from preprocess.detectron2.projects.DensePose.apply_net_gradio import DensePose4Gradio from preprocess.humanparsing.run_parsing import Parsing from preprocess.openpose.run_openpose import OpenPose import pytorch_lightning as pl import os import sys import time from glob import glob from os.path import join as opj from pathlib import Path import gradio as gr import torch from omegaconf import OmegaConf from PIL import Image import spaces from cldm.model import create_model from cldm.plms_hacked import PLMSSampler from utils_stableviton import get_batch, get_mask_location, tensor2img print("pip import done") PROJECT_ROOT = Path(__file__).absolute().parents[1].absolute() sys.path.insert(0, str(PROJECT_ROOT)) IMG_H = 512 IMG_W = 384 openpose_model_hd = OpenPose(0) openpose_model_hd.preprocessor.body_estimation.model.to('cuda') parsing_model_hd = Parsing(0) densepose_model_hd = DensePose4Gradio( cfg='preprocess/detectron2/projects/DensePose/configs/densepose_rcnn_R_50_FPN_s1x.yaml', model='https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/model_final_162be9.pkl', ) category_dict = ['upperbody', 'lowerbody', 'dress'] category_dict_utils = ['upper_body', 'lower_body', 'dresses'] # #### model init >>>> config = OmegaConf.load("./configs/VITON.yaml") config.model.params.img_H = IMG_H config.model.params.img_W = IMG_W params = config.model.params model = create_model(config_path=None, config=config) model.load_state_dict(torch.load("./checkpoints/VITONHD.ckpt", map_location="cpu")["state_dict"]) model = model.cuda() model.eval() sampler = PLMSSampler(model) # #### model init <<<< def stable_viton_model_hd( batch, n_steps, ): z, cond = model.get_input(batch, params.first_stage_key) bs = z.shape[0] c_crossattn = cond["c_crossattn"][0][:bs] if c_crossattn.ndim == 4: c_crossattn = model.get_learned_conditioning(c_crossattn) cond["c_crossattn"] = [c_crossattn] uc_cross = model.get_unconditional_conditioning(bs) uc_full = {"c_concat": cond["c_concat"], "c_crossattn": [uc_cross]} uc_full["first_stage_cond"] = cond["first_stage_cond"] for k, v in batch.items(): if isinstance(v, torch.Tensor): batch[k] = v.cuda() sampler.model.batch = batch ts = torch.full((1,), 999, device=z.device, dtype=torch.long) start_code = model.q_sample(z, ts) output, _, _ = sampler.sample( n_steps, bs, (4, IMG_H // 8, IMG_W // 8), cond, x_T=start_code, verbose=False, eta=0.0, unconditional_conditioning=uc_full, ) output = model.decode_first_stage(output) output = tensor2img(output) pil_output = Image.fromarray(output) return pil_output @spaces.GPU # TODO: turn on when final upload @torch.no_grad() def process_hd(vton_img, garm_img, n_steps): model_type = 'hd' category = 0 # 0:upperbody; 1:lowerbody; 2:dress stt = time.time() print('load images... ', end='') garm_img = Image.open(garm_img).resize((IMG_W, IMG_H)) vton_img = Image.open(vton_img).resize((IMG_W, IMG_H)) print('%.2fs' % (time.time() - stt)) stt = time.time() print('get agnostic map... ', end='') keypoints = openpose_model_hd(vton_img.resize((IMG_W, IMG_H))) model_parse, _ = parsing_model_hd(vton_img.resize((IMG_W, IMG_H))) mask, mask_gray = get_mask_location(model_type, category_dict_utils[category], model_parse, keypoints) mask = mask.resize((IMG_W, IMG_H), Image.NEAREST) mask_gray = mask_gray.resize((IMG_W, IMG_H), Image.NEAREST) masked_vton_img = Image.composite(mask_gray, vton_img, mask) # agnostic map print('%.2fs' % (time.time() - stt)) stt = time.time() print('get densepose... ', end='') vton_img = vton_img.resize((IMG_W, IMG_H)) # size for densepose densepose = densepose_model_hd.execute(vton_img) # densepose # human_img_arg = _apply_exif_orientation(vton_img.resize((IMG_W, IMG_H))) # human_img_arg = convert_PIL_to_numpy(human_img_arg, format="BGR") # args = apply_net.create_argument_parser().parse_args(('show', './configs/densepose_rcnn_R_50_FPN_s1x.yaml', './ckpt/densepose/model_final_162be9.pkl', 'dp_segm', '-v', '--opts', 'MODEL.DEVICE', 'cuda')) # verbosity = getattr(args, "verbosity", None) # pose_img = args.func(args, human_img_arg) # pose_img = pose_img[:, :, ::-1] # pose_img = Image.fromarray(pose_img).resize((IMG_W, IMG_H)) print('%.2fs' % (time.time() - stt)) batch = get_batch( vton_img, garm_img, densepose, masked_vton_img, mask, IMG_H, IMG_W ) sample = stable_viton_model_hd( batch, n_steps ) return sample example_path = opj(os.path.dirname(__file__), 'examples') example_model_ps = sorted(glob(opj(example_path, "model/*"))) example_garment_ps = sorted(glob(opj(example_path, "garment/*"))) with gr.Blocks(css='style.css') as demo: gr.HTML( """

StableVITON Demo 👕👔👗

     
""" ) with gr.Row(): gr.Markdown("## Experience virtual try-on with your own images!") with gr.Row(): with gr.Column(): vton_img = gr.Image(label="Model", type="filepath", height=384, value=example_model_ps[0]) example = gr.Examples( inputs=vton_img, examples_per_page=14, examples=example_model_ps) with gr.Column(): garm_img = gr.Image(label="Garment", type="filepath", height=384, value=example_garment_ps[0]) example = gr.Examples( inputs=garm_img, examples_per_page=14, examples=example_garment_ps) with gr.Column(): result_gallery = gr.Image(label='Output', show_label=False, scale=1) # result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", scale=1) with gr.Column(): run_button = gr.Button(value="Run") # TODO: change default values (important!) # n_samples = gr.Slider(label="Images", minimum=1, maximum=4, value=1, step=1) n_steps = gr.Slider(label="Steps", minimum=20, maximum=70, value=25, step=1) # guidance_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_steps] run_button.click(fn=process_hd, inputs=ips, outputs=[result_gallery]) demo.queue().launch()