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| import gradio as gr | |
| import os | |
| from pathlib import Path | |
| import sys | |
| import torch | |
| from PIL import Image, ImageOps | |
| from utils_ootd import get_mask_location | |
| PROJECT_ROOT = Path(__file__).absolute().parents[1].absolute() | |
| sys.path.insert(0, str(PROJECT_ROOT)) | |
| from preprocess.openpose.run_openpose import OpenPose | |
| from preprocess.humanparsing.run_parsing import Parsing | |
| from ootd.inference_ootd_hd import OOTDiffusionHD | |
| from ootd.inference_ootd_dc import OOTDiffusionDC | |
| import spaces | |
| openpose_model_hd = OpenPose(0) | |
| parsing_model_hd = Parsing(0) | |
| ootd_model_hd = OOTDiffusionHD(0) | |
| openpose_model_dc = OpenPose(1) | |
| parsing_model_dc = Parsing(1) | |
| ootd_model_dc = OOTDiffusionDC(1) | |
| category_dict = ['upperbody', 'lowerbody', 'dress'] | |
| category_dict_utils = ['upper_body', 'lower_body', 'dresses'] | |
| example_path = os.path.join(os.path.dirname(__file__), 'examples') | |
| model_hd = os.path.join(example_path, 'model/model_1.png') | |
| garment_hd = os.path.join(example_path, 'garment/03244_00.jpg') | |
| model_dc = os.path.join(example_path, 'model/model_8.png') | |
| garment_dc = os.path.join(example_path, 'garment/048554_1.jpg') | |
| def process_hd(vton_img, garm_img, n_samples, n_steps, image_scale, seed): | |
| model_type = 'hd' | |
| category = 0 # 0:upperbody; 1:lowerbody; 2:dress | |
| with torch.no_grad(): | |
| garm_img = Image.open(garm_img).resize((768, 1024)) | |
| vton_img = Image.open(vton_img).resize((768, 1024)) | |
| keypoints = openpose_model_hd(vton_img.resize((384, 512))) | |
| model_parse, _ = parsing_model_hd(vton_img.resize((384, 512))) | |
| mask, mask_gray = get_mask_location(model_type, category_dict_utils[category], model_parse, keypoints) | |
| mask = mask.resize((768, 1024), Image.NEAREST) | |
| mask_gray = mask_gray.resize((768, 1024), Image.NEAREST) | |
| masked_vton_img = Image.composite(mask_gray, vton_img, mask) | |
| images = ootd_model_hd( | |
| model_type=model_type, | |
| category=category_dict[category], | |
| image_garm=garm_img, | |
| image_vton=masked_vton_img, | |
| mask=mask, | |
| image_ori=vton_img, | |
| num_samples=n_samples, | |
| num_steps=n_steps, | |
| image_scale=image_scale, | |
| seed=seed, | |
| ) | |
| return images | |
| def process_dc(vton_img, garm_img, category, n_samples, n_steps, image_scale, seed): | |
| model_type = 'dc' | |
| if category == 'Upper-body': | |
| category = 0 | |
| elif category == 'Lower-body': | |
| category = 1 | |
| else: | |
| category =2 | |
| with torch.no_grad(): | |
| garm_img = Image.open(garm_img).resize((768, 1024)) | |
| vton_img = Image.open(vton_img).resize((768, 1024)) | |
| keypoints = openpose_model_dc(vton_img.resize((384, 512))) | |
| model_parse, _ = parsing_model_dc(vton_img.resize((384, 512))) | |
| mask, mask_gray = get_mask_location(model_type, category_dict_utils[category], model_parse, keypoints) | |
| mask = mask.resize((768, 1024), Image.NEAREST) | |
| mask_gray = mask_gray.resize((768, 1024), Image.NEAREST) | |
| masked_vton_img = Image.composite(mask_gray, vton_img, mask) | |
| images = ootd_model_dc( | |
| model_type=model_type, | |
| category=category_dict[category], | |
| image_garm=garm_img, | |
| image_vton=masked_vton_img, | |
| mask=mask, | |
| image_ori=vton_img, | |
| num_samples=n_samples, | |
| num_steps=n_steps, | |
| image_scale=image_scale, | |
| seed=seed, | |
| ) | |
| return images | |
| block = gr.Blocks().queue() | |
| with block: | |
| with gr.Row(): | |
| gr.Markdown("# OOTDiffusion Demo") | |
| with gr.Row(): | |
| gr.Markdown("## Half-body") | |
| with gr.Row(): | |
| gr.Markdown("***Support upper-body garments***") | |
| with gr.Row(): | |
| with gr.Column(): | |
| vton_img = gr.Image(label="Model", sources='upload', type="filepath", height=384, value=model_hd) | |
| example = gr.Examples( | |
| inputs=vton_img, | |
| examples_per_page=14, | |
| examples=[ | |
| os.path.join(example_path, 'model/model_1.png'), | |
| os.path.join(example_path, 'model/model_2.png'), | |
| os.path.join(example_path, 'model/model_3.png'), | |
| os.path.join(example_path, 'model/model_4.png'), | |
| os.path.join(example_path, 'model/model_5.png'), | |
| os.path.join(example_path, 'model/model_6.png'), | |
| os.path.join(example_path, 'model/model_7.png'), | |
| os.path.join(example_path, 'model/01008_00.jpg'), | |
| os.path.join(example_path, 'model/07966_00.jpg'), | |
| os.path.join(example_path, 'model/05997_00.jpg'), | |
| os.path.join(example_path, 'model/02849_00.jpg'), | |
| os.path.join(example_path, 'model/14627_00.jpg'), | |
| os.path.join(example_path, 'model/09597_00.jpg'), | |
| os.path.join(example_path, 'model/01861_00.jpg'), | |
| ]) | |
| with gr.Column(): | |
| garm_img = gr.Image(label="Garment", sources='upload', type="filepath", height=384, value=garment_hd) | |
| example = gr.Examples( | |
| inputs=garm_img, | |
| examples_per_page=14, | |
| examples=[ | |
| os.path.join(example_path, 'garment/03244_00.jpg'), | |
| os.path.join(example_path, 'garment/00126_00.jpg'), | |
| os.path.join(example_path, 'garment/03032_00.jpg'), | |
| os.path.join(example_path, 'garment/06123_00.jpg'), | |
| os.path.join(example_path, 'garment/02305_00.jpg'), | |
| os.path.join(example_path, 'garment/00055_00.jpg'), | |
| os.path.join(example_path, 'garment/00470_00.jpg'), | |
| os.path.join(example_path, 'garment/02015_00.jpg'), | |
| os.path.join(example_path, 'garment/10297_00.jpg'), | |
| os.path.join(example_path, 'garment/07382_00.jpg'), | |
| os.path.join(example_path, 'garment/07764_00.jpg'), | |
| os.path.join(example_path, 'garment/00151_00.jpg'), | |
| os.path.join(example_path, 'garment/12562_00.jpg'), | |
| os.path.join(example_path, 'garment/04825_00.jpg'), | |
| ]) | |
| with gr.Column(): | |
| result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", preview=True, scale=1) | |
| with gr.Column(): | |
| run_button = gr.Button(value="Run") | |
| n_samples = gr.Slider(label="Images", minimum=1, maximum=4, value=1, step=1) | |
| n_steps = gr.Slider(label="Steps", minimum=20, maximum=40, value=20, step=1) | |
| # scale = gr.Slider(label="Scale", minimum=1.0, maximum=12.0, value=5.0, step=0.1) | |
| image_scale = gr.Slider(label="Guidance scale", minimum=1.0, maximum=5.0, value=2.0, step=0.1) | |
| seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, value=-1) | |
| ips = [vton_img, garm_img, n_samples, n_steps, image_scale, seed] | |
| run_button.click(fn=process_hd, inputs=ips, outputs=[result_gallery]) | |
| with gr.Row(): | |
| gr.Markdown("## Full-body") | |
| with gr.Row(): | |
| gr.Markdown("***Support upper-body/lower-body/dresses; garment category must be paired!!!***") | |
| with gr.Row(): | |
| with gr.Column(): | |
| vton_img_dc = gr.Image(label="Model", sources='upload', type="filepath", height=384, value=model_dc) | |
| example = gr.Examples( | |
| label="Examples (upper-body/lower-body)", | |
| inputs=vton_img_dc, | |
| examples_per_page=7, | |
| examples=[ | |
| os.path.join(example_path, 'model/model_8.png'), | |
| os.path.join(example_path, 'model/049447_0.jpg'), | |
| os.path.join(example_path, 'model/049713_0.jpg'), | |
| os.path.join(example_path, 'model/051482_0.jpg'), | |
| os.path.join(example_path, 'model/051918_0.jpg'), | |
| os.path.join(example_path, 'model/051962_0.jpg'), | |
| os.path.join(example_path, 'model/049205_0.jpg'), | |
| ]) | |
| example = gr.Examples( | |
| label="Examples (dress)", | |
| inputs=vton_img_dc, | |
| examples_per_page=7, | |
| examples=[ | |
| os.path.join(example_path, 'model/model_9.png'), | |
| os.path.join(example_path, 'model/052767_0.jpg'), | |
| os.path.join(example_path, 'model/052472_0.jpg'), | |
| os.path.join(example_path, 'model/053514_0.jpg'), | |
| os.path.join(example_path, 'model/053228_0.jpg'), | |
| os.path.join(example_path, 'model/052964_0.jpg'), | |
| os.path.join(example_path, 'model/053700_0.jpg'), | |
| ]) | |
| with gr.Column(): | |
| garm_img_dc = gr.Image(label="Garment", sources='upload', type="filepath", height=384, value=garment_dc) | |
| category_dc = gr.Dropdown(label="Garment category (important option!!!)", choices=["Upper-body", "Lower-body", "Dress"], value="Upper-body") | |
| example = gr.Examples( | |
| label="Examples (upper-body)", | |
| inputs=garm_img_dc, | |
| examples_per_page=7, | |
| examples=[ | |
| os.path.join(example_path, 'garment/048554_1.jpg'), | |
| os.path.join(example_path, 'garment/049920_1.jpg'), | |
| os.path.join(example_path, 'garment/049965_1.jpg'), | |
| os.path.join(example_path, 'garment/049949_1.jpg'), | |
| os.path.join(example_path, 'garment/050181_1.jpg'), | |
| os.path.join(example_path, 'garment/049805_1.jpg'), | |
| os.path.join(example_path, 'garment/050105_1.jpg'), | |
| ]) | |
| example = gr.Examples( | |
| label="Examples (lower-body)", | |
| inputs=garm_img_dc, | |
| examples_per_page=7, | |
| examples=[ | |
| os.path.join(example_path, 'garment/051827_1.jpg'), | |
| os.path.join(example_path, 'garment/051946_1.jpg'), | |
| os.path.join(example_path, 'garment/051473_1.jpg'), | |
| os.path.join(example_path, 'garment/051515_1.jpg'), | |
| os.path.join(example_path, 'garment/051517_1.jpg'), | |
| os.path.join(example_path, 'garment/051988_1.jpg'), | |
| os.path.join(example_path, 'garment/051412_1.jpg'), | |
| ]) | |
| example = gr.Examples( | |
| label="Examples (dress)", | |
| inputs=garm_img_dc, | |
| examples_per_page=7, | |
| examples=[ | |
| os.path.join(example_path, 'garment/053290_1.jpg'), | |
| os.path.join(example_path, 'garment/053744_1.jpg'), | |
| os.path.join(example_path, 'garment/053742_1.jpg'), | |
| os.path.join(example_path, 'garment/053786_1.jpg'), | |
| os.path.join(example_path, 'garment/053790_1.jpg'), | |
| os.path.join(example_path, 'garment/053319_1.jpg'), | |
| os.path.join(example_path, 'garment/052234_1.jpg'), | |
| ]) | |
| with gr.Column(): | |
| result_gallery_dc = gr.Gallery(label='Output', show_label=False, elem_id="gallery", preview=True, scale=1) | |
| with gr.Column(): | |
| run_button_dc = gr.Button(value="Run") | |
| n_samples_dc = gr.Slider(label="Images", minimum=1, maximum=4, value=1, step=1) | |
| n_steps_dc = gr.Slider(label="Steps", minimum=20, maximum=40, value=20, step=1) | |
| # scale_dc = gr.Slider(label="Scale", minimum=1.0, maximum=12.0, value=5.0, step=0.1) | |
| image_scale_dc = gr.Slider(label="Guidance scale", minimum=1.0, maximum=5.0, value=2.0, step=0.1) | |
| seed_dc = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, value=-1) | |
| ips_dc = [vton_img_dc, garm_img_dc, category_dc, n_samples_dc, n_steps_dc, image_scale_dc, seed_dc] | |
| run_button_dc.click(fn=process_dc, inputs=ips_dc, outputs=[result_gallery_dc]) | |
| block.launch() | |