import argparse import os from datetime import datetime import gradio as gr import numpy as np import torch from diffusers.image_processor import VaeImageProcessor from huggingface_hub import snapshot_download from PIL import Image from model.cloth_masker import AutoMasker, vis_mask from model.pipeline import CatVTONPipeline from utils import init_weight_dtype, resize_and_crop, resize_and_padding def parse_args(): parser = argparse.ArgumentParser(description="Simple example of a training script.") parser.add_argument( "--base_model_path", type=str, default="runwayml/stable-diffusion-inpainting", help=( "The path to the base model to use for evaluation. This can be a local path or a model identifier from the Model Hub." ), ) parser.add_argument( "--resume_path", type=str, default="zhengchong/CatVTON", help=( "The Path to the checkpoint of trained tryon model." ), ) parser.add_argument( "--output_dir", type=str, default="resource/demo/output", help="The output directory where the model predictions will be written.", ) parser.add_argument( "--width", type=int, default=768, help=( "The resolution for input images, all the images in the train/validation dataset will be resized to this" " resolution" ), ) parser.add_argument( "--height", type=int, default=1024, help=( "The resolution for input images, all the images in the train/validation dataset will be resized to this" " resolution" ), ) parser.add_argument( "--repaint", action="store_true", help="Whether to repaint the result image with the original background." ) parser.add_argument( "--allow_tf32", action="store_true", default=True, help=( "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" ), ) parser.add_argument( "--mixed_precision", type=str, default="bf16", choices=["no", "fp16", "bf16"], help=( "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." ), ) # parser.add_argument( # "--enable_condition_noise", # action="store_true", # default=True, # help="Whether or not to enable condition noise.", # ) args = parser.parse_args() env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) if env_local_rank != -1 and env_local_rank != args.local_rank: args.local_rank = env_local_rank return args def image_grid(imgs, rows, cols): assert len(imgs) == rows * cols w, h = imgs[0].size grid = Image.new("RGB", size=(cols * w, rows * h)) for i, img in enumerate(imgs): grid.paste(img, box=(i % cols * w, i // cols * h)) return grid args = parse_args() repo_path = snapshot_download(repo_id=args.resume_path) # Pipeline pipeline = CatVTONPipeline( base_ckpt=args.base_model_path, attn_ckpt=repo_path, attn_ckpt_version="mix", weight_dtype=init_weight_dtype(args.mixed_precision), use_tf32=args.allow_tf32, device='cuda' ) # AutoMasker mask_processor = VaeImageProcessor(vae_scale_factor=8, do_normalize=False, do_binarize=True, do_convert_grayscale=True) automasker = AutoMasker( densepose_ckpt=os.path.join(repo_path, "DensePose"), schp_ckpt=os.path.join(repo_path, "SCHP"), device='cuda', ) def submit_function( person_image, cloth_image, cloth_type, num_inference_steps, guidance_scale, seed, show_type ): person_image, mask = person_image["background"], person_image["layers"][0] mask = Image.open(mask).convert("L") if len(np.unique(np.array(mask))) == 1: mask = None else: mask = np.array(mask) mask[mask > 0] = 255 mask = Image.fromarray(mask) tmp_folder = args.output_dir date_str = datetime.now().strftime("%Y%m%d%H%M%S") result_save_path = os.path.join(tmp_folder, date_str[:8], date_str[8:] + ".png") if not os.path.exists(os.path.join(tmp_folder, date_str[:8])): os.makedirs(os.path.join(tmp_folder, date_str[:8])) generator = None if seed != -1: generator = torch.Generator(device='cuda').manual_seed(seed) person_image = Image.open(person_image).convert("RGB") cloth_image = Image.open(cloth_image).convert("RGB") person_image = resize_and_crop(person_image, (args.width, args.height)) cloth_image = resize_and_padding(cloth_image, (args.width, args.height)) # Process mask if mask is not None: mask = resize_and_crop(mask, (args.width, args.height)) else: mask = automasker( person_image, cloth_type )['mask'] mask = mask_processor.blur(mask, blur_factor=9) # Inference # try: result_image = pipeline( image=person_image, condition_image=cloth_image, mask=mask, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=generator )[0] # except Exception as e: # raise gr.Error( # "An error occurred. Please try again later: {}".format(e) # ) # Post-process masked_person = vis_mask(person_image, mask) save_result_image = image_grid([person_image, masked_person, cloth_image, result_image], 1, 4) save_result_image.save(result_save_path) if show_type == "result only": return result_image else: width, height = person_image.size if show_type == "input & result": condition_width = width // 2 conditions = image_grid([person_image, cloth_image], 2, 1) else: condition_width = width // 3 conditions = image_grid([person_image, masked_person , cloth_image], 3, 1) conditions = conditions.resize((condition_width, height), Image.NEAREST) new_result_image = Image.new("RGB", (width + condition_width + 5, height)) new_result_image.paste(conditions, (0, 0)) new_result_image.paste(result_image, (condition_width + 5, 0)) return new_result_image def person_example_fn(image_path): return image_path HEADER = """

🐈 CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models

arxiv huggingface GitHub Demo webpage License
""" def app_gradio(): with gr.Blocks(title="CatVTON") as demo: gr.Markdown(HEADER) with gr.Row(): with gr.Column(scale=1, min_width=350): with gr.Row(): image_path = gr.Image( type="filepath", interactive=True, visible=False, ) person_image = gr.ImageEditor( interactive=True, label="Person Image", type="filepath" ) with gr.Row(): with gr.Column(scale=1, min_width=230): cloth_image = gr.Image( interactive=True, label="Condition Image", type="filepath" ) with gr.Column(scale=1, min_width=120): gr.Markdown( 'Two ways to provide Mask:
1. Upload the person image and use the `🖌️` above to draw the Mask (higher priority)
2. Select the `Try-On Cloth Type` to generate automatically ' ) cloth_type = gr.Radio( label="Try-On Cloth Type", choices=["upper", "lower", "overall"], value="upper", ) submit = gr.Button("Submit") gr.Markdown( '
!!! Click only Once, Wait for Delay !!!
' ) gr.Markdown( 'Advanced options can adjust details:
1. `Inference Step` may enhance details;
2. `CFG` is highly correlated with saturation;
3. `Random seed` may improve pseudo-shadow.' ) with gr.Accordion("Advanced Options", open=False): num_inference_steps = gr.Slider( label="Inference Step", minimum=10, maximum=100, step=5, value=50 ) # Guidence Scale guidance_scale = gr.Slider( label="CFG Strenth", minimum=0.0, maximum=7.5, step=0.5, value=2.5 ) # Random Seed seed = gr.Slider( label="Seed", minimum=-1, maximum=10000, step=1, value=42 ) show_type = gr.Radio( label="Show Type", choices=["result only", "input & result", "input & mask & result"], value="input & mask & result", ) with gr.Column(scale=2, min_width=500): result_image = gr.Image(interactive=False, label="Result") with gr.Row(): # Photo Examples root_path = "resource/demo/example" with gr.Column(): men_exm = gr.Examples( examples=[ os.path.join(root_path, "person", "men", _) for _ in os.listdir(os.path.join(root_path, "person", "men")) ], examples_per_page=4, inputs=image_path, label="Person Examples ①", ) women_exm = gr.Examples( examples=[ os.path.join(root_path, "person", "women", _) for _ in os.listdir(os.path.join(root_path, "person", "women")) ], examples_per_page=4, inputs=image_path, label="Person Examples ②", ) gr.Markdown( '*Person examples come from the demos of OOTDiffusion and OutfitAnyone. ' ) with gr.Column(): condition_upper_exm = gr.Examples( examples=[ os.path.join(root_path, "condition", "upper", _) for _ in os.listdir(os.path.join(root_path, "condition", "upper")) ], examples_per_page=4, inputs=cloth_image, label="Condition Upper Examples", ) condition_overall_exm = gr.Examples( examples=[ os.path.join(root_path, "condition", "overall", _) for _ in os.listdir(os.path.join(root_path, "condition", "overall")) ], examples_per_page=4, inputs=cloth_image, label="Condition Overall Examples", ) condition_person_exm = gr.Examples( examples=[ os.path.join(root_path, "condition", "person", _) for _ in os.listdir(os.path.join(root_path, "condition", "person")) ], examples_per_page=4, inputs=cloth_image, label="Condition Reference Person Examples", ) gr.Markdown( '*Condition examples come from the Internet. ' ) image_path.change( person_example_fn, inputs=image_path, outputs=person_image ) submit.click( submit_function, [ person_image, cloth_image, cloth_type, num_inference_steps, guidance_scale, seed, show_type, ], result_image, ) demo.queue().launch(share=True, show_error=True) if __name__ == "__main__": app_gradio()