Delete ckpt/app.py

ckpt/app.py

@@ -1,399 +0,0 @@
-import sys
-from PIL import Image
-import gradio as gr
-import numpy as np
-import cv2
-from modelscope.outputs import OutputKeys
-from modelscope.pipelines import pipeline
-from modelscope.utils.constant import Tasks
-from dressing_sd.pipelines.pipeline_sd import PipIpaControlNet
-from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
-
-from torchvision import transforms
-import cv2
-from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
-import diffusers
-
-from transformers import CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
-from adapter.attention_processor import CacheAttnProcessor2_0, RefSAttnProcessor2_0, RefLoraSAttnProcessor2_0, LoRAIPAttnProcessor2_0
-from diffusers import ControlNetModel, UNet2DConditionModel, \
-    AutoencoderKL, DDIMScheduler
-from adapter.resampler import Resampler
-
-from transformers import (
-    CLIPImageProcessor,
-    CLIPVisionModelWithProjection,
-    CLIPTextModel,
-    CLIPTextModelWithProjection,
-)
-from diffusers import DDPMScheduler, AutoencoderKL, UniPCMultistepScheduler
-from typing import List
-
-import torch
-
-import argparse
-import os
-
-from controlnet_aux import OpenposeDetector
-from insightface.app import FaceAnalysis
-from insightface.utils import face_align
-
-
-# device = 'cuda:2' if torch.cuda.is_available() else 'cpu'
-
-parser = argparse.ArgumentParser(description='ReferenceAdapter diffusion')
-parser.add_argument('--if_resampler', type=bool, default=True)
-parser.add_argument('--if_ipa', type=bool, default=True)
-parser.add_argument('--if_control', type=bool, default=True)
-
-parser.add_argument('--pretrained_model_name_or_path',
-                    default="./ckpt/Realistic_Vision_V4.0_noVAE",
-                    type=str)
-parser.add_argument('--ip_ckpt',
-                    default="./ckpt/ip-adapter-faceid-plus_sd15.bin",
-                    type=str)
-parser.add_argument('--pretrained_image_encoder_path',
-                    default="./ckpt/image_encoder/",
-                    type=str)
-parser.add_argument('--pretrained_vae_model_path',
-                    default="./ckpt/sd-vae-ft-mse/",
-                    type=str)
-parser.add_argument('--model_ckpt',
-                    default="./ckpt/IMAGDressing-v1_512.pt",
-                    type=str)
-parser.add_argument('--output_path', type=str, default="./output_ipa_control_resampler")
-# parser.add_argument('--device', type=str, default="cuda:0")
-args = parser.parse_args()
-
-# svae path
-output_path = args.output_path
-
-if not os.path.exists(output_path):
-    os.makedirs(output_path)
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-args.device = device
-
-base_path = 'feishen29/IMAGDressing-v1'
-
-generator = torch.Generator(device=args.device).manual_seed(42)
-vae = AutoencoderKL.from_pretrained(args.pretrained_vae_model_path).to(dtype=torch.float16, device=args.device)
-tokenizer = CLIPTokenizer.from_pretrained("./ckpt/tokenizer")
-text_encoder = CLIPTextModel.from_pretrained("./ckpt/text_encoder").to(
-    dtype=torch.float16, device=args.device)
-image_encoder = CLIPVisionModelWithProjection.from_pretrained(args.pretrained_image_encoder_path).to(
-    dtype=torch.float16, device=args.device)
-unet = UNet2DConditionModel.from_pretrained("./ckpt/unet").to(
-    dtype=torch.float16,device=args.device)
-
-image_face_fusion = pipeline('face_fusion_torch', model='damo/cv_unet_face_fusion_torch', model_revision='v1.0.3')
-
-#face_model
-app = FaceAnalysis(providers=[('CUDAExecutionProvider', {"device_id": args.device})]) ##使用GPU:0, 默认使用buffalo_l就可以了
-app.prepare(ctx_id=0, det_size=(640, 640))
-
-# def ref proj weight
-image_proj = Resampler(
-    dim=unet.config.cross_attention_dim,
-    depth=4,
-    dim_head=64,
-    heads=12,
-    num_queries=16,
-    embedding_dim=image_encoder.config.hidden_size,
-    output_dim=unet.config.cross_attention_dim,
-    ff_mult=4
-)
-image_proj = image_proj.to(dtype=torch.float16, device=args.device)
-
-# set attention processor
-attn_procs = {}
-st = unet.state_dict()
-for name in unet.attn_processors.keys():
-    cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
-    if name.startswith("mid_block"):
-        hidden_size = unet.config.block_out_channels[-1]
-    elif name.startswith("up_blocks"):
-        block_id = int(name[len("up_blocks.")])
-        hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
-    elif name.startswith("down_blocks"):
-        block_id = int(name[len("down_blocks.")])
-        hidden_size = unet.config.block_out_channels[block_id]
-    # lora_rank = hidden_size // 2 # args.lora_rank
-    if cross_attention_dim is None:
-        attn_procs[name] = RefLoraSAttnProcessor2_0(name, hidden_size)
-    else:
-        attn_procs[name] = LoRAIPAttnProcessor2_0(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim)
-
-unet.set_attn_processor(attn_procs)
-adapter_modules = torch.nn.ModuleList(unet.attn_processors.values())
-adapter_modules = adapter_modules.to(dtype=torch.float16, device=args.device)
-del st
-
-ref_unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet").to(
-    dtype=torch.float16,
-    device=args.device)
-ref_unet.set_attn_processor(
-    {name: CacheAttnProcessor2_0() for name in ref_unet.attn_processors.keys()})  # set cache
-
-# weights load
-model_sd = torch.load(args.model_ckpt, map_location="cpu")["module"]
-
-ref_unet_dict = {}
-unet_dict = {}
-image_proj_dict = {}
-adapter_modules_dict = {}
-for k in model_sd.keys():
-    if k.startswith("ref_unet"):
-        ref_unet_dict[k.replace("ref_unet.", "")] = model_sd[k]
-    elif k.startswith("unet"):
-        unet_dict[k.replace("unet.", "")] = model_sd[k]
-    elif k.startswith("proj"):
-        image_proj_dict[k.replace("proj.", "")] = model_sd[k]
-    elif k.startswith("adapter_modules") and 'ref' in k:
-        adapter_modules_dict[k.replace("adapter_modules.", "")] = model_sd[k]
-    else:
-        print(k)
-
-ref_unet.load_state_dict(ref_unet_dict)
-image_proj.load_state_dict(image_proj_dict)
-adapter_modules.load_state_dict(adapter_modules_dict, strict=False)
-
-noise_scheduler = DDIMScheduler(
-    num_train_timesteps=1000,
-    beta_start=0.00085,
-    beta_end=0.012,
-    beta_schedule="scaled_linear",
-    clip_sample=False,
-    set_alpha_to_one=False,
-    steps_offset=1,
-)
-# noise_scheduler = UniPCMultistepScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler")
-
-control_net_openpose = ControlNetModel.from_pretrained(
-    "/home/sf/control_v11p_sd15_openpose",
-    torch_dtype=torch.float16).to(device=args.device)
-# pipe = PipIpaControlNet(unet=unet, reference_unet=ref_unet, vae=vae, tokenizer=tokenizer,
-#                         text_encoder=text_encoder, image_encoder=image_encoder,
-#                         ip_ckpt=args.ip_ckpt,
-#                         ImgProj=image_proj, controlnet=control_net_openpose,
-#                         scheduler=noise_scheduler,
-#                         safety_checker=StableDiffusionSafetyChecker,
-#                         feature_extractor=CLIPImageProcessor)
-
-img_transform = transforms.Compose([
-    transforms.Resize([640, 512], interpolation=transforms.InterpolationMode.BILINEAR),
-    transforms.ToTensor(),
-    transforms.Normalize([0.5], [0.5]),
-])
-
-openpose_model = OpenposeDetector.from_pretrained("/home/sf/ControlNet").to(args.device)
-
-def resize_img(input_image, max_side=640, min_side=512, size=None, 
-               pad_to_max_side=False, mode=Image.BILINEAR, base_pixel_number=64):
-    w, h = input_image.size
-    ratio = min_side / min(h, w)
-    w, h = round(ratio*w), round(ratio*h)
-    ratio = max_side / max(h, w)
-    input_image = input_image.resize([round(ratio*w), round(ratio*h)], mode)
-    w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number
-    h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number
-    input_image = input_image.resize([w_resize_new, h_resize_new], mode)
-    return input_image
-
-def tryon_process(garm_img, face_img, pose_img, prompt, cloth_guidance_scale, caption_guidance_scale,
-                  face_guidance_scale,self_guidance_scale, cross_guidance_scale,if_ipa, if_post, if_control, denoise_steps, seed=42):
-    # prompt = prompt + ', confident smile expression, fashion, best quality, amazing quality, very aesthetic'
-    if prompt is None:
-        prompt = "a photography of a model"
-    prompt = prompt + ', best quality, high quality'
-    print(prompt, cloth_guidance_scale, if_ipa, if_control, denoise_steps, seed)
-    clip_image_processor = CLIPImageProcessor()
-    # clothes_img = garm_img.convert("RGB")
-    if not garm_img:
-        raise gr.Error("请上传衣服 / Please upload garment")
-    clothes_img = resize_img(garm_img)
-    vae_clothes = img_transform(clothes_img).unsqueeze(0)
-    # print(vae_clothes.shape)
-    ref_clip_image = clip_image_processor(images=clothes_img, return_tensors="pt").pixel_values
-
-    if if_ipa:
-        # image = cv2.imread(face_img)
-        faces = app.get(face_img)
-        
-        if not faces:
-            raise gr.Error("人脸检测异常，尝试其他肖像 / Abnormal face detection. Try another portrait")
-        faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)
-        face_image = face_align.norm_crop(face_img, landmark=faces[0].kps, image_size=224) # you can also segment the face
-        
-        # face_img = face_image[:, :, ::-1]
-        # face_img = Image.fromarray(face_image.astype('uint8'))
-        # face_img.save('face.png')
-        
-        face_clip_image = clip_image_processor(images=face_image, return_tensors="pt").pixel_values
-    else:
-        faceid_embeds = None
-        face_clip_image = None
-
-    if if_control:
-        pose_img = openpose_model(pose_img.convert("RGB"))
-        # pose_img.save('pose.png')
-        pose_image = diffusers.utils.load_image(pose_img)
-    else:
-        pose_image = None
-    # print(if_ipa, if_control)
-    # pipe, generator = prepare_pipeline(args, if_ipa, if_control, unet, ref_unet, vae, tokenizer, text_encoder,
-    #                                    image_encoder, image_proj, control_net_openpose)
-
-    noise_scheduler = DDIMScheduler(
-        num_train_timesteps=1000,
-        beta_start=0.00085,
-        beta_end=0.012,
-        beta_schedule="scaled_linear",
-        clip_sample=False,
-        set_alpha_to_one=False,
-        steps_offset=1,
-    )
-    # noise_scheduler = UniPCMultistepScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler")
-    pipe = PipIpaControlNet(unet=unet, reference_unet=ref_unet, vae=vae, tokenizer=tokenizer,
-                            text_encoder=text_encoder, image_encoder=image_encoder,
-                            ip_ckpt=args.ip_ckpt,
-                            ImgProj=image_proj, controlnet=control_net_openpose,
-                            scheduler=noise_scheduler,
-                            safety_checker=StableDiffusionSafetyChecker,
-                            feature_extractor=CLIPImageProcessor)
-    output = pipe(
-        ref_image=vae_clothes,
-        prompt=prompt,
-        ref_clip_image=ref_clip_image,
-        pose_image=pose_image,
-        face_clip_image=face_clip_image,
-        faceid_embeds=faceid_embeds,
-        null_prompt='',
-        negative_prompt='bare, naked, nude, undressed, monochrome, lowres, bad anatomy, worst quality, low quality',
-        width=512,
-        height=640,
-        num_images_per_prompt=1,
-        guidance_scale=caption_guidance_scale,
-        image_scale=cloth_guidance_scale,
-        ipa_scale=face_guidance_scale,
-        s_lora_scale= self_guidance_scale,
-        c_lora_scale= cross_guidance_scale,
-        generator=generator,
-        num_inference_steps=denoise_steps,
-    ).images
-    
-    if if_post and if_ipa:
-        # 将 PIL 图像转换为 NumPy 数组
-        output_array = np.array(output[0])
-        # 将 RGB 图像转换为 BGR 图像
-        bgr_array = cv2.cvtColor(output_array, cv2.COLOR_RGB2BGR)
-        # 将 NumPy 数组转换为 PIL 图像
-        bgr_image = Image.fromarray(bgr_array)
-        result = image_face_fusion(dict(template=bgr_image, user=Image.fromarray(face_image.astype('uint8'))))
-        return result[OutputKeys.OUTPUT_IMG]
-    return output[0]
-
-example_path = os.path.dirname(__file__)
-
-garm_list = os.listdir(os.path.join(example_path, "cloth", 'cloth'))
-garm_list_path = [os.path.join(example_path, "cloth", 'cloth', garm) for garm in garm_list]
-
-face_list = os.listdir(os.path.join(example_path, "face", 'face'))
-face_list_path = [os.path.join(example_path, "face", 'face', face) for face in face_list]
-
-pose_list = os.listdir(os.path.join(example_path, "pose", 'pose'))
-pose_list_path = [os.path.join(example_path, "pose", 'pose', pose) for pose in pose_list]
-
-
-
-##default human
-
-
-image_blocks = gr.Blocks().queue()
-with image_blocks as demo:
-    gr.Markdown("## IMAGDressing-v1: Customizable Virtual Dressing 👕👔👚")
-    gr.Markdown(
-        "Customize your virtual look with ease—adjust your appearance, pose, and garment as you like<br>."
-        "If you enjoy this project, please check out the [source codes](https://github.com/muzishen/IMAGDressing) and [model](https://huggingface.co/feishen29/IMAGDressing). Do not hesitate to give us a star. Thank you!<br>"
-        "Your support fuels the development of new versions."
-    )
-    with gr.Row():
-        with gr.Column():
-            garm_img = gr.Image(label="Garment", sources='upload', type="pil")
-            example = gr.Examples(
-                inputs=garm_img,
-                examples_per_page=8,
-                examples=garm_list_path)
-
-        with gr.Column():
-            imgs = gr.Image(label="Face", sources='upload', type="numpy")
-          
-            with gr.Row():
-                is_checked_face = gr.Checkbox(label="Yes", info="Use face ", value=False)
-            example = gr.Examples(
-                inputs=imgs,
-                examples_per_page=10,
-                examples=face_list_path
-            )
-            with gr.Row():
-                is_checked_postprocess = gr.Checkbox(label="Yes", info="Use postprocess ", value=False)
-
-        with gr.Column():
-            pose_img = gr.Image(label="Pose", sources='upload', type="pil")
-            with gr.Row():
-                is_checked_pose = gr.Checkbox(label="Yes", info="Use pose ", value=False)
-
-            example = gr.Examples(
-                inputs=pose_img,
-                examples_per_page=8,
-                examples=pose_list_path)
-
-        # with gr.Column():
-        #     # image_out = gr.Image(label="Output", elem_id="output-img", height=400)
-        #     masked_img = gr.Image(label="Masked image output", elem_id="masked-img", show_share_button=False)
-        with gr.Column():
-            # image_out = gr.Image(label="Output", elem_id="output-img", height=400)
-            image_out = gr.Image(label="Output", elem_id="output-img", show_share_button=False)
-    # Add usage tips below the output image
-    gr.Markdown("""
-    ### Usage Tips
-    - **Upload Images**: Upload your desired garment, face, and pose images in the respective sections.
-    - **Select Options**: Use the checkboxes to include face and pose in the generated output.
-    - **View Output**: The resulting image will be displayed in the Output section.
-    - **Examples**: Click on example images to quickly load and test different configurations.
-    - **Advanced Settings**: Click on **Advanced Settings** to edit captions and adjust hyperparameters.
-    - **Feedback**: If you have any issues or suggestions, please let us know through the [GitHub repository](https://github.com/muzishen/IMAGDressing).
-    """)
-    with gr.Column():
-        try_button = gr.Button(value="Dressing")
-        with gr.Accordion(label="Advanced Settings", open=False):
-            with gr.Row(elem_id="prompt-container"):
-                with gr.Row():
-                    prompt = gr.Textbox(placeholder="Description of prompt ex) A beautiful woman dress Short Sleeve Round Neck T-shirts",value='A beautiful woman',
-                                        show_label=False, elem_id="prompt")
-                # with gr.Row():
-                #     neg_prompt = gr.Textbox(placeholder="Description of neg prompt ex) Short Sleeve Round Neck T-shirts",
-                #                         show_label=False, elem_id="neg_prompt")
-            with gr.Row():
-                cloth_guidance_scale = gr.Slider(label="Cloth guidance Scale", minimum=0.0, maximum=1.0, value=0.9, step=0.1,
-                                             visible=True)
-            with gr.Row():
-                caption_guidance_scale = gr.Slider(label="Prompt Guidance Scale", minimum=1, maximum=10., value=7.0, step=0.1,
-                                             visible=True)
-            with gr.Row():
-                face_guidance_scale = gr.Slider(label="Face Guidance Scale", minimum=0.0, maximum=2.0, value=0.9, step=0.1,
-                                             visible=True)
-            with gr.Row():
-                self_guidance_scale = gr.Slider(label="Self-Attention Lora Scale", minimum=0.0, maximum=0.5, value=0.2, step=0.1,
-                                             visible=True)
-            with gr.Row():
-                cross_guidance_scale = gr.Slider(label="Cross-Attention Lora Scale", minimum=0.0, maximum=0.5, value=0.2, step=0.1,
-                                             visible=True)
-            with gr.Row():
-                denoise_steps = gr.Number(label="Denoising Steps", minimum=20, maximum=50, value=30, step=1)
-                seed = gr.Number(label="Seed", minimum=-1, maximum=2147483647, step=1, value=20240508)
-    
-    try_button.click(fn=tryon_process, inputs=[garm_img, imgs, pose_img, prompt, cloth_guidance_scale, caption_guidance_scale, face_guidance_scale,self_guidance_scale, cross_guidance_scale, is_checked_face, is_checked_postprocess, is_checked_pose, denoise_steps, seed],
-                     outputs=[image_out], api_name='tryon')
-
-image_blocks.launch(server_port=20021)  # 指定固定端口