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| from pathlib import Path | |
| import sys | |
| from PIL import Image | |
| 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 argparse | |
| parser = argparse.ArgumentParser(description='run ootd') | |
| parser.add_argument('--gpu_id', '-g', type=int, default=0, required=False) | |
| parser.add_argument('--model_path', type=str, default="", required=True) | |
| parser.add_argument('--cloth_path', type=str, default="", required=True) | |
| parser.add_argument('--model_type', type=str, default="hd", required=False) | |
| parser.add_argument('--category', '-c', type=int, default=0, required=False) | |
| parser.add_argument('--scale', type=float, default=2.0, required=False) | |
| parser.add_argument('--step', type=int, default=20, required=False) | |
| parser.add_argument('--sample', type=int, default=4, required=False) | |
| parser.add_argument('--seed', type=int, default=-1, required=False) | |
| args = parser.parse_args() | |
| openpose_model = OpenPose(args.gpu_id) | |
| parsing_model = Parsing(args.gpu_id) | |
| category_dict = ['upperbody', 'lowerbody', 'dress'] | |
| category_dict_utils = ['upper_body', 'lower_body', 'dresses'] | |
| model_type = args.model_type # "hd" or "dc" | |
| category = args.category # 0:upperbody; 1:lowerbody; 2:dress | |
| cloth_path = args.cloth_path | |
| model_path = args.model_path | |
| image_scale = args.scale | |
| n_steps = args.step | |
| n_samples = args.sample | |
| seed = args.seed | |
| if model_type == "hd": | |
| model = OOTDiffusionHD(args.gpu_id) | |
| elif model_type == "dc": | |
| model = OOTDiffusionDC(args.gpu_id) | |
| else: | |
| raise ValueError("model_type must be \'hd\' or \'dc\'!") | |
| if __name__ == '__main__': | |
| if model_type == 'hd' and category != 0: | |
| raise ValueError("model_type \'hd\' requires category == 0 (upperbody)!") | |
| cloth_img = Image.open(cloth_path).resize((768, 1024)) | |
| model_img = Image.open(model_path).resize((768, 1024)) | |
| keypoints = openpose_model(model_img.resize((384, 512))) | |
| model_parse, _ = parsing_model(model_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, model_img, mask) | |
| masked_vton_img.save('./images_output/mask.jpg') | |
| images = model( | |
| model_type=model_type, | |
| category=category_dict[category], | |
| image_garm=cloth_img, | |
| image_vton=masked_vton_img, | |
| mask=mask, | |
| image_ori=model_img, | |
| num_samples=n_samples, | |
| num_steps=n_steps, | |
| image_scale=image_scale, | |
| seed=seed, | |
| ) | |
| image_idx = 0 | |
| for image in images: | |
| image.save('./images_output/out_' + model_type + '_' + str(image_idx) + '.png') | |
| image_idx += 1 | |