# Copyright 2024 NVIDIA CORPORATION & AFFILIATES # # 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. # # SPDX-License-Identifier: Apache-2.0 # This file is modified from https://github.com/PixArt-alpha/PixArt-sigma import os import random import numpy as np import torch from torchvision import transforms as T from torchvision.transforms.functional import InterpolationMode from tqdm import tqdm from diffusion.data.builder import DATASETS from diffusion.data.datasets.sana_data import SanaWebDataset from diffusion.data.datasets.utils import * from diffusion.data.wids import lru_json_load def get_closest_ratio(height: float, width: float, ratios: dict): aspect_ratio = height / width closest_ratio = min(ratios.keys(), key=lambda ratio: abs(float(ratio) - aspect_ratio)) return ratios[closest_ratio], float(closest_ratio) @DATASETS.register_module() class SanaWebDatasetMS(SanaWebDataset): def __init__( self, data_dir="", meta_path=None, cache_dir="/cache/data/sana-webds-meta", max_shards_to_load=None, transform=None, resolution=256, sample_subset=None, load_vae_feat=False, load_text_feat=False, input_size=32, patch_size=2, max_length=300, config=None, caption_proportion=None, sort_dataset=False, num_replicas=None, external_caption_suffixes=None, external_clipscore_suffixes=None, clip_thr=0.0, clip_thr_temperature=1.0, vae_downsample_rate=32, **kwargs, ): super().__init__( data_dir=data_dir, meta_path=meta_path, cache_dir=cache_dir, max_shards_to_load=max_shards_to_load, transform=transform, resolution=resolution, sample_subset=sample_subset, load_vae_feat=load_vae_feat, load_text_feat=load_text_feat, input_size=input_size, patch_size=patch_size, max_length=max_length, config=config, caption_proportion=caption_proportion, sort_dataset=sort_dataset, num_replicas=num_replicas, external_caption_suffixes=external_caption_suffixes, external_clipscore_suffixes=external_clipscore_suffixes, clip_thr=clip_thr, clip_thr_temperature=clip_thr_temperature, vae_downsample_rate=32, **kwargs, ) self.base_size = int(kwargs["aspect_ratio_type"].split("_")[-1]) self.aspect_ratio = eval(kwargs.pop("aspect_ratio_type")) # base aspect ratio self.ratio_index = {} self.ratio_nums = {} self.interpolate_model = InterpolationMode.BICUBIC self.interpolate_model = ( InterpolationMode.BICUBIC if self.aspect_ratio not in [ASPECT_RATIO_2048, ASPECT_RATIO_2880] else InterpolationMode.LANCZOS ) for k, v in self.aspect_ratio.items(): self.ratio_index[float(k)] = [] self.ratio_nums[float(k)] = 0 self.vae_downsample_rate = vae_downsample_rate def __getitem__(self, idx): for _ in range(10): try: data = self.getdata(idx) return data except Exception as e: print(f"Error details: {str(e)}") idx = random.choice(self.ratio_index[self.closest_ratio]) raise RuntimeError("Too many bad data.") def getdata(self, idx): data = self.dataset[idx] info = data[".json"] self.key = data["__key__"] dataindex_info = { "index": data["__index__"], "shard": "/".join(data["__shard__"].rsplit("/", 2)[-2:]), "shardindex": data["__shardindex__"], } # external json file for suffix in self.external_caption_suffixes: caption_json_path = data["__shard__"].replace(".tar", f"{suffix}.json") if os.path.exists(caption_json_path): try: caption_json = lru_json_load(caption_json_path) except: caption_json = {} if self.key in caption_json: info.update(caption_json[self.key]) data_info = {} ori_h, ori_w = info["height"], info["width"] # Calculate the closest aspect ratio and resize & crop image[w, h] closest_size, closest_ratio = get_closest_ratio(ori_h, ori_w, self.aspect_ratio) closest_size = list(map(lambda x: int(x), closest_size)) self.closest_ratio = closest_ratio data_info["img_hw"] = torch.tensor([ori_h, ori_w], dtype=torch.float32) data_info["aspect_ratio"] = closest_ratio caption_type, caption_clipscore = self.weighted_sample_clipscore(data, info) caption_type = caption_type if caption_type in info else self.default_prompt txt_fea = "" if info[caption_type] is None else info[caption_type] if self.load_vae_feat: img = data[".npy"] if len(img.shape) == 4 and img.shape[0] == 1: img = img[0] h, w = (img.shape[1], img.shape[2]) assert h == int(closest_size[0] // self.vae_downsample_rate) and w == int( closest_size[1] // self.vae_downsample_rate ), f"h: {h}, w: {w}, ori_hw: {closest_size}, data_info: {dataindex_info}" else: img = data[".png"] if ".png" in data else data[".jpg"] if closest_size[0] / ori_h > closest_size[1] / ori_w: resize_size = closest_size[0], int(ori_w * closest_size[0] / ori_h) else: resize_size = int(ori_h * closest_size[1] / ori_w), closest_size[1] self.transform = T.Compose( [ T.Lambda(lambda img: img.convert("RGB")), T.Resize(resize_size, interpolation=self.interpolate_model), # Image.BICUBIC T.CenterCrop(closest_size), T.ToTensor(), T.Normalize([0.5], [0.5]), ] ) if idx not in self.ratio_index[closest_ratio]: self.ratio_index[closest_ratio].append(idx) if self.transform: img = self.transform(img) attention_mask = torch.ones(1, 1, self.max_length, dtype=torch.int16) # 1x1xT if self.load_text_feat: npz_path = f"{self.key}.npz" txt_info = np.load(npz_path) txt_fea = torch.from_numpy(txt_info["caption_feature"]) # 1xTx4096 if "attention_mask" in txt_info: attention_mask = torch.from_numpy(txt_info["attention_mask"])[None] # make sure the feature length are the same if txt_fea.shape[1] != self.max_length: txt_fea = torch.cat([txt_fea, txt_fea[:, -1:].repeat(1, self.max_length - txt_fea.shape[1], 1)], dim=1) attention_mask = torch.cat( [attention_mask, torch.zeros(1, 1, self.max_length - attention_mask.shape[-1])], dim=-1 ) return ( img, txt_fea, attention_mask.to(torch.int16), data_info, idx, caption_type, dataindex_info, str(caption_clipscore), ) def __len__(self): return len(self.dataset) @DATASETS.register_module() class DummyDatasetMS(SanaWebDatasetMS): def __init__(self, **kwargs): self.base_size = int(kwargs["aspect_ratio_type"].split("_")[-1]) self.aspect_ratio = eval(kwargs.pop("aspect_ratio_type")) # base aspect ratio self.ratio_index = {} self.ratio_nums = {} self.interpolate_model = InterpolationMode.BICUBIC self.interpolate_model = ( InterpolationMode.BICUBIC if self.aspect_ratio not in [ASPECT_RATIO_2048, ASPECT_RATIO_2880] else InterpolationMode.LANCZOS ) for k, v in self.aspect_ratio.items(): self.ratio_index[float(k)] = [] self.ratio_nums[float(k)] = 0 self.ori_imgs_nums = 1_000_000 self.height = 384 self.width = 672 def __getitem__(self, idx): img = torch.randn((3, self.height, self.width)) txt_fea = "The image depicts a young woman standing in the middle of a street, leaning against a silver car. She is dressed in a stylish outfit consisting of a blue blouse and black pants. Her hair is long and dark, and she is looking directly at the camera with a confident expression. The street is lined with colorful buildings, and the trees have autumn leaves, suggesting the season is fall. The lighting is warm, with sunlight casting long shadows on the street. There are a few people in the background, and the overall atmosphere is vibrant and lively." attention_mask = torch.ones(1, 1, 300, dtype=torch.int16) # 1x1xT data_info = {"img_hw": torch.tensor([816.0, 1456.0]), "aspect_ratio": 0.57} idx = 2500 caption_type = self.default_prompt dataindex_info = {"index": 2500, "shard": "data_for_test_after_change/00000000.tar", "shardindex": 2500} return img, txt_fea, attention_mask, data_info, idx, caption_type, dataindex_info def __len__(self): return self.ori_imgs_nums def get_data_info(self, idx): return {"height": self.height, "width": self.width, "version": "1.0", "key": "dummpy_key"} if __name__ == "__main__": from torch.utils.data import DataLoader from diffusion.data.datasets.utils import ASPECT_RATIO_1024 from diffusion.data.transforms import get_transform image_size = 256 transform = get_transform("default_train", image_size) data_dir = ["data/debug_data_train/debug_data"] for data_path in data_dir: train_dataset = SanaWebDatasetMS(data_dir=data_path, resolution=image_size, transform=transform, max_length=300) dataloader = DataLoader(train_dataset, batch_size=1, shuffle=False, num_workers=4) for data in tqdm(dataloader): break print(dataloader.dataset.index_info)