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| import argparse | |
| import os | |
| os.environ['CUDA_HOME'] = '/usr/local/cuda' | |
| os.environ['PATH'] = os.environ['PATH'] + ':/usr/local/cuda/bin' | |
| from datetime import datetime | |
| import gradio as gr | |
| import spaces | |
| import numpy as np | |
| import torch | |
| from diffusers.image_processor import VaeImageProcessor | |
| from huggingface_hub import snapshot_download | |
| from PIL import Image | |
| torch.jit.script = lambda f: f | |
| from model.cloth_masker import AutoMasker, vis_mask | |
| from model.pipeline import CatVTONPipeline, CatVTONPix2PixPipeline | |
| from model.flux.pipeline_flux_tryon import FluxTryOnPipeline | |
| 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="booksforcharlie/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( | |
| "--p2p_base_model_path", | |
| type=str, | |
| default="timbrooks/instruct-pix2pix", | |
| 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." | |
| ), | |
| ) | |
| 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() | |
| # Mask-based CatVTON | |
| catvton_repo = "zhengchong/CatVTON" | |
| repo_path = snapshot_download(repo_id=catvton_repo) | |
| # 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', | |
| ) | |
| # Flux-based CatVTON | |
| access_token = os.getenv("HUGGING_FACE_HUB_TOKEN") | |
| flux_repo = "black-forest-labs/FLUX.1-Fill-dev" | |
| pipeline_flux = FluxTryOnPipeline.from_pretrained(flux_repo, use_auth_token=access_token) | |
| pipeline_flux.load_lora_weights( | |
| os.path.join(repo_path, "flux-lora"), | |
| weight_name='pytorch_lora_weights.safetensors' | |
| ) | |
| pipeline_flux.to("cuda", init_weight_dtype(args.mixed_precision)) | |
| # Mask-free CatVTON | |
| catvton_mf_repo = "zhengchong/CatVTON-MaskFree" | |
| repo_path_mf = snapshot_download(repo_id=catvton_mf_repo, use_auth_token=access_token) | |
| pipeline_p2p = CatVTONPix2PixPipeline( | |
| base_ckpt=args.p2p_base_model_path, | |
| attn_ckpt=repo_path_mf, | |
| attn_ckpt_version="mix-48k-1024", | |
| weight_dtype=init_weight_dtype(args.mixed_precision), | |
| use_tf32=args.allow_tf32, | |
| 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 submit_function_p2p( | |
| person_image, | |
| cloth_image, | |
| num_inference_steps, | |
| guidance_scale, | |
| seed): | |
| person_image= person_image["background"] | |
| 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)) | |
| # Inference | |
| try: | |
| result_image = pipeline_p2p( | |
| image=person_image, | |
| condition_image=cloth_image, | |
| 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 | |
| save_result_image = image_grid([person_image, cloth_image, result_image], 1, 3) | |
| save_result_image.save(result_save_path) | |
| return result_image | |
| def submit_function_flux( | |
| person_image, | |
| cloth_image, | |
| cloth_type, | |
| num_inference_steps, | |
| guidance_scale, | |
| seed, | |
| show_type | |
| ): | |
| # Process image editor input | |
| 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) | |
| # Set random seed | |
| generator = None | |
| if seed != -1: | |
| generator = torch.Generator(device='cuda').manual_seed(seed) | |
| # Process input images | |
| person_image = Image.open(person_image).convert("RGB") | |
| cloth_image = Image.open(cloth_image).convert("RGB") | |
| # Adjust image sizes | |
| 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 | |
| result_image = pipeline_flux( | |
| image=person_image, | |
| condition_image=cloth_image, | |
| mask_image=mask, | |
| width=args.width, | |
| height=args.height, | |
| num_inference_steps=num_inference_steps, | |
| guidance_scale=guidance_scale, | |
| generator=generator | |
| ).images[0] | |
| # Post-processing | |
| masked_person = vis_mask(person_image, mask) | |
| # Return result based on show type | |
| 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 = """ | |
| <h1 style="text-align: center;"> 🐈 CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models </h1> | |
| <div style="display: flex; justify-content: center; align-items: center;"> | |
| <a href="http://arxiv.org/abs/2407.15886" style="margin: 0 2px;"> | |
| <img src='https://img.shields.io/badge/arXiv-2407.15886-red?style=flat&logo=arXiv&logoColor=red' alt='arxiv'> | |
| </a> | |
| <a href='https://huggingface.co./zhengchong/CatVTON' style="margin: 0 2px;"> | |
| <img src='https://img.shields.io/badge/Hugging Face-ckpts-orange?style=flat&logo=HuggingFace&logoColor=orange' alt='huggingface'> | |
| </a> | |
| <a href="https://github.com/Zheng-Chong/CatVTON" style="margin: 0 2px;"> | |
| <img src='https://img.shields.io/badge/GitHub-Repo-blue?style=flat&logo=GitHub' alt='GitHub'> | |
| </a> | |
| <a href="http://120.76.142.206:8888" style="margin: 0 2px;"> | |
| <img src='https://img.shields.io/badge/Demo-Gradio-gold?style=flat&logo=Gradio&logoColor=red' alt='Demo'> | |
| </a> | |
| <a href="https://huggingface.co./spaces/zhengchong/CatVTON" style="margin: 0 2px;"> | |
| <img src='https://img.shields.io/badge/Space-ZeroGPU-orange?style=flat&logo=Gradio&logoColor=red' alt='Demo'> | |
| </a> | |
| <a href='https://zheng-chong.github.io/CatVTON/' style="margin: 0 2px;"> | |
| <img src='https://img.shields.io/badge/Webpage-Project-silver?style=flat&logo=&logoColor=orange' alt='webpage'> | |
| </a> | |
| <a href="https://github.com/Zheng-Chong/CatVTON/LICENCE" style="margin: 0 2px;"> | |
| <img src='https://img.shields.io/badge/License-CC BY--NC--SA--4.0-lightgreen?style=flat&logo=Lisence' alt='License'> | |
| </a> | |
| </div> | |
| <br> | |
| · This demo and our weights are only for Non-commercial Use. <br> | |
| · Thanks to <a href="https://huggingface.co./zero-gpu-explorers">ZeroGPU</a> for providing A100 for our <a href="https://huggingface.co./spaces/zhengchong/CatVTON">HuggingFace Space</a>. <br> | |
| · SafetyChecker is set to filter NSFW content, but it may block normal results too. Please adjust the <span>`seed`</span> for normal outcomes.<br> | |
| """ | |
| def app_gradio(): | |
| with gr.Blocks(title="CatVTON") as demo: | |
| gr.Markdown(HEADER) | |
| with gr.Tab("Mask-based & SD1.5"): | |
| 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( | |
| '<span style="color: #808080; font-size: small;">Two ways to provide Mask:<br>1. Upload the person image and use the `🖌️` above to draw the Mask (higher priority)<br>2. Select the `Try-On Cloth Type` to generate automatically </span>' | |
| ) | |
| cloth_type = gr.Radio( | |
| label="Try-On Cloth Type", | |
| choices=["upper", "lower", "overall"], | |
| value="upper", | |
| ) | |
| submit = gr.Button("Submit") | |
| gr.Markdown( | |
| '<center><span style="color: #FF0000">!!! Click only Once, Wait for Delay !!!</span></center>' | |
| ) | |
| gr.Markdown( | |
| '<span style="color: #808080; font-size: small;">Advanced options can adjust details:<br>1. `Inference Step` may enhance details;<br>2. `CFG` is highly correlated with saturation;<br>3. `Random seed` may improve pseudo-shadow.</span>' | |
| ) | |
| 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( | |
| '<span style="color: #808080; font-size: small;">*Person examples come from the demos of <a href="https://huggingface.co./spaces/levihsu/OOTDiffusion">OOTDiffusion</a> and <a href="https://www.outfitanyone.org">OutfitAnyone</a>. </span>' | |
| ) | |
| 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( | |
| '<span style="color: #808080; font-size: small;">*Condition examples come from the Internet. </span>' | |
| ) | |
| 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, | |
| ) | |
| with gr.Tab("Mask-based & Flux.1 Fill Dev"): | |
| with gr.Row(): | |
| with gr.Column(scale=1, min_width=350): | |
| with gr.Row(): | |
| image_path_flux = gr.Image( | |
| type="filepath", | |
| interactive=True, | |
| visible=False, | |
| ) | |
| person_image_flux = gr.ImageEditor( | |
| interactive=True, label="Person Image", type="filepath" | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=1, min_width=230): | |
| cloth_image_flux = gr.Image( | |
| interactive=True, label="Condition Image", type="filepath" | |
| ) | |
| with gr.Column(scale=1, min_width=120): | |
| gr.Markdown( | |
| '<span style="color: #808080; font-size: small;">Two ways to provide Mask:<br>1. Upload the person image and use the `🖌️` above to draw the Mask (higher priority)<br>2. Select the `Try-On Cloth Type` to generate automatically </span>' | |
| ) | |
| cloth_type = gr.Radio( | |
| label="Try-On Cloth Type", | |
| choices=["upper", "lower", "overall"], | |
| value="upper", | |
| ) | |
| submit_flux = gr.Button("Submit") | |
| gr.Markdown( | |
| '<center><span style="color: #FF0000">!!! Click only Once, Wait for Delay !!!</span></center>' | |
| ) | |
| with gr.Accordion("Advanced Options", open=False): | |
| num_inference_steps_flux = gr.Slider( | |
| label="Inference Step", minimum=10, maximum=100, step=5, value=50 | |
| ) | |
| # Guidence Scale | |
| guidance_scale_flux = gr.Slider( | |
| label="CFG Strenth", minimum=0.0, maximum=50, step=0.5, value=30 | |
| ) | |
| # Random Seed | |
| seed_flux = 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_flux = gr.Image(interactive=False, label="Result") | |
| with gr.Row(): | |
| # Photo Examples | |
| root_path = "resource/demo/example" | |
| with gr.Column(): | |
| 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_flux, | |
| label="Person Examples ①", | |
| ) | |
| 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_flux, | |
| label="Person Examples ②", | |
| ) | |
| gr.Markdown( | |
| '<span style="color: #808080; font-size: small;">*Person examples come from the demos of <a href="https://huggingface.co./spaces/levihsu/OOTDiffusion">OOTDiffusion</a> and <a href="https://www.outfitanyone.org">OutfitAnyone</a>. </span>' | |
| ) | |
| with gr.Column(): | |
| 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_flux, | |
| label="Condition Upper Examples", | |
| ) | |
| 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_flux, | |
| 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_flux, | |
| label="Condition Reference Person Examples", | |
| ) | |
| gr.Markdown( | |
| '<span style="color: #808080; font-size: small;">*Condition examples come from the Internet. </span>' | |
| ) | |
| image_path_flux.change( | |
| person_example_fn, inputs=image_path_flux, outputs=person_image_flux | |
| ) | |
| submit_flux.click( | |
| submit_function_flux, | |
| [person_image_flux, cloth_image_flux, cloth_type, num_inference_steps_flux, guidance_scale_flux, seed_flux, show_type], | |
| result_image_flux, | |
| ) | |
| with gr.Tab("Mask-free & SD1.5"): | |
| with gr.Row(): | |
| with gr.Column(scale=1, min_width=350): | |
| with gr.Row(): | |
| image_path_p2p = gr.Image( | |
| type="filepath", | |
| interactive=True, | |
| visible=False, | |
| ) | |
| person_image_p2p = gr.ImageEditor( | |
| interactive=True, label="Person Image", type="filepath" | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=1, min_width=230): | |
| cloth_image_p2p = gr.Image( | |
| interactive=True, label="Condition Image", type="filepath" | |
| ) | |
| submit_p2p = gr.Button("Submit") | |
| gr.Markdown( | |
| '<center><span style="color: #FF0000">!!! Click only Once, Wait for Delay !!!</span></center>' | |
| ) | |
| gr.Markdown( | |
| '<span style="color: #808080; font-size: small;">Advanced options can adjust details:<br>1. `Inference Step` may enhance details;<br>2. `CFG` is highly correlated with saturation;<br>3. `Random seed` may improve pseudo-shadow.</span>' | |
| ) | |
| with gr.Accordion("Advanced Options", open=False): | |
| num_inference_steps_p2p = gr.Slider( | |
| label="Inference Step", minimum=10, maximum=100, step=5, value=50 | |
| ) | |
| # Guidence Scale | |
| guidance_scale_p2p = gr.Slider( | |
| label="CFG Strenth", minimum=0.0, maximum=7.5, step=0.5, value=2.5 | |
| ) | |
| # Random Seed | |
| seed_p2p = 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_p2p = gr.Image(interactive=False, label="Result") | |
| with gr.Row(): | |
| # Photo Examples | |
| root_path = "resource/demo/example" | |
| with gr.Column(): | |
| 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_p2p, | |
| label="Person Examples ①", | |
| ) | |
| 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_p2p, | |
| label="Person Examples ②", | |
| ) | |
| gr.Markdown( | |
| '<span style="color: #808080; font-size: small;">*Person examples come from the demos of <a href="https://huggingface.co./spaces/levihsu/OOTDiffusion">OOTDiffusion</a> and <a href="https://www.outfitanyone.org">OutfitAnyone</a>. </span>' | |
| ) | |
| with gr.Column(): | |
| 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_p2p, | |
| label="Condition Upper Examples", | |
| ) | |
| 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_p2p, | |
| 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_p2p, | |
| label="Condition Reference Person Examples", | |
| ) | |
| gr.Markdown( | |
| '<span style="color: #808080; font-size: small;">*Condition examples come from the Internet. </span>' | |
| ) | |
| image_path_p2p.change( | |
| person_example_fn, inputs=image_path_p2p, outputs=person_image_p2p | |
| ) | |
| submit_p2p.click( | |
| submit_function_p2p, | |
| [ | |
| person_image_p2p, | |
| cloth_image_p2p, | |
| num_inference_steps_p2p, | |
| guidance_scale_p2p, | |
| seed_p2p], | |
| result_image_p2p, | |
| ) | |
| demo.queue().launch(share=True, show_error=True) | |
| if __name__ == "__main__": | |
| app_gradio() | |