f-caa44e08876e)|![image_with_lora](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/83a0a41a-691f-4610-8e7b-d8e17c50a282)| diff --git a/examples/train/hunyuan_dit/train_hunyuan_dit_lora.py b/examples/train/hunyuan_dit/train_hunyuan_dit_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..118b8fe96d2d133666804a07397471bf4f78f13a --- /dev/null +++ b/examples/train/hunyuan_dit/train_hunyuan_dit_lora.py @@ -0,0 +1,298 @@ +from diffsynth import ModelManager, HunyuanDiTImagePipeline +from peft import LoraConfig, inject_adapter_in_model +from torchvision import transforms +from PIL import Image +import lightning as pl +import pandas as pd +import torch, os, argparse +os.environ["TOKENIZERS_PARALLELISM"] = "True" + + + +class TextImageDataset(torch.utils.data.Dataset): + def __init__(self, dataset_path, steps_per_epoch=10000, height=1024, width=1024, center_crop=True, random_flip=False): + self.steps_per_epoch = steps_per_epoch + metadata = pd.read_csv(os.path.join(dataset_path, "train/metadata.csv")) + self.path = [os.path.join(dataset_path, "train", file_name) for file_name in metadata["file_name"]] + self.text = metadata["text"].to_list() + self.image_processor = transforms.Compose( + [ + transforms.Resize(max(height, width), interpolation=transforms.InterpolationMode.BILINEAR), + transforms.CenterCrop((height, width)) if center_crop else transforms.RandomCrop((height, width)), + transforms.RandomHorizontalFlip() if random_flip else transforms.Lambda(lambda x: x), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ] + ) + + + def __getitem__(self, index): + data_id = torch.randint(0, len(self.path), (1,))[0] + data_id = (data_id + index) % len(self.path) # For fixed seed. + text = self.text[data_id] + image = Image.open(self.path[data_id]).convert("RGB") + image = self.image_processor(image) + return {"text": text, "image": image} + + + def __len__(self): + return self.steps_per_epoch + + + +class LightningModel(pl.LightningModule): + def __init__(self, torch_dtype=torch.float16, learning_rate=1e-4, pretrained_weights=[], lora_rank=4, lora_alpha=4, use_gradient_checkpointing=True): + super().__init__() + + # Load models + model_manager = ModelManager(torch_dtype=torch_dtype, device=self.device) + model_manager.load_models(pretrained_weights) + self.pipe = HunyuanDiTImagePipeline.from_model_manager(model_manager) + + # Freeze parameters + self.pipe.text_encoder.requires_grad_(False) + self.pipe.text_encoder_t5.requires_grad_(False) + self.pipe.dit.requires_grad_(False) + self.pipe.vae_decoder.requires_grad_(False) + self.pipe.vae_encoder.requires_grad_(False) + self.pipe.text_encoder.eval() + self.pipe.text_encoder_t5.eval() + self.pipe.dit.train() + self.pipe.vae_decoder.eval() + self.pipe.vae_encoder.eval() + + # Add LoRA to DiT + lora_config = LoraConfig( + r=lora_rank, + lora_alpha=lora_alpha, + init_lora_weights="gaussian", + target_modules=["to_q", "to_k", "to_v", "to_out"], + ) + self.pipe.dit = inject_adapter_in_model(lora_config, self.pipe.dit) + for param in self.pipe.dit.parameters(): + # Upcast LoRA parameters into fp32 + if param.requires_grad: + param.data = param.to(torch.float32) + + # Set other parameters + self.learning_rate = learning_rate + self.use_gradient_checkpointing = use_gradient_checkpointing + + + def training_step(self, batch, batch_idx): + # Data + text, image = batch["text"], batch["image"] + + # Prepare input parameters + self.pipe.device = self.device + prompt_emb, attention_mask, prompt_emb_t5, attention_mask_t5 = self.pipe.prompter.encode_prompt( + self.pipe.text_encoder, self.pipe.text_encoder_t5, text, positive=True, device=self.device + ) + latents = self.pipe.vae_encoder(image.to(dtype=self.pipe.torch_dtype, device=self.device)) + noise = torch.randn_like(latents) + timestep = torch.randint(0, 1000, (1,), device=self.device) + extra_input = self.pipe.prepare_extra_input(image.shape[-2], image.shape[-1], batch_size=latents.shape[0]) + noisy_latents = self.pipe.scheduler.add_noise(latents, noise, timestep) + training_target = self.pipe.scheduler.training_target(latents, noise, timestep) + + # Compute loss + noise_pred = self.pipe.dit( + noisy_latents, + prompt_emb, prompt_emb_t5, attention_mask, attention_mask_t5, + timestep, + **extra_input, + use_gradient_checkpointing=self.use_gradient_checkpointing + ) + loss = torch.nn.functional.mse_loss(noise_pred, training_target) + + # Record log + self.log("train_loss", loss, prog_bar=True) + return loss + + + def configure_optimizers(self): + trainable_modules = filter(lambda p: p.requires_grad, self.pipe.dit.parameters()) + optimizer = torch.optim.AdamW(trainable_modules, lr=self.learning_rate) + return optimizer + + + def on_save_checkpoint(self, checkpoint): + checkpoint.clear() + trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.pipe.dit.named_parameters())) + trainable_param_names = set([named_param[0] for named_param in trainable_param_names]) + state_dict = self.pipe.dit.state_dict() + for name, param in state_dict.items(): + if name in trainable_param_names: + checkpoint[name] = param + + + +def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--pretrained_path", + type=str, + default=None, + required=True, + help="Path to pretrained model. For example, `./HunyuanDiT/t2i`.", + ) + parser.add_argument( + "--dataset_path", + type=str, + default=None, + required=True, + help="The path of the Dataset.", + ) + parser.add_argument( + "--output_path", + type=str, + default="./", + help="Path to save the model.", + ) + parser.add_argument( + "--steps_per_epoch", + type=int, + default=500, + help="Number of steps per epoch.", + ) + parser.add_argument( + "--height", + type=int, + default=1024, + help="Image height.", + ) + parser.add_argument( + "--width", + type=int, + default=1024, + help="Image width.", + ) + parser.add_argument( + "--center_crop", + default=False, + action="store_true", + help=( + "Whether to center crop the input images to the resolution. If not set, the images will be randomly" + " cropped. The images will be resized to the resolution first before cropping." + ), + ) + parser.add_argument( + "--random_flip", + default=False, + action="store_true", + help="Whether to randomly flip images horizontally", + ) + parser.add_argument( + "--batch_size", + type=int, + default=1, + help="Batch size (per device) for the training dataloader.", + ) + parser.add_argument( + "--dataloader_num_workers", + type=int, + default=0, + help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", + ) + parser.add_argument( + "--precision", + type=str, + default="16-mixed", + choices=["32", "16", "16-mixed"], + help="Training precision", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=1e-4, + help="Learning rate.", + ) + parser.add_argument( + "--lora_rank", + type=int, + default=4, + help="The dimension of the LoRA update matrices.", + ) + parser.add_argument( + "--lora_alpha", + type=float, + default=4.0, + help="The weight of the LoRA update matrices.", + ) + parser.add_argument( + "--use_gradient_checkpointing", + default=False, + action="store_true", + help="Whether to use gradient checkpointing.", + ) + parser.add_argument( + "--accumulate_grad_batches", + type=int, + default=1, + help="The number of batches in gradient accumulation.", + ) + parser.add_argument( + "--training_strategy", + type=str, + default="auto", + choices=["auto", "deepspeed_stage_1", "deepspeed_stage_2", "deepspeed_stage_3"], + help="Training strategy", + ) + parser.add_argument( + "--max_epochs", + type=int, + default=1, + help="Number of epochs.", + ) + args = parser.parse_args() + return args + + + +if __name__ == '__main__': + # args + args = parse_args() + + # dataset and data loader + dataset = TextImageDataset( + args.dataset_path, + steps_per_epoch=args.steps_per_epoch * args.batch_size, + height=args.height, + width=args.width, + center_crop=args.center_crop, + random_flip=args.random_flip + ) + train_loader = torch.utils.data.DataLoader( + dataset, + shuffle=True, + batch_size=args.batch_size, + num_workers=args.dataloader_num_workers + ) + + # model + model = LightningModel( + pretrained_weights=[ + os.path.join(args.pretrained_path, "clip_text_encoder/pytorch_model.bin"), + os.path.join(args.pretrained_path, "mt5/pytorch_model.bin"), + os.path.join(args.pretrained_path, "model/pytorch_model_ema.pt"), + os.path.join(args.pretrained_path, "sdxl-vae-fp16-fix/diffusion_pytorch_model.bin"), + ], + torch_dtype=torch.float32 if args.precision == "32" else torch.float16, + learning_rate=args.learning_rate, + lora_rank=args.lora_rank, + lora_alpha=args.lora_alpha, + use_gradient_checkpointing=args.use_gradient_checkpointing + ) + + # train + trainer = pl.Trainer( + max_epochs=args.max_epochs, + accelerator="gpu", + devices="auto", + precision=args.precision, + strategy=args.training_strategy, + default_root_dir=args.output_path, + accumulate_grad_batches=args.accumulate_grad_batches, + callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)] + ) + trainer.fit(model=model, train_dataloaders=train_loader) diff --git a/examples/train/kolors/README.md b/examples/train/kolors/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ba505b4fff2ee7d14f68732a586e4cc3ff5ce9a1 --- /dev/null +++ b/examples/train/kolors/README.md @@ -0,0 +1,185 @@ +# Kolors + +Kolors is a Chinese diffusion model, which is based on ChatGLM and Stable Diffusion XL. We provide training scripts here. + +## Download models + +The following files will be used for constructing Kolors. You can download Kolors from [huggingface](https://huggingface.co/Kwai-Kolors/Kolors) or [modelscope](https://modelscope.cn/models/Kwai-Kolors/Kolors). Due to precision overflow issues, we need to download an additional VAE model (from [huggingface](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix) or [modelscope](https://modelscope.cn/models/AI-ModelScope/sdxl-vae-fp16-fix)). + +``` +models +├── kolors +│ └── Kolors +│ ├── text_encoder +│ │ ├── config.json +│ │ ├── pytorch_model-00001-of-00007.bin +│ │ ├── pytorch_model-00002-of-00007.bin +│ │ ├── pytorch_model-00003-of-00007.bin +│ │ ├── pytorch_model-00004-of-00007.bin +│ │ ├── pytorch_model-00005-of-00007.bin +│ │ ├── pytorch_model-00006-of-00007.bin +│ │ ├── pytorch_model-00007-of-00007.bin +│ │ └── pytorch_model.bin.index.json +│ ├── unet +│ │ └── diffusion_pytorch_model.safetensors +│ └── vae +│ └── diffusion_pytorch_model.safetensors +└── sdxl-vae-fp16-fix + └── diffusion_pytorch_model.safetensors +``` + +You can use the following code to download these files: + +```python +from diffsynth import download_models + +download_models(["Kolors", "SDXL-vae-fp16-fix"]) +``` + +## Train + +### Install training dependency + +``` +pip install peft lightning pandas torchvision +``` + +### Prepare your dataset + +We provide an example dataset [here](https://modelscope.cn/datasets/buptwq/lora-stable-diffusion-finetune/files). You need to manage the training images as follows: + +``` +data/dog/ +└── train + ├── 00.jpg + ├── 01.jpg + ├── 02.jpg + ├── 03.jpg + ├── 04.jpg + └── metadata.csv +``` + +`metadata.csv`: + +``` +file_name,text +00.jpg,一只小狗 +01.jpg,一只小狗 +02.jpg,一只小狗 +03.jpg,一只小狗 +04.jpg,一只小狗 +``` + +### Train a LoRA model + +We provide a training script `train_kolors_lora.py`. Before you run this training script, please copy it to the root directory of this project. + +The following settings are recommended. 22GB VRAM is required. + +``` +CUDA_VISIBLE_DEVICES="0" python examples/train/kolors/train_kolors_lora.py \ + --pretrained_unet_path models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors \ + --pretrained_text_encoder_path models/kolors/Kolors/text_encoder \ + --pretrained_fp16_vae_path models/sdxl-vae-fp16-fix/diffusion_pytorch_model.safetensors \ + --dataset_path data/dog \ + --output_path ./models \ + --max_epochs 10 \ + --center_crop \ + --use_gradient_checkpointing \ + --precision "16-mixed" +``` + +Optional arguments: +``` + -h, --help show this help message and exit + --pretrained_unet_path PRETRAINED_UNET_PATH + Path to pretrained model (UNet). For example, `models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors`. + --pretrained_text_encoder_path PRETRAINED_TEXT_ENCODER_PATH + Path to pretrained model (Text Encoder). For example, `models/kolors/Kolors/text_encoder`. + --pretrained_fp16_vae_path PRETRAINED_FP16_VAE_PATH + Path to pretrained model (VAE). For example, `models/kolors/Kolors/sdxl-vae-fp16-fix/diffusion_pytorch_model.safetensors`. + --dataset_path DATASET_PATH + The path of the Dataset. + --output_path OUTPUT_PATH + Path to save the model. + --steps_per_epoch STEPS_PER_EPOCH + Number of steps per epoch. + --height HEIGHT Image height. + --width WIDTH Image width. + --center_crop Whether to center crop the input images to the resolution. If not set, the images will be randomly cropped. The images will be resized to the resolution first before cropping. + --random_flip Whether to randomly flip images horizontally + --batch_size BATCH_SIZE + Batch size (per device) for the training dataloader. + --dataloader_num_workers DATALOADER_NUM_WORKERS + Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process. + --precision {32,16,16-mixed} + Training precision + --learning_rate LEARNING_RATE + Learning rate. + --lora_rank LORA_RANK + The dimension of the LoRA update matrices. + --lora_alpha LORA_ALPHA + The weight of the LoRA update matrices. + --use_gradient_checkpointing + Whether to use gradient checkpointing. + --accumulate_grad_batches ACCUMULATE_GRAD_BATCHES + The number of batches in gradient accumulation. + --training_strategy {auto,deepspeed_stage_1,deepspeed_stage_2,deepspeed_stage_3} + Training strategy + --max_epochs MAX_EPOCHS + Number of epochs. +``` + +### Inference with your own LoRA model + +After training, you can use your own LoRA model to generate new images. Here are some examples. + +```python +from diffsynth import ModelManager, KolorsImagePipeline +from peft import LoraConfig, inject_adapter_in_model +import torch + + +def load_lora(model, lora_rank, lora_alpha, lora_path): + lora_config = LoraConfig( + r=lora_rank, + lora_alpha=lora_alpha, + init_lora_weights="gaussian", + target_modules=["to_q", "to_k", "to_v", "to_out"], + ) + model = inject_adapter_in_model(lora_config, model) + state_dict = torch.load(lora_path, map_location="cpu") + model.load_state_dict(state_dict, strict=False) + return model + + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/kolors/Kolors/text_encoder", + "models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors", + "models/kolors/Kolors/vae/diffusion_pytorch_model.safetensors" + ]) +pipe = KolorsImagePipeline.from_model_manager(model_manager) + +# Generate an image with lora +pipe.unet = load_lora( + pipe.unet, + lora_rank=4, lora_alpha=4.0, # The two parameters should be consistent with those in your training script. + lora_path="path/to/your/lora/model/lightning_logs/version_x/checkpoints/epoch=x-step=xxx.ckpt" +) +torch.manual_seed(0) +image = pipe( + prompt="一只小狗蹦蹦跳跳，周围是姹紫嫣红的鲜花，远处是山脉", + negative_prompt="", + cfg_scale=4, + num_inference_steps=50, height=1024, width=1024, +) +image.save("image_with_lora.jpg") +``` + +Prompt: 一只小狗蹦蹦跳跳，周围是姹紫嫣红的鲜花，远处是山脉 + +|Without LoRA|With LoRA| +|-|-| +|![image_without_lora](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/9d79ed7a-e8cf-4d98-800a-f182809db318)|![image_with_lora](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/02f62323-6ee5-4788-97a1-549732dbe4f0)| diff --git a/examples/train/kolors/train_kolors_lora.py b/examples/train/kolors/train_kolors_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..7f5c4cac2a284167da3874e51a98954e84418d14 --- /dev/null +++ b/examples/train/kolors/train_kolors_lora.py @@ -0,0 +1,322 @@ +from diffsynth import KolorsImagePipeline, load_state_dict, ChatGLMModel, SDXLUNet, SDXLVAEEncoder +from peft import LoraConfig, inject_adapter_in_model +from torchvision import transforms +from PIL import Image +import lightning as pl +import pandas as pd +import torch, os, argparse +os.environ["TOKENIZERS_PARALLELISM"] = "True" + + + +class TextImageDataset(torch.utils.data.Dataset): + def __init__(self, dataset_path, steps_per_epoch=10000, height=1024, width=1024, center_crop=True, random_flip=False): + self.steps_per_epoch = steps_per_epoch + metadata = pd.read_csv(os.path.join(dataset_path, "train/metadata.csv")) + self.path = [os.path.join(dataset_path, "train", file_name) for file_name in metadata["file_name"]] + self.text = metadata["text"].to_list() + self.image_processor = transforms.Compose( + [ + transforms.Resize(max(height, width), interpolation=transforms.InterpolationMode.BILINEAR), + transforms.CenterCrop((height, width)) if center_crop else transforms.RandomCrop((height, width)), + transforms.RandomHorizontalFlip() if random_flip else transforms.Lambda(lambda x: x), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ] + ) + + + def __getitem__(self, index): + data_id = torch.randint(0, len(self.path), (1,))[0] + data_id = (data_id + index) % len(self.path) # For fixed seed. + text = self.text[data_id] + image = Image.open(self.path[data_id]).convert("RGB") + image = self.image_processor(image) + return {"text": text, "image": image} + + + def __len__(self): + return self.steps_per_epoch + + + +def load_model_from_diffsynth(ModelClass, model_kwargs, state_dict_path, torch_dtype, device): + model = ModelClass(**model_kwargs).to(dtype=torch_dtype, device=device) + state_dict = load_state_dict(state_dict_path, torch_dtype=torch_dtype) + model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict)) + return model + + +def load_model_from_transformers(ModelClass, model_kwargs, state_dict_path, torch_dtype, device): + model = ModelClass.from_pretrained(state_dict_path, torch_dtype=torch_dtype) + model = model.to(dtype=torch_dtype, device=device) + return model + + + +class LightningModel(pl.LightningModule): + def __init__( + self, + pretrained_unet_path, pretrained_text_encoder_path, pretrained_fp16_vae_path, + torch_dtype=torch.float16, learning_rate=1e-4, lora_rank=4, lora_alpha=4, use_gradient_checkpointing=True + ): + super().__init__() + + # Load models + self.pipe = KolorsImagePipeline(device=self.device, torch_dtype=torch_dtype) + self.pipe.text_encoder = load_model_from_transformers(ChatGLMModel, {}, pretrained_text_encoder_path, torch_dtype, self.device) + self.pipe.unet = load_model_from_diffsynth(SDXLUNet, {"is_kolors": True}, pretrained_unet_path, torch_dtype, self.device) + self.pipe.vae_encoder = load_model_from_diffsynth(SDXLVAEEncoder, {}, pretrained_fp16_vae_path, torch_dtype, self.device) + + # Freeze parameters + self.pipe.text_encoder.requires_grad_(False) + self.pipe.unet.requires_grad_(False) + self.pipe.vae_encoder.requires_grad_(False) + self.pipe.text_encoder.eval() + self.pipe.unet.train() + self.pipe.vae_encoder.eval() + + # Add LoRA to UNet + lora_config = LoraConfig( + r=lora_rank, + lora_alpha=lora_alpha, + init_lora_weights="gaussian", + target_modules=["to_q", "to_k", "to_v", "to_out"], + ) + self.pipe.unet = inject_adapter_in_model(lora_config, self.pipe.unet) + for param in self.pipe.unet.parameters(): + # Upcast LoRA parameters into fp32 + if param.requires_grad: + param.data = param.to(torch.float32) + + # Set other parameters + self.learning_rate = learning_rate + self.use_gradient_checkpointing = use_gradient_checkpointing + self.pipe.scheduler.set_timesteps(1100) + + + def training_step(self, batch, batch_idx): + # Data + text, image = batch["text"], batch["image"] + + # Prepare input parameters + self.pipe.device = self.device + add_prompt_emb, prompt_emb = self.pipe.prompter.encode_prompt( + self.pipe.text_encoder, text, clip_skip=2, device=self.device, positive=True, + ) + height, width = image.shape[-2:] + latents = self.pipe.vae_encoder(image.to(self.device)) + noise = torch.randn_like(latents) + timestep = torch.randint(0, 1100, (1,), device=self.device)[0] + add_time_id = torch.tensor([height, width, 0, 0, height, width], device=self.device) + noisy_latents = self.pipe.scheduler.add_noise(latents, noise, timestep) + + # Compute loss + noise_pred = self.pipe.unet( + noisy_latents, timestep, prompt_emb, add_time_id, add_prompt_emb, + use_gradient_checkpointing=self.use_gradient_checkpointing + ) + loss = torch.nn.functional.mse_loss(noise_pred, noise) + + # Record log + self.log("train_loss", loss, prog_bar=True) + return loss + + + def configure_optimizers(self): + trainable_modules = filter(lambda p: p.requires_grad, self.pipe.unet.parameters()) + optimizer = torch.optim.AdamW(trainable_modules, lr=self.learning_rate) + return optimizer + + + def on_save_checkpoint(self, checkpoint): + checkpoint.clear() + trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.pipe.unet.named_parameters())) + trainable_param_names = set([named_param[0] for named_param in trainable_param_names]) + state_dict = self.pipe.unet.state_dict() + for name, param in state_dict.items(): + if name in trainable_param_names: + checkpoint[name] = param + + + +def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--pretrained_unet_path", + type=str, + default=None, + required=True, + help="Path to pretrained model (UNet). For example, `models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors`.", + ) + parser.add_argument( + "--pretrained_text_encoder_path", + type=str, + default=None, + required=True, + help="Path to pretrained model (Text Encoder). For example, `models/kolors/Kolors/text_encoder`.", + ) + parser.add_argument( + "--pretrained_fp16_vae_path", + type=str, + default=None, + required=True, + help="Path to pretrained model (VAE). For example, `models/kolors/Kolors/sdxl-vae-fp16-fix/diffusion_pytorch_model.safetensors`.", + ) + parser.add_argument( + "--dataset_path", + type=str, + default=None, + required=True, + help="The path of the Dataset.", + ) + parser.add_argument( + "--output_path", + type=str, + default="./", + help="Path to save the model.", + ) + parser.add_argument( + "--steps_per_epoch", + type=int, + default=500, + help="Number of steps per epoch.", + ) + parser.add_argument( + "--height", + type=int, + default=1024, + help="Image height.", + ) + parser.add_argument( + "--width", + type=int, + default=1024, + help="Image width.", + ) + parser.add_argument( + "--center_crop", + default=False, + action="store_true", + help=( + "Whether to center crop the input images to the resolution. If not set, the images will be randomly" + " cropped. The images will be resized to the resolution first before cropping." + ), + ) + parser.add_argument( + "--random_flip", + default=False, + action="store_true", + help="Whether to randomly flip images horizontally", + ) + parser.add_argument( + "--batch_size", + type=int, + default=1, + help="Batch size (per device) for the training dataloader.", + ) + parser.add_argument( + "--dataloader_num_workers", + type=int, + default=0, + help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", + ) + parser.add_argument( + "--precision", + type=str, + default="16-mixed", + choices=["32", "16", "16-mixed"], + help="Training precision", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=1e-4, + help="Learning rate.", + ) + parser.add_argument( + "--lora_rank", + type=int, + default=4, + help="The dimension of the LoRA update matrices.", + ) + parser.add_argument( + "--lora_alpha", + type=float, + default=4.0, + help="The weight of the LoRA update matrices.", + ) + parser.add_argument( + "--use_gradient_checkpointing", + default=False, + action="store_true", + help="Whether to use gradient checkpointing.", + ) + parser.add_argument( + "--accumulate_grad_batches", + type=int, + default=1, + help="The number of batches in gradient accumulation.", + ) + parser.add_argument( + "--training_strategy", + type=str, + default="auto", + choices=["auto", "deepspeed_stage_1", "deepspeed_stage_2", "deepspeed_stage_3"], + help="Training strategy", + ) + parser.add_argument( + "--max_epochs", + type=int, + default=1, + help="Number of epochs.", + ) + args = parser.parse_args() + return args + + + +if __name__ == '__main__': + # args + args = parse_args() + + # dataset and data loader + dataset = TextImageDataset( + args.dataset_path, + steps_per_epoch=args.steps_per_epoch * args.batch_size, + height=args.height, + width=args.width, + center_crop=args.center_crop, + random_flip=args.random_flip + ) + train_loader = torch.utils.data.DataLoader( + dataset, + shuffle=True, + batch_size=args.batch_size, + num_workers=args.dataloader_num_workers + ) + + # model + model = LightningModel( + args.pretrained_unet_path, + args.pretrained_text_encoder_path, + args.pretrained_fp16_vae_path, + torch_dtype=torch.float32 if args.precision == "32" else torch.float16, + learning_rate=args.learning_rate, + lora_rank=args.lora_rank, + lora_alpha=args.lora_alpha, + use_gradient_checkpointing=args.use_gradient_checkpointing + ) + + # train + trainer = pl.Trainer( + max_epochs=args.max_epochs, + accelerator="gpu", + devices="auto", + precision=args.precision, + strategy=args.training_strategy, + default_root_dir=args.output_path, + accumulate_grad_batches=args.accumulate_grad_batches, + callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)] + ) + trainer.fit(model=model, train_dataloaders=train_loader) diff --git a/examples/train/stable_diffusion_3/README.md b/examples/train/stable_diffusion_3/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1163e5916173bff2847ce76d57ab41459a2fdeaa --- /dev/null +++ b/examples/train/stable_diffusion_3/README.md @@ -0,0 +1,160 @@ +# Stable Diffusion 3 + +Stable Diffusion 3 is a powerful text-to-image model. We provide training scripts here. + +## Download models + +Only one file is required in the training script. You can use [`sd3_medium_incl_clips.safetensors`](https://huggingface.co/stabilityai/stable-diffusion-3-medium/resolve/main/sd3_medium_incl_clips.safetensors) (without T5 encoder) or [`sd3_medium_incl_clips_t5xxlfp16.safetensors`](https://huggingface.co/stabilityai/stable-diffusion-3-medium/resolve/main/sd3_medium_incl_clips_t5xxlfp16.safetensors) (with T5 encoder). + +``` +models/stable_diffusion_3/ +├── Put Stable Diffusion 3 checkpoints here.txt +├── sd3_medium_incl_clips.safetensors +└── sd3_medium_incl_clips_t5xxlfp16.safetensors +``` + +You can use the following code to download these files: + +```python +from diffsynth import download_models + +download_models(["StableDiffusion3", "StableDiffusion3_without_T5"]) +``` + +## Train + +### Install training dependency + +``` +pip install peft lightning pandas torchvision +``` + +### Prepare your dataset + +We provide an example dataset [here](https://modelscope.cn/datasets/buptwq/lora-stable-diffusion-finetune/files). You need to manage the training images as follows: + +``` +data/dog/ +└── train + ├── 00.jpg + ├── 01.jpg + ├── 02.jpg + ├── 03.jpg + ├── 04.jpg + └── metadata.csv +``` + +`metadata.csv`: + +``` +file_name,text +00.jpg,a dog +01.jpg,a dog +02.jpg,a dog +03.jpg,a dog +04.jpg,a dog +``` + +### Train a LoRA model + +We provide a training script `train_sd3_lora.py`. Before you run this training script, please copy it to the root directory of this project. + +We recommand to enable gradient checkpointing. 10GB VRAM is enough if you train LoRA without the T5 encoder (use `sd3_medium_incl_clips.safetensors`), and 19GB VRAM is required if you enable the T5 encoder (use `sd3_medium_incl_clips_t5xxlfp16.safetensors`). + +``` +CUDA_VISIBLE_DEVICES="0" python train_sd3_lora.py \ + --pretrained_path models/stable_diffusion_3/sd3_medium_incl_clips.safetensors \ + --dataset_path data/dog \ + --output_path ./models \ + --max_epochs 1 \ + --center_crop \ + --use_gradient_checkpointing +``` + +Optional arguments: +``` + -h, --help show this help message and exit + --pretrained_path PRETRAINED_PATH + Path to pretrained model. For example, `models/stable_diffusion_3/sd3_medium_incl_clips.safetensors` or `models/stable_diffusion_3/sd3_medium_incl_clips_t5xxlfp16.safetensors`. + --dataset_path DATASET_PATH + The path of the Dataset. + --output_path OUTPUT_PATH + Path to save the model. + --steps_per_epoch STEPS_PER_EPOCH + Number of steps per epoch. + --height HEIGHT Image height. + --width WIDTH Image width. + --center_crop Whether to center crop the input images to the resolution. If not set, the images will be randomly cropped. The images will be resized to the resolution first before cropping. + --random_flip Whether to randomly flip images horizontally + --batch_size BATCH_SIZE + Batch size (per device) for the training dataloader. + --dataloader_num_workers DATALOADER_NUM_WORKERS + Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process. + --precision {32,16,16-mixed} + Training precision + --learning_rate LEARNING_RATE + Learning rate. + --lora_rank LORA_RANK + The dimension of the LoRA update matrices. + --lora_alpha LORA_ALPHA + The weight of the LoRA update matrices. + --use_gradient_checkpointing + Whether to use gradient checkpointing. + --accumulate_grad_batches ACCUMULATE_GRAD_BATCHES + The number of batches in gradient accumulation. + --training_strategy {auto,deepspeed_stage_1,deepspeed_stage_2,deepspeed_stage_3} + Training strategy + --max_epochs MAX_EPOCHS + Number of epochs. +``` + +### Inference with your own LoRA model + +After training, you can use your own LoRA model to generate new images. Here are some examples. + +```python +from diffsynth import ModelManager, SD3ImagePipeline +import torch +from peft import LoraConfig, inject_adapter_in_model + + +def load_lora(dit, lora_rank, lora_alpha, lora_path): + lora_config = LoraConfig( + r=lora_rank, + lora_alpha=lora_alpha, + init_lora_weights="gaussian", + target_modules=["a_to_qkv", "b_to_qkv"], + ) + dit = inject_adapter_in_model(lora_config, dit) + state_dict = torch.load(lora_path, map_location="cpu") + dit.load_state_dict(state_dict, strict=False) + return dit + + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=["models/stable_diffusion_3/sd3_medium_incl_clips.safetensors"]) +pipe = SD3ImagePipeline.from_model_manager(model_manager) + + +# Generate an image with lora +pipe.dit = load_lora( + pipe.dit, + lora_rank=4, lora_alpha=4.0, # The two parameters should be consistent with those in your training script. + lora_path="path/to/your/lora/model/lightning_logs/version_x/checkpoints/epoch=x-step=xxx.ckpt" +) +torch.manual_seed(0) +image = pipe( + prompt="a dog is jumping, flowers around the dog, the background is mountains and clouds", + negative_prompt="bad quality, poor quality, doll, disfigured, jpg, toy, bad anatomy, missing limbs, missing fingers, 3d, cgi, extra tails", + cfg_scale=7.5, + num_inference_steps=100, width=1024, height=1024, +) +image.save("image_with_lora.jpg") +``` + +Prompt: a dog is jumping, flowers around the dog, the background is mountains and clouds + +|Without LoRA|With LoRA| +|-|-| +|![image_without_lora](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/ddb834a5-6366-412b-93dc-6d957230d66e)|![image_with_lora](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/8e7b2888-d874-4da4-a75b-11b6b214b9bf)| diff --git a/examples/train/stable_diffusion_3/train_sd3_lora.py b/examples/train/stable_diffusion_3/train_sd3_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..5f7b22a1e19145df8b5a94cfd9380521e37d0371 --- /dev/null +++ b/examples/train/stable_diffusion_3/train_sd3_lora.py @@ -0,0 +1,293 @@ +from diffsynth import ModelManager, SD3ImagePipeline +from peft import LoraConfig, inject_adapter_in_model +from torchvision import transforms +from PIL import Image +import lightning as pl +import pandas as pd +import torch, os, argparse +os.environ["TOKENIZERS_PARALLELISM"] = "True" + + + +class TextImageDataset(torch.utils.data.Dataset): + def __init__(self, dataset_path, steps_per_epoch=10000, height=1024, width=1024, center_crop=True, random_flip=False): + self.steps_per_epoch = steps_per_epoch + metadata = pd.read_csv(os.path.join(dataset_path, "train/metadata.csv")) + self.path = [os.path.join(dataset_path, "train", file_name) for file_name in metadata["file_name"]] + self.text = metadata["text"].to_list() + self.image_processor = transforms.Compose( + [ + transforms.Resize(max(height, width), interpolation=transforms.InterpolationMode.BILINEAR), + transforms.CenterCrop((height, width)) if center_crop else transforms.RandomCrop((height, width)), + transforms.RandomHorizontalFlip() if random_flip else transforms.Lambda(lambda x: x), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ] + ) + + + def __getitem__(self, index): + data_id = torch.randint(0, len(self.path), (1,))[0] + data_id = (data_id + index) % len(self.path) # For fixed seed. + text = self.text[data_id] + image = Image.open(self.path[data_id]).convert("RGB") + image = self.image_processor(image) + return {"text": text, "image": image} + + + def __len__(self): + return self.steps_per_epoch + + + +class LightningModel(pl.LightningModule): + def __init__(self, torch_dtype=torch.float16, learning_rate=1e-4, pretrained_weights=[], lora_rank=4, lora_alpha=4, use_gradient_checkpointing=True): + super().__init__() + + # Load models + model_manager = ModelManager(torch_dtype=torch_dtype, device=self.device) + model_manager.load_models(pretrained_weights) + self.pipe = SD3ImagePipeline.from_model_manager(model_manager) + + # Freeze parameters + self.pipe.text_encoder_1.requires_grad_(False) + self.pipe.text_encoder_2.requires_grad_(False) + if self.pipe.text_encoder_3 is not None: + self.pipe.text_encoder_3.requires_grad_(False) + self.pipe.dit.requires_grad_(False) + self.pipe.vae_decoder.requires_grad_(False) + self.pipe.vae_encoder.requires_grad_(False) + self.pipe.text_encoder_1.eval() + self.pipe.text_encoder_2.eval() + if self.pipe.text_encoder_3 is not None: + self.pipe.text_encoder_3.eval() + self.pipe.dit.train() + self.pipe.vae_decoder.eval() + self.pipe.vae_encoder.eval() + + # Add LoRA to DiT + lora_config = LoraConfig( + r=lora_rank, + lora_alpha=lora_alpha, + init_lora_weights="gaussian", + target_modules=["a_to_qkv", "b_to_qkv"], + ) + self.pipe.dit = inject_adapter_in_model(lora_config, self.pipe.dit) + for param in self.pipe.dit.parameters(): + # Upcast LoRA parameters into fp32 + if param.requires_grad: + param.data = param.to(torch.float32) + + # Set other parameters + self.learning_rate = learning_rate + self.use_gradient_checkpointing = use_gradient_checkpointing + self.pipe.scheduler.set_timesteps(1000) + + + def training_step(self, batch, batch_idx): + # Data + text, image = batch["text"], batch["image"] + + # Prepare input parameters + self.pipe.device = self.device + prompt_emb, pooled_prompt_emb = self.pipe.prompter.encode_prompt( + self.pipe.text_encoder_1, self.pipe.text_encoder_2, self.pipe.text_encoder_3, + text, device=self.device + ) + latents = self.pipe.vae_encoder(image.to(dtype=self.pipe.torch_dtype, device=self.device)) + noise = torch.randn_like(latents) + timestep_id = torch.randint(0, 1000, (1,)) + timestep = self.pipe.scheduler.timesteps[timestep_id].to(self.device) + noisy_latents = self.pipe.scheduler.add_noise(latents, noise, self.pipe.scheduler.timesteps[timestep_id]) + training_target = self.pipe.scheduler.training_target(latents, noise, timestep) + + # Compute loss + noise_pred = self.pipe.dit(noisy_latents, timestep, prompt_emb, pooled_prompt_emb, use_gradient_checkpointing=self.use_gradient_checkpointing) + loss = torch.nn.functional.mse_loss(noise_pred, training_target) + + # Record log + self.log("train_loss", loss, prog_bar=True) + return loss + + + def configure_optimizers(self): + trainable_modules = filter(lambda p: p.requires_grad, self.pipe.dit.parameters()) + optimizer = torch.optim.AdamW(trainable_modules, lr=self.learning_rate) + return optimizer + + + def on_save_checkpoint(self, checkpoint): + checkpoint.clear() + trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.pipe.dit.named_parameters())) + trainable_param_names = set([named_param[0] for named_param in trainable_param_names]) + state_dict = self.pipe.dit.state_dict() + for name, param in state_dict.items(): + if name in trainable_param_names: + checkpoint[name] = param + + + +def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--pretrained_path", + type=str, + default=None, + required=True, + help="Path to pretrained model. For example, `models/stable_diffusion_3/sd3_medium_incl_clips.safetensors` or `models/stable_diffusion_3/sd3_medium_incl_clips_t5xxlfp16.safetensors`.", + ) + parser.add_argument( + "--dataset_path", + type=str, + default=None, + required=True, + help="The path of the Dataset.", + ) + parser.add_argument( + "--output_path", + type=str, + default="./", + help="Path to save the model.", + ) + parser.add_argument( + "--steps_per_epoch", + type=int, + default=500, + help="Number of steps per epoch.", + ) + parser.add_argument( + "--height", + type=int, + default=1024, + help="Image height.", + ) + parser.add_argument( + "--width", + type=int, + default=1024, + help="Image width.", + ) + parser.add_argument( + "--center_crop", + default=False, + action="store_true", + help=( + "Whether to center crop the input images to the resolution. If not set, the images will be randomly" + " cropped. The images will be resized to the resolution first before cropping." + ), + ) + parser.add_argument( + "--random_flip", + default=False, + action="store_true", + help="Whether to randomly flip images horizontally", + ) + parser.add_argument( + "--batch_size", + type=int, + default=1, + help="Batch size (per device) for the training dataloader.", + ) + parser.add_argument( + "--dataloader_num_workers", + type=int, + default=0, + help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", + ) + parser.add_argument( + "--precision", + type=str, + default="16-mixed", + choices=["32", "16", "16-mixed"], + help="Training precision", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=1e-4, + help="Learning rate.", + ) + parser.add_argument( + "--lora_rank", + type=int, + default=4, + help="The dimension of the LoRA update matrices.", + ) + parser.add_argument( + "--lora_alpha", + type=float, + default=4.0, + help="The weight of the LoRA update matrices.", + ) + parser.add_argument( + "--use_gradient_checkpointing", + default=False, + action="store_true", + help="Whether to use gradient checkpointing.", + ) + parser.add_argument( + "--accumulate_grad_batches", + type=int, + default=1, + help="The number of batches in gradient accumulation.", + ) + parser.add_argument( + "--training_strategy", + type=str, + default="auto", + choices=["auto", "deepspeed_stage_1", "deepspeed_stage_2", "deepspeed_stage_3"], + help="Training strategy", + ) + parser.add_argument( + "--max_epochs", + type=int, + default=1, + help="Number of epochs.", + ) + args = parser.parse_args() + return args + + + +if __name__ == '__main__': + # args + args = parse_args() + + # dataset and data loader + dataset = TextImageDataset( + args.dataset_path, + steps_per_epoch=args.steps_per_epoch * args.batch_size, + height=args.height, + width=args.width, + center_crop=args.center_crop, + random_flip=args.random_flip + ) + train_loader = torch.utils.data.DataLoader( + dataset, + shuffle=True, + batch_size=args.batch_size, + num_workers=args.dataloader_num_workers + ) + + # model + model = LightningModel( + pretrained_weights=[args.pretrained_path], + torch_dtype=torch.float32 if args.precision == "32" else torch.float16, + learning_rate=args.learning_rate, + lora_rank=args.lora_rank, + lora_alpha=args.lora_alpha, + use_gradient_checkpointing=args.use_gradient_checkpointing + ) + + # train + trainer = pl.Trainer( + max_epochs=args.max_epochs, + accelerator="gpu", + devices="auto", + precision=args.precision, + strategy=args.training_strategy, + default_root_dir=args.output_path, + accumulate_grad_batches=args.accumulate_grad_batches, + callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)] + ) + trainer.fit(model=model, train_dataloaders=train_loader) diff --git a/examples/video_synthesis/README.md b/examples/video_synthesis/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f9802e69b926e2a18275a4722a1521d957f6fea9 --- /dev/null +++ b/examples/video_synthesis/README.md @@ -0,0 +1,9 @@ +# Text to Video + +In DiffSynth Studio, we can use AnimateDiff and SVD to generate videos. However, these models usually generate terrible contents. We do not recommend users to use these models, until a more powerful video model emerges. + +### Example 7: Text to Video + +Generate a video using a Stable Diffusion model and an AnimateDiff model. We can break the limitation of number of frames! See [sd_text_to_video.py](./sd_text_to_video.py). + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/8f556355-4079-4445-9b48-e9da77699437 diff --git a/examples/video_synthesis/sd_text_to_video.py b/examples/video_synthesis/sd_text_to_video.py new file mode 100644 index 0000000000000000000000000000000000000000..55446e580215244e635d31dc1b4379bc80d851f0 --- /dev/null +++ b/examples/video_synthesis/sd_text_to_video.py @@ -0,0 +1,47 @@ +from diffsynth import ModelManager, SDImagePipeline, SDVideoPipeline, save_video, download_models +from diffsynth.extensions.RIFE import RIFEInterpolater +import torch + + +# Download models (automatically) +# `models/stable_diffusion/dreamshaper_8.safetensors`: [link](https://civitai.com/api/download/models/128713?type=Model&format=SafeTensor&size=pruned&fp=fp16) +# `models/AnimateDiff/mm_sd_v15_v2.ckpt`: [link](https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15_v2.ckpt) +# `models/RIFE/flownet.pkl`: [link](https://drive.google.com/file/d/1APIzVeI-4ZZCEuIRE1m6WYfSCaOsi_7_/view?usp=sharing) +download_models(["DreamShaper_8", "AnimateDiff_v2", "RIFE"]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models([ + "models/stable_diffusion/dreamshaper_8.safetensors", + "models/AnimateDiff/mm_sd_v15_v2.ckpt", + "models/RIFE/flownet.pkl" +]) + +# Text -> Image +pipe_image = SDImagePipeline.from_model_manager(model_manager) +torch.manual_seed(0) +image = pipe_image( + prompt = "lightning storm, sea", + negative_prompt = "", + cfg_scale=7.5, + num_inference_steps=30, height=512, width=768, +) + +# Text + Image -> Video (6GB VRAM is enough!) +pipe = SDVideoPipeline.from_model_manager(model_manager) +output_video = pipe( + prompt = "lightning storm, sea", + negative_prompt = "", + cfg_scale=7.5, + num_frames=64, + num_inference_steps=10, height=512, width=768, + animatediff_batch_size=16, animatediff_stride=1, input_frames=[image]*64, denoising_strength=0.9, + vram_limit_level=0, +) + +# Video -> Video with high fps +interpolater = RIFEInterpolater.from_model_manager(model_manager) +output_video = interpolater.interpolate(output_video, num_iter=3) + +# Save images and video +save_video(output_video, "output_video.mp4", fps=120) diff --git a/examples/video_synthesis/sdxl_text_to_video.py b/examples/video_synthesis/sdxl_text_to_video.py new file mode 100644 index 0000000000000000000000000000000000000000..b96498a7554335f8a7bb1c3ec8e2edea1138c287 --- /dev/null +++ b/examples/video_synthesis/sdxl_text_to_video.py @@ -0,0 +1,28 @@ +from diffsynth import ModelManager, SDXLVideoPipeline, save_video, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion_xl/sd_xl_base_1.0.safetensors`: [link](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors) +# `models/AnimateDiff/mm_sdxl_v10_beta.ckpt`: [link](https://huggingface.co/guoyww/animatediff/resolve/main/mm_sdxl_v10_beta.ckpt) +download_models(["StableDiffusionXL_v1", "AnimateDiff_xl_beta"]) + +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models([ + "models/stable_diffusion_xl/sd_xl_base_1.0.safetensors", + "models/AnimateDiff/mm_sdxl_v10_beta.ckpt" +]) +pipe = SDXLVideoPipeline.from_model_manager(model_manager) + +prompt = "A panda standing on a surfboard in the ocean in sunset, 4k, high resolution.Realistic, Cinematic, high resolution" +negative_prompt = "" + +torch.manual_seed(0) +video = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=8.5, + height=1024, width=1024, num_frames=16, + num_inference_steps=100, +) +save_video(video, "output_video.mp4", fps=16) diff --git a/examples/video_synthesis/svd_text_to_video.py b/examples/video_synthesis/svd_text_to_video.py new file mode 100644 index 0000000000000000000000000000000000000000..ee91c92069ffb1bbc43c19d69e0bdd0d2ced612c --- /dev/null +++ b/examples/video_synthesis/svd_text_to_video.py @@ -0,0 +1,36 @@ +from diffsynth import save_video, SDXLImagePipeline, ModelManager, SVDVideoPipeline, download_models +from diffsynth import ModelManager +import torch + + +# Download models (automatically) +# `models/stable_diffusion_xl/sd_xl_base_1.0.safetensors`: [link](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors) +# `models/stable_video_diffusion/svd_xt.safetensors`: [link](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/svd_xt.safetensors) +download_models(["StableDiffusionXL_v1", "stable-video-diffusion-img2vid-xt"]) + +prompt = "cloud, wind" +torch.manual_seed(0) + +# 1. Text-to-image using SD-XL +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models(["models/stable_diffusion_xl/sd_xl_base_1.0.safetensors"]) +pipe = SDXLImagePipeline.from_model_manager(model_manager) +image = pipe( + prompt=prompt, + negative_prompt="", + cfg_scale=6, + height=1024, width=1024, num_inference_steps=50, +) +model_manager.to("cpu") + +# 2. Image-to-video using SVD +model_manager = ModelManager() +model_manager.load_models(["models/stable_video_diffusion/svd_xt.safetensors"]) +pipe = SVDVideoPipeline.from_model_manager(model_manager) +video = pipe( + input_image=image, + num_frames=25, fps=15, height=1024, width=1024, + motion_bucket_id=127, + num_inference_steps=50 +) +save_video(video, "output_video.mp4", fps=15) diff --git a/models/AnimateDiff/Put AnimateDiff ckpt files here.txt b/models/AnimateDiff/Put AnimateDiff ckpt files here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/AnimateDiff/mm_sd_v15_v2.ckpt b/models/AnimateDiff/mm_sd_v15_v2.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..e52e8a28920f998f42b8fa2bc0a277f42c6eb176 --- /dev/null +++ b/models/AnimateDiff/mm_sd_v15_v2.ckpt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:69ed0f5fef82b110aca51bcab73b21104242bc65d6ab4b8b2a2a94d31cad1bf0 +size 1817888431 diff --git a/models/Annotators/ControlNetHED.pth b/models/Annotators/ControlNetHED.pth new file mode 100644 index 0000000000000000000000000000000000000000..e0edbff99b09b7241441fb1f9f25187e0f1ff5c9 --- /dev/null +++ b/models/Annotators/ControlNetHED.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5ca93762ffd68a29fee1af9d495bf6aab80ae86f08905fb35472a083a4c7a8fa +size 29444406 diff --git a/models/Annotators/Put ControlNet annotators here.txt b/models/Annotators/Put ControlNet annotators here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/Annotators/dpt_hybrid-midas-501f0c75.pt b/models/Annotators/dpt_hybrid-midas-501f0c75.pt new file mode 100644 index 0000000000000000000000000000000000000000..a54fd8ca8d59181d9343d79eb3f6deb6c5319eba --- /dev/null +++ b/models/Annotators/dpt_hybrid-midas-501f0c75.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:501f0c75b3bca7daec6b3682c5054c09b366765aef6fa3a09d03a5cb4b230853 +size 492757791 diff --git a/models/Annotators/sk_model.pth b/models/Annotators/sk_model.pth new file mode 100644 index 0000000000000000000000000000000000000000..7cf07cb6b07cfb90850895d6db9c5816a736b097 --- /dev/null +++ b/models/Annotators/sk_model.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c686ced2a666b4850b4bb6ccf0748031c3eda9f822de73a34b8979970d90f0c6 +size 17173511 diff --git a/models/Annotators/sk_model2.pth b/models/Annotators/sk_model2.pth new file mode 100644 index 0000000000000000000000000000000000000000..20bdda49983cb7d896cb27060d5292b0b3879376 --- /dev/null +++ b/models/Annotators/sk_model2.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:30a534781061f34e83bb9406b4335da4ff2616c95d22a585c1245aa8363e74e0 +size 17173511 diff --git 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0000000000000000000000000000000000000000..65f19b20d0f0cf9e17987a33e2a54b931a056f41 --- /dev/null +++ b/models/ControlNet/control_v11p_sd15_softedge.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:20759ef71a109c4796e637d86eea8db492ec9772b6d5cb7a9afd077847b403f3 +size 1445235707 diff --git a/models/HunyuanDiT/Put Hunyuan DiT checkpoints here.txt b/models/HunyuanDiT/Put Hunyuan DiT checkpoints here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/IpAdapter/Put IP-Adapter checkpoints here.txt b/models/IpAdapter/Put IP-Adapter checkpoints here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/RIFE/Put RIFE models here.txt b/models/RIFE/Put RIFE models here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/lora/Put lora files here.txt b/models/lora/Put lora files here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/stable_diffusion/Put Stable Diffusion checkpoints here.txt b/models/stable_diffusion/Put Stable Diffusion checkpoints here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/stable_diffusion/aingdiffusion_v12.safetensors b/models/stable_diffusion/aingdiffusion_v12.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..791b580ebfbddf34063e61dc0aa93275d811516b --- /dev/null +++ b/models/stable_diffusion/aingdiffusion_v12.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:74161670206f7eb5e55267cd1f05d50c7b27bcb21d0a77bec1a192630280fe5a +size 2132625894 diff --git a/models/stable_diffusion_3/Put Stable Diffusion 3 checkpoints here.txt b/models/stable_diffusion_3/Put Stable Diffusion 3 checkpoints here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/stable_diffusion_xl/Put Stable Diffusion XL checkpoints here.txt b/models/stable_diffusion_xl/Put Stable Diffusion XL checkpoints here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/stable_diffusion_xl_turbo/Put Stable Diffusion XL Turbo checkpoints here.txt b/models/stable_diffusion_xl_turbo/Put Stable Diffusion XL Turbo checkpoints here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/stable_video_diffusion/Put Stable Video Diffusion checkpoints here.txt b/models/stable_video_diffusion/Put Stable Video Diffusion checkpoints here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/textual_inversion/Put Textual Inversion files here.txt b/models/textual_inversion/Put Textual Inversion files here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/textual_inversion/verybadimagenegative_v1.3.pt b/models/textual_inversion/verybadimagenegative_v1.3.pt new file mode 100644 index 0000000000000000000000000000000000000000..e5304ebcb481b120a35ad93e85c1ae5febacf638 --- /dev/null +++ b/models/textual_inversion/verybadimagenegative_v1.3.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d70463f87042e2b5951c303542f3e171cf49af9b7df53b2f20779493786eb143 +size 31659 diff --git a/models/translator/Put translator models here.txt b/models/translator/Put translator models here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/pages/1_Image_Creator.py b/pages/1_Image_Creator.py new file mode 100644 index 0000000000000000000000000000000000000000..d5a26decce5ce64e686722d98cd9359f7c5f7994 --- /dev/null +++ b/pages/1_Image_Creator.py @@ -0,0 +1,291 @@ +import torch, os, io +import numpy as np +from PIL import Image +import streamlit as st +st.set_page_config(layout="wide") +from streamlit_drawable_canvas import st_canvas +from diffsynth.models import ModelManager +from diffsynth.pipelines import SDImagePipeline, SDXLImagePipeline, SD3ImagePipeline, HunyuanDiTImagePipeline +from diffsynth.data.video import crop_and_resize + + +config = { + "Stable Diffusion": { + "model_folder": "models/stable_diffusion", + "pipeline_class": SDImagePipeline, + "fixed_parameters": {} + }, + "Stable Diffusion XL": { + "model_folder": "models/stable_diffusion_xl", + "pipeline_class": SDXLImagePipeline, + "fixed_parameters": {} + }, + "Stable Diffusion 3": { + "model_folder": "models/stable_diffusion_3", + "pipeline_class": SD3ImagePipeline, + "fixed_parameters": {} + }, + "Stable Diffusion XL Turbo": { + "model_folder": "models/stable_diffusion_xl_turbo", + "pipeline_class": SDXLImagePipeline, + "fixed_parameters": { + "negative_prompt": "", + "cfg_scale": 1.0, + "num_inference_steps": 1, + "height": 512, + "width": 512, + } + }, + "HunyuanDiT": { + "model_folder": "models/HunyuanDiT", + "pipeline_class": HunyuanDiTImagePipeline, + "fixed_parameters": { + "height": 1024, + "width": 1024, + } + }, +} + + +def load_model_list(model_type): + folder = config[model_type]["model_folder"] + file_list = [i for i in os.listdir(folder) if i.endswith(".safetensors")] + if model_type == "HunyuanDiT": + file_list += [i for i in os.listdir(folder) if os.path.isdir(os.path.join(folder, i))] + file_list = sorted(file_list) + return file_list + + +def release_model(): + if "model_manager" in st.session_state: + st.session_state["model_manager"].to("cpu") + del st.session_state["loaded_model_path"] + del st.session_state["model_manager"] + del st.session_state["pipeline"] + torch.cuda.empty_cache() + + +def load_model(model_type, model_path): + model_manager = ModelManager() + if model_type == "HunyuanDiT": + model_manager.load_models([ + os.path.join(model_path, "clip_text_encoder/pytorch_model.bin"), + os.path.join(model_path, "mt5/pytorch_model.bin"), + os.path.join(model_path, "model/pytorch_model_ema.pt"), + os.path.join(model_path, "sdxl-vae-fp16-fix/diffusion_pytorch_model.bin"), + ]) + else: + model_manager.load_model(model_path) + pipeline = config[model_type]["pipeline_class"].from_model_manager(model_manager) + st.session_state.loaded_model_path = model_path + st.session_state.model_manager = model_manager + st.session_state.pipeline = pipeline + return model_manager, pipeline + + +def use_output_image_as_input(update=True): + # Search for input image + output_image_id = 0 + selected_output_image = None + while True: + if f"use_output_as_input_{output_image_id}" not in st.session_state: + break + if st.session_state[f"use_output_as_input_{output_image_id}"]: + selected_output_image = st.session_state["output_images"][output_image_id] + break + output_image_id += 1 + if update and selected_output_image is not None: + st.session_state["input_image"] = selected_output_image + return selected_output_image is not None + + +def apply_stroke_to_image(stroke_image, image): + image = np.array(image.convert("RGB")).astype(np.float32) + height, width, _ = image.shape + + stroke_image = np.array(Image.fromarray(stroke_image).resize((width, height))).astype(np.float32) + weight = stroke_image[:, :, -1:] / 255 + stroke_image = stroke_image[:, :, :-1] + + image = stroke_image * weight + image * (1 - weight) + image = np.clip(image, 0, 255).astype(np.uint8) + image = Image.fromarray(image) + return image + + +@st.cache_data +def image2bits(image): + image_byte = io.BytesIO() + image.save(image_byte, format="PNG") + image_byte = image_byte.getvalue() + return image_byte + + +def show_output_image(image): + st.image(image, use_column_width="always") + st.button("Use it as input image", key=f"use_output_as_input_{image_id}") + st.download_button("Download", data=image2bits(image), file_name="image.png", mime="image/png", key=f"download_output_{image_id}") + + +column_input, column_output = st.columns(2) +with st.sidebar: + # Select a model + with st.expander("Model", expanded=True): + model_type = st.selectbox("Model type", [model_type_ for model_type_ in config]) + fixed_parameters = config[model_type]["fixed_parameters"] + model_path_list = ["None"] + load_model_list(model_type) + model_path = st.selectbox("Model path", model_path_list) + + # Load the model + if model_path == "None": + # No models are selected. Release VRAM. + st.markdown("No models are selected.") + release_model() + else: + # A model is selected. + model_path = os.path.join(config[model_type]["model_folder"], model_path) + if st.session_state.get("loaded_model_path", "") != model_path: + # The loaded model is not the selected model. Reload it. + st.markdown(f"Loading model at {model_path}.") + st.markdown("Please wait a moment...") + release_model() + model_manager, pipeline = load_model(model_type, model_path) + st.markdown("Done.") + else: + # The loaded model is not the selected model. Fetch it from `st.session_state`. + st.markdown(f"Loading model at {model_path}.") + st.markdown("Please wait a moment...") + model_manager, pipeline = st.session_state.model_manager, st.session_state.pipeline + st.markdown("Done.") + + # Show parameters + with st.expander("Prompt", expanded=True): + prompt = st.text_area("Positive prompt") + if "negative_prompt" in fixed_parameters: + negative_prompt = fixed_parameters["negative_prompt"] + else: + negative_prompt = st.text_area("Negative prompt") + if "cfg_scale" in fixed_parameters: + cfg_scale = fixed_parameters["cfg_scale"] + else: + cfg_scale = st.slider("Classifier-free guidance scale", min_value=1.0, max_value=10.0, value=7.5) + with st.expander("Image", expanded=True): + if "num_inference_steps" in fixed_parameters: + num_inference_steps = fixed_parameters["num_inference_steps"] + else: + num_inference_steps = st.slider("Inference steps", min_value=1, max_value=100, value=20) + if "height" in fixed_parameters: + height = fixed_parameters["height"] + else: + height = st.select_slider("Height", options=[256, 512, 768, 1024, 2048], value=512) + if "width" in fixed_parameters: + width = fixed_parameters["width"] + else: + width = st.select_slider("Width", options=[256, 512, 768, 1024, 2048], value=512) + num_images = st.number_input("Number of images", value=2) + use_fixed_seed = st.checkbox("Use fixed seed", value=False) + if use_fixed_seed: + seed = st.number_input("Random seed", min_value=0, max_value=10**9, step=1, value=0) + + # Other fixed parameters + denoising_strength = 1.0 + repetition = 1 + + +# Show input image +with column_input: + with st.expander("Input image (Optional)", expanded=True): + with st.container(border=True): + column_white_board, column_upload_image = st.columns([1, 2]) + with column_white_board: + create_white_board = st.button("Create white board") + delete_input_image = st.button("Delete input image") + with column_upload_image: + upload_image = st.file_uploader("Upload image", type=["png", "jpg"], key="upload_image") + + if upload_image is not None: + st.session_state["input_image"] = crop_and_resize(Image.open(upload_image), height, width) + elif create_white_board: + st.session_state["input_image"] = Image.fromarray(np.ones((height, width, 3), dtype=np.uint8) * 255) + else: + use_output_image_as_input() + + if delete_input_image and "input_image" in st.session_state: + del st.session_state.input_image + if delete_input_image and "upload_image" in st.session_state: + del st.session_state.upload_image + + input_image = st.session_state.get("input_image", None) + if input_image is not None: + with st.container(border=True): + column_drawing_mode, column_color_1, column_color_2 = st.columns([4, 1, 1]) + with column_drawing_mode: + drawing_mode = st.radio("Drawing tool", ["transform", "freedraw", "line", "rect"], horizontal=True, index=1) + with column_color_1: + stroke_color = st.color_picker("Stroke color") + with column_color_2: + fill_color = st.color_picker("Fill color") + stroke_width = st.slider("Stroke width", min_value=1, max_value=50, value=10) + with st.container(border=True): + denoising_strength = st.slider("Denoising strength", min_value=0.0, max_value=1.0, value=0.7) + repetition = st.slider("Repetition", min_value=1, max_value=8, value=1) + with st.container(border=True): + input_width, input_height = input_image.size + canvas_result = st_canvas( + fill_color=fill_color, + stroke_width=stroke_width, + stroke_color=stroke_color, + background_color="rgba(255, 255, 255, 0)", + background_image=input_image, + update_streamlit=True, + height=int(512 / input_width * input_height), + width=512, + drawing_mode=drawing_mode, + key="canvas" + ) + + +with column_output: + run_button = st.button("Generate image", type="primary") + auto_update = st.checkbox("Auto update", value=False) + num_image_columns = st.slider("Columns", min_value=1, max_value=8, value=2) + image_columns = st.columns(num_image_columns) + + # Run + if (run_button or auto_update) and model_path != "None": + + if input_image is not None: + input_image = input_image.resize((width, height)) + if canvas_result.image_data is not None: + input_image = apply_stroke_to_image(canvas_result.image_data, input_image) + + output_images = [] + for image_id in range(num_images * repetition): + if use_fixed_seed: + torch.manual_seed(seed + image_id) + else: + torch.manual_seed(np.random.randint(0, 10**9)) + if image_id >= num_images: + input_image = output_images[image_id - num_images] + with image_columns[image_id % num_image_columns]: + progress_bar_st = st.progress(0.0) + image = pipeline( + prompt, negative_prompt=negative_prompt, + cfg_scale=cfg_scale, num_inference_steps=num_inference_steps, + height=height, width=width, + input_image=input_image, denoising_strength=denoising_strength, + progress_bar_st=progress_bar_st + ) + output_images.append(image) + progress_bar_st.progress(1.0) + show_output_image(image) + st.session_state["output_images"] = output_images + + elif "output_images" in st.session_state: + for image_id in range(len(st.session_state.output_images)): + with image_columns[image_id % num_image_columns]: + image = st.session_state.output_images[image_id] + progress_bar = st.progress(1.0) + show_output_image(image) + if "upload_image" in st.session_state and use_output_image_as_input(update=False): + st.markdown("If you want to use an output image as input image, please delete the uploaded image manually.") diff --git a/pages/2_Video_Creator.py b/pages/2_Video_Creator.py new file mode 100644 index 0000000000000000000000000000000000000000..87480726d3d32f48a2648266ce84d14e81e42315 --- /dev/null +++ b/pages/2_Video_Creator.py @@ -0,0 +1,197 @@ +import streamlit as st +st.set_page_config(layout="wide") +from diffsynth import SDVideoPipelineRunner +import os +import numpy as np + + +def load_model_list(folder): + file_list = os.listdir(folder) + file_list = [i for i in file_list if i.endswith(".safetensors") or i.endswith(".pth") or i.endswith(".ckpt")] + file_list = sorted(file_list) + return file_list + + +def match_processor_id(model_name, supported_processor_id_list): + sorted_processor_id = [i[1] for i in sorted([(-len(i), i) for i in supported_processor_id_list])] + for processor_id in sorted_processor_id: + if processor_id in model_name: + return supported_processor_id_list.index(processor_id) + 1 + return 0 + + +config = { + "models": { + "model_list": [], + "textual_inversion_folder": "models/textual_inversion", + "device": "cuda", + "lora_alphas": [], + "controlnet_units": [] + }, + "data": { + "input_frames": None, + "controlnet_frames": [], + "output_folder": "output", + "fps": 60 + }, + "pipeline": { + "seed": 0, + "pipeline_inputs": {} + } +} + + +with st.expander("Model", expanded=True): + stable_diffusion_ckpt = st.selectbox("Stable Diffusion", ["None"] + load_model_list("models/stable_diffusion")) + if stable_diffusion_ckpt != "None": + config["models"]["model_list"].append(os.path.join("models/stable_diffusion", stable_diffusion_ckpt)) + animatediff_ckpt = st.selectbox("AnimateDiff", ["None"] + load_model_list("models/AnimateDiff")) + if animatediff_ckpt != "None": + config["models"]["model_list"].append(os.path.join("models/AnimateDiff", animatediff_ckpt)) + column_lora, column_lora_alpha = st.columns([2, 1]) + with column_lora: + sd_lora_ckpt = st.selectbox("LoRA", ["None"] + load_model_list("models/lora")) + with column_lora_alpha: + lora_alpha = st.slider("LoRA Alpha", min_value=-4.0, max_value=4.0, value=1.0, step=0.1) + if sd_lora_ckpt != "None": + config["models"]["model_list"].append(os.path.join("models/lora", sd_lora_ckpt)) + config["models"]["lora_alphas"].append(lora_alpha) + + +with st.expander("Data", expanded=True): + with st.container(border=True): + input_video = st.text_input("Input Video File Path (e.g., data/your_video.mp4)", value="") + column_height, column_width, column_start_frame_index, column_end_frame_index = st.columns([2, 2, 1, 1]) + with column_height: + height = st.select_slider("Height", options=[256, 512, 768, 1024, 1536, 2048], value=1024) + with column_width: + width = st.select_slider("Width", options=[256, 512, 768, 1024, 1536, 2048], value=1024) + with column_start_frame_index: + start_frame_id = st.number_input("Start Frame id", value=0) + with column_end_frame_index: + end_frame_id = st.number_input("End Frame id", value=16) + if input_video != "": + config["data"]["input_frames"] = { + "video_file": input_video, + "image_folder": None, + "height": height, + "width": width, + "start_frame_id": start_frame_id, + "end_frame_id": end_frame_id + } + with st.container(border=True): + output_video = st.text_input("Output Video File Path (e.g., data/a_folder_to_save_something)", value="output") + fps = st.number_input("FPS", value=60) + config["data"]["output_folder"] = output_video + config["data"]["fps"] = fps + + +with st.expander("ControlNet Units", expanded=True): + supported_processor_id_list = ["canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "tile"] + controlnet_units = st.tabs(["ControlNet Unit 0", "ControlNet Unit 1", "ControlNet Unit 2"]) + for controlnet_id in range(len(controlnet_units)): + with controlnet_units[controlnet_id]: + controlnet_ckpt = st.selectbox("ControlNet", ["None"] + load_model_list("models/ControlNet"), + key=f"controlnet_ckpt_{controlnet_id}") + processor_id = st.selectbox("Processor", ["None"] + supported_processor_id_list, + index=match_processor_id(controlnet_ckpt, supported_processor_id_list), + disabled=controlnet_ckpt == "None", key=f"processor_id_{controlnet_id}") + controlnet_scale = st.slider("Scale", min_value=0.0, max_value=1.0, step=0.01, value=0.5, + disabled=controlnet_ckpt == "None", key=f"controlnet_scale_{controlnet_id}") + use_input_video_as_controlnet_input = st.checkbox("Use input video as ControlNet input", value=True, + disabled=controlnet_ckpt == "None", + key=f"use_input_video_as_controlnet_input_{controlnet_id}") + if not use_input_video_as_controlnet_input: + controlnet_input_video = st.text_input("ControlNet Input Video File Path", value="", + disabled=controlnet_ckpt == "None", key=f"controlnet_input_video_{controlnet_id}") + column_height, column_width, column_start_frame_index, column_end_frame_index = st.columns([2, 2, 1, 1]) + with column_height: + height = st.select_slider("Height", options=[256, 512, 768, 1024, 1536, 2048], value=1024, + disabled=controlnet_ckpt == "None", key=f"controlnet_height_{controlnet_id}") + with column_width: + width = st.select_slider("Width", options=[256, 512, 768, 1024, 1536, 2048], value=1024, + disabled=controlnet_ckpt == "None", key=f"controlnet_width_{controlnet_id}") + with column_start_frame_index: + start_frame_id = st.number_input("Start Frame id", value=0, + disabled=controlnet_ckpt == "None", key=f"controlnet_start_frame_id_{controlnet_id}") + with column_end_frame_index: + end_frame_id = st.number_input("End Frame id", value=16, + disabled=controlnet_ckpt == "None", key=f"controlnet_end_frame_id_{controlnet_id}") + if input_video != "": + config["data"]["input_video"] = { + "video_file": input_video, + "image_folder": None, + "height": height, + "width": width, + "start_frame_id": start_frame_id, + "end_frame_id": end_frame_id + } + if controlnet_ckpt != "None": + config["models"]["model_list"].append(os.path.join("models/ControlNet", controlnet_ckpt)) + config["models"]["controlnet_units"].append({ + "processor_id": processor_id, + "model_path": os.path.join("models/ControlNet", controlnet_ckpt), + "scale": controlnet_scale, + }) + if use_input_video_as_controlnet_input: + config["data"]["controlnet_frames"].append(config["data"]["input_frames"]) + else: + config["data"]["controlnet_frames"].append({ + "video_file": input_video, + "image_folder": None, + "height": height, + "width": width, + "start_frame_id": start_frame_id, + "end_frame_id": end_frame_id + }) + + +with st.container(border=True): + with st.expander("Seed", expanded=True): + use_fixed_seed = st.checkbox("Use fixed seed", value=False) + if use_fixed_seed: + seed = st.number_input("Random seed", min_value=0, max_value=10**9, step=1, value=0) + else: + seed = np.random.randint(0, 10**9) + with st.expander("Textual Guidance", expanded=True): + prompt = st.text_area("Positive prompt") + negative_prompt = st.text_area("Negative prompt") + column_cfg_scale, column_clip_skip = st.columns(2) + with column_cfg_scale: + cfg_scale = st.slider("Classifier-free guidance scale", min_value=1.0, max_value=10.0, value=7.0) + with column_clip_skip: + clip_skip = st.slider("Clip Skip", min_value=1, max_value=4, value=1) + with st.expander("Denoising", expanded=True): + column_num_inference_steps, column_denoising_strength = st.columns(2) + with column_num_inference_steps: + num_inference_steps = st.slider("Inference steps", min_value=1, max_value=100, value=10) + with column_denoising_strength: + denoising_strength = st.slider("Denoising strength", min_value=0.0, max_value=1.0, value=1.0) + with st.expander("Efficiency", expanded=False): + animatediff_batch_size = st.slider("Animatediff batch size (sliding window size)", min_value=1, max_value=32, value=16, step=1) + animatediff_stride = st.slider("Animatediff stride", + min_value=1, + max_value=max(2, animatediff_batch_size), + value=max(1, animatediff_batch_size // 2), + step=1) + unet_batch_size = st.slider("UNet batch size", min_value=1, max_value=32, value=1, step=1) + controlnet_batch_size = st.slider("ControlNet batch size", min_value=1, max_value=32, value=1, step=1) + cross_frame_attention = st.checkbox("Enable Cross-Frame Attention", value=False) + config["pipeline"]["seed"] = seed + config["pipeline"]["pipeline_inputs"] = { + "prompt": prompt, + "negative_prompt": negative_prompt, + "cfg_scale": cfg_scale, + "clip_skip": clip_skip, + "denoising_strength": denoising_strength, + "num_inference_steps": num_inference_steps, + "animatediff_batch_size": animatediff_batch_size, + "animatediff_stride": animatediff_stride, + "unet_batch_size": unet_batch_size, + "controlnet_batch_size": controlnet_batch_size, + "cross_frame_attention": cross_frame_attention, + } + +run_button = st.button("☢️Run☢️", type="primary") +if run_button: + SDVideoPipelineRunner(in_streamlit=True).run(config) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..4934741fbda151c766a6389d042ea189c5896a58 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,13 @@ +torch>=2.0.0 +cupy-cuda12x +pip +transformers +controlnet-aux==0.0.7 +streamlit +streamlit-drawable-canvas +imageio +imageio[ffmpeg] +safetensors +einops +sentencepiece +modelscope diff --git a/setup.py b/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..1f70f2a6f5a24cb17fcbdd0d90f2625824d9412e --- /dev/null +++ b/setup.py @@ -0,0 +1,29 @@ +import os +from setuptools import setup, find_packages +import pkg_resources + +# Path to the requirements file +requirements_path = os.path.join(os.path.dirname(__file__), "requirements.txt") + +# Read the requirements from the requirements file +if os.path.exists(requirements_path): + with open(requirements_path, 'r') as f: + install_requires = [str(r) for r in pkg_resources.parse_requirements(f)] +else: + install_requires = [] + +setup( + name="diffsynth", + version="1.0.0", + description="Enjoy the magic of Diffusion models!", + author="Artiprocher", + packages=find_packages(), + install_requires=install_requires, + include_package_data=True, + classifiers=[ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: Apache-2.0 License", + "Operating System :: OS Independent", + ], + python_requires='>=3.6', +)