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import spaces |
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import gradio as gr |
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import os |
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import math |
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from preprocess.humanparsing.run_parsing import Parsing |
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from preprocess.dwpose import DWposeDetector |
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from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor |
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import torch |
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import torch.nn as nn |
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from src.pose_guider import PoseGuider |
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from PIL import Image |
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from src.utils_mask import get_mask_location |
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import numpy as np |
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from src.pipeline_stable_diffusion_3_tryon import StableDiffusion3TryOnPipeline |
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from src.transformer_sd3_garm import SD3Transformer2DModel as SD3Transformer2DModel_Garm |
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from src.transformer_sd3_vton import SD3Transformer2DModel as SD3Transformer2DModel_Vton |
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import cv2 |
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import random |
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from huggingface_hub import snapshot_download |
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example_path = os.path.join(os.path.dirname(__file__), 'examples') |
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fitdit_repo = "BoyuanJiang/FitDiT" |
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repo_path = snapshot_download(repo_id=fitdit_repo) |
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weight_dtype = torch.bfloat16 |
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device = "cuda" |
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transformer_garm = SD3Transformer2DModel_Garm.from_pretrained(os.path.join(repo_path, "transformer_garm"), torch_dtype=weight_dtype) |
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transformer_vton = SD3Transformer2DModel_Vton.from_pretrained(os.path.join(repo_path, "transformer_vton"), torch_dtype=weight_dtype) |
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pose_guider = PoseGuider(conditioning_embedding_channels=1536, conditioning_channels=3, block_out_channels=(32, 64, 256, 512)) |
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pose_guider.load_state_dict(torch.load(os.path.join(repo_path, "pose_guider", "diffusion_pytorch_model.bin"))) |
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image_encoder_large = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=weight_dtype) |
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image_encoder_bigG = CLIPVisionModelWithProjection.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", torch_dtype=weight_dtype) |
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pose_guider.to(device=device, dtype=weight_dtype) |
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image_encoder_large.to(device=device) |
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image_encoder_bigG.to(device=device) |
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pipeline = StableDiffusion3TryOnPipeline.from_pretrained(repo_path, torch_dtype=weight_dtype, \ |
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transformer_garm=transformer_garm, transformer_vton=transformer_vton, pose_guider=pose_guider, \ |
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image_encoder_large=image_encoder_large, image_encoder_bigG=image_encoder_bigG) |
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pipeline.to(device) |
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dwprocessor = DWposeDetector(model_root=repo_path, device=device) |
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parsing_model = Parsing(model_root=repo_path, device=device) |
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def generate_mask(vton_img, category, offset_top, offset_bottom, offset_left, offset_right): |
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with torch.inference_mode(): |
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vton_img = Image.open(vton_img) |
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vton_img_det = resize_image(vton_img) |
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pose_image, keypoints, _, candidate = dwprocessor(np.array(vton_img_det)[:,:,::-1]) |
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candidate[candidate<0]=0 |
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candidate = candidate[0] |
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candidate[:, 0]*=vton_img_det.width |
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candidate[:, 1]*=vton_img_det.height |
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pose_image = pose_image[:,:,::-1] |
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pose_image = Image.fromarray(pose_image) |
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model_parse, _ = parsing_model(vton_img_det) |
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mask, mask_gray = get_mask_location(category, model_parse, \ |
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candidate, model_parse.width, model_parse.height, \ |
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offset_top, offset_bottom, offset_left, offset_right) |
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mask = mask.resize(vton_img.size) |
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mask_gray = mask_gray.resize(vton_img.size) |
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mask = mask.convert("L") |
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mask_gray = mask_gray.convert("L") |
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masked_vton_img = Image.composite(mask_gray, vton_img, mask) |
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im = {} |
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im['background'] = np.array(vton_img.convert("RGBA")) |
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im['layers'] = [np.concatenate((np.array(mask_gray.convert("RGB")), np.array(mask)[:,:,np.newaxis]),axis=2)] |
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im['composite'] = np.array(masked_vton_img.convert("RGBA")) |
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return im, pose_image |
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@spaces.GPU |
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def process(vton_img, garm_img, pre_mask, pose_image, n_steps, image_scale, seed, num_images_per_prompt, resolution): |
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assert resolution in ["768x1024", "1152x1536", "1536x2048"] |
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new_width, new_height = resolution.split("x") |
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new_width = int(new_width) |
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new_height = int(new_height) |
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with torch.inference_mode(): |
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garm_img = Image.open(garm_img) |
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vton_img = Image.open(vton_img) |
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model_image_size = vton_img.size |
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garm_img, _, _ = pad_and_resize(garm_img, new_width=new_width, new_height=new_height) |
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vton_img, pad_w, pad_h = pad_and_resize(vton_img, new_width=new_width, new_height=new_height) |
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mask = pre_mask["layers"][0][:,:,3] |
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mask = Image.fromarray(mask) |
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mask, _, _ = pad_and_resize(mask, new_width=new_width, new_height=new_height, pad_color=(0,0,0)) |
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mask = mask.convert("L") |
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pose_image = Image.fromarray(pose_image) |
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pose_image, _, _ = pad_and_resize(pose_image, new_width=new_width, new_height=new_height, pad_color=(0,0,0)) |
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if seed==-1: |
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seed = random.randint(0, 2147483647) |
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res = pipeline( |
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height=new_height, |
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width=new_width, |
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guidance_scale=image_scale, |
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num_inference_steps=n_steps, |
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generator=torch.Generator("cpu").manual_seed(seed), |
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cloth_image=garm_img, |
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model_image=vton_img, |
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mask=mask, |
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pose_image=pose_image, |
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num_images_per_prompt=num_images_per_prompt |
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).images |
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for idx in range(len(res)): |
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res[idx] = unpad_and_resize(res[idx], pad_w, pad_h, model_image_size[0], model_image_size[1]) |
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return res |
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def pad_and_resize(im, new_width=768, new_height=1024, pad_color=(255, 255, 255), mode=Image.LANCZOS): |
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old_width, old_height = im.size |
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ratio_w = new_width / old_width |
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ratio_h = new_height / old_height |
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if ratio_w < ratio_h: |
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new_size = (new_width, round(old_height * ratio_w)) |
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else: |
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new_size = (round(old_width * ratio_h), new_height) |
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im_resized = im.resize(new_size, mode) |
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pad_w = math.ceil((new_width - im_resized.width) / 2) |
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pad_h = math.ceil((new_height - im_resized.height) / 2) |
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new_im = Image.new('RGB', (new_width, new_height), pad_color) |
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new_im.paste(im_resized, (pad_w, pad_h)) |
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return new_im, pad_w, pad_h |
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def unpad_and_resize(padded_im, pad_w, pad_h, original_width, original_height): |
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width, height = padded_im.size |
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left = pad_w |
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top = pad_h |
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right = width - pad_w |
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bottom = height - pad_h |
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cropped_im = padded_im.crop((left, top, right, bottom)) |
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resized_im = cropped_im.resize((original_width, original_height), Image.LANCZOS) |
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return resized_im |
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def resize_image(img, target_size=768): |
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width, height = img.size |
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if width < height: |
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scale = target_size / width |
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else: |
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scale = target_size / height |
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new_width = int(round(width * scale)) |
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new_height = int(round(height * scale)) |
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resized_img = img.resize((new_width, new_height), Image.LANCZOS) |
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return resized_img |
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HEADER = """ |
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<h1 style="text-align: center;"> FitDiT: Advancing the Authentic Garment Details for High-fidelity Virtual Try-on </h1> |
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<div style="display: flex; justify-content: center; align-items: center;"> |
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<a href="https://github.com/BoyuanJiang/FitDiT" style="margin: 0 2px;"> |
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<img src='https://img.shields.io/badge/GitHub-Repo-blue?style=flat&logo=GitHub' alt='GitHub'> |
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</a> |
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<a href="https://arxiv.org/abs/2411.10499" style="margin: 0 2px;"> |
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<img src='https://img.shields.io/badge/arXiv-2411.10499-red?style=flat&logo=arXiv&logoColor=red' alt='arxiv'> |
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</a> |
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<a href="http://demo.fitdit.byjiang.com/" style="margin: 0 2px;"> |
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<img src='https://img.shields.io/badge/Demo-Gradio-gold?style=flat&logo=Gradio&logoColor=red' alt='Demo'> |
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</a> |
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<a href='https://byjiang.com/FitDiT/' style="margin: 0 2px;"> |
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<img src='https://img.shields.io/badge/Webpage-Project-silver?style=flat&logo=&logoColor=orange' alt='webpage'> |
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</a> |
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<a href="https://raw.githubusercontent.com/BoyuanJiang/FitDiT/refs/heads/main/LICENSE" style="margin: 0 2px;"> |
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<img src='https://img.shields.io/badge/License-CC BY--NC--SA--4.0-lightgreen?style=flat&logo=Lisence' alt='License'> |
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</a> |
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</div> |
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<br> |
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FitDiT is designed for high-fidelity virtual try-on using Diffusion Transformers (DiT). It can only be used for <b>Non-commercial Use</b>.<br> |
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If you like our work, please star <a href="https://github.com/BoyuanJiang/FitDiT" style="color: blue; text-decoration: underline;">our github repository</a>. A <b>ComfyUI version</b> of FitDiT is available <a href="https://github.com/BoyuanJiang/FitDiT/tree/FitDiT-ComfyUI" style="color: blue; text-decoration: underline;">here</a>. |
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""" |
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def create_demo(): |
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with gr.Blocks(title="FitDiT") as demo: |
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gr.Markdown(HEADER) |
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with gr.Row(): |
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with gr.Column(): |
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vton_img = gr.Image(label="Model", sources=None, type="filepath", height=512) |
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with gr.Column(): |
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garm_img = gr.Image(label="Garment", sources=None, type="filepath", height=512) |
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with gr.Row(): |
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with gr.Column(): |
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masked_vton_img = gr.ImageEditor(label="masked_vton_img", type="numpy", height=512, interactive=True, brush=gr.Brush(default_color="rgb(127, 127, 127)", colors=[ |
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"rgb(128, 128, 128)" |
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])) |
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pose_image = gr.Image(label="pose_image", visible=False, interactive=False) |
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with gr.Column(): |
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result_gallery = gr.Gallery(label="Output", elem_id="output-img", interactive=False, columns=[2], rows=[2], object_fit="contain", height="auto") |
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with gr.Row(): |
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with gr.Column(): |
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offset_top = gr.Slider(label="mask offset top", minimum=-200, maximum=200, step=1, value=0) |
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with gr.Column(): |
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offset_bottom = gr.Slider(label="mask offset bottom", minimum=-200, maximum=200, step=1, value=0) |
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with gr.Column(): |
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offset_left = gr.Slider(label="mask offset left", minimum=-200, maximum=200, step=1, value=0) |
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with gr.Column(): |
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offset_right = gr.Slider(label="mask offset right", minimum=-200, maximum=200, step=1, value=0) |
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with gr.Row(): |
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with gr.Column(): |
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n_steps = gr.Slider(label="Steps", minimum=15, maximum=30, value=20, step=1) |
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with gr.Column(): |
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image_scale = gr.Slider(label="Guidance scale", minimum=1.0, maximum=5.0, value=2, step=0.1) |
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with gr.Column(): |
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seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, value=-1) |
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with gr.Column(): |
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num_images_per_prompt = gr.Slider(label="num_images", minimum=1, maximum=4, step=1, value=1) |
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with gr.Row(): |
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with gr.Column(): |
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example = gr.Examples( |
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label="Model (upper-body)", |
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inputs=vton_img, |
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examples_per_page=7, |
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examples=[ |
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os.path.join(example_path, 'model/0279.jpg'), |
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os.path.join(example_path, 'model/0303.jpg'), |
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os.path.join(example_path, 'model/2.jpg'), |
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os.path.join(example_path, 'model/0083.jpg'), |
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]) |
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example = gr.Examples( |
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label="Model (upper-body/lower-body)", |
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inputs=vton_img, |
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examples_per_page=7, |
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examples=[ |
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os.path.join(example_path, 'model/0.jpg'), |
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os.path.join(example_path, 'model/0179.jpg'), |
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os.path.join(example_path, 'model/0223.jpg'), |
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os.path.join(example_path, 'model/0347.jpg'), |
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]) |
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example = gr.Examples( |
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label="Model (dresses)", |
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inputs=vton_img, |
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examples_per_page=7, |
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examples=[ |
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os.path.join(example_path, 'model/4.jpg'), |
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os.path.join(example_path, 'model/5.jpg'), |
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os.path.join(example_path, 'model/6.jpg'), |
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os.path.join(example_path, 'model/7.jpg'), |
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]) |
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with gr.Column(): |
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example = gr.Examples( |
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label="Garment (upper-body)", |
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inputs=garm_img, |
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examples_per_page=7, |
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examples=[ |
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os.path.join(example_path, 'garment/12.jpg'), |
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os.path.join(example_path, 'garment/0012.jpg'), |
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os.path.join(example_path, 'garment/0047.jpg'), |
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os.path.join(example_path, 'garment/0049.jpg'), |
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]) |
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example = gr.Examples( |
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label="Garment (lower-body)", |
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inputs=garm_img, |
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examples_per_page=7, |
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examples=[ |
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os.path.join(example_path, 'garment/0317.jpg'), |
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os.path.join(example_path, 'garment/0327.jpg'), |
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os.path.join(example_path, 'garment/0329.jpg'), |
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os.path.join(example_path, 'garment/0362.jpg'), |
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]) |
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example = gr.Examples( |
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label="Garment (dresses)", |
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inputs=garm_img, |
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examples_per_page=7, |
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examples=[ |
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os.path.join(example_path, 'garment/8.jpg'), |
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os.path.join(example_path, 'garment/9.png'), |
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os.path.join(example_path, 'garment/10.jpg'), |
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os.path.join(example_path, 'garment/11.jpg'), |
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]) |
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with gr.Column(): |
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category = gr.Dropdown(label="Garment category", choices=["Upper-body", "Lower-body", "Dresses"], value="Upper-body") |
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resolution = gr.Dropdown(label="Try-on resolution", choices=["768x1024", "1152x1536", "1536x2048"], value="768x1024") |
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with gr.Column(): |
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run_mask_button = gr.Button(value="Step1: Run Mask") |
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run_button = gr.Button(value="Step2: Run Try-on") |
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ips1 = [vton_img, category, offset_top, offset_bottom, offset_left, offset_right] |
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ips2 = [vton_img, garm_img, masked_vton_img, pose_image, n_steps, image_scale, seed, num_images_per_prompt, resolution] |
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run_mask_button.click(fn=generate_mask, inputs=ips1, outputs=[masked_vton_img, pose_image]) |
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run_button.click(fn=process, inputs=ips2, outputs=[result_gallery]) |
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return demo |
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if __name__ == "__main__": |
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demo = create_demo() |
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demo.launch() |
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