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| from adaface.adaface_wrapper import AdaFaceWrapper | |
| import torch | |
| #import torch.nn.functional as F | |
| from PIL import Image | |
| import numpy as np | |
| import os, argparse, glob, re | |
| def save_images(images, num_images_per_row, subject_name, prompt, perturb_std, save_dir = "samples-ada"): | |
| if num_images_per_row > len(images): | |
| num_images_per_row = len(images) | |
| os.makedirs(save_dir, exist_ok=True) | |
| num_columns = int(np.ceil(len(images) / num_images_per_row)) | |
| # Save 4 images as a grid image in save_dir | |
| grid_image = Image.new('RGB', (512 * num_images_per_row, 512 * num_columns)) | |
| for i, image in enumerate(images): | |
| image = image.resize((512, 512)) | |
| grid_image.paste(image, (512 * (i % num_images_per_row), 512 * (i // num_images_per_row))) | |
| prompt_sig = prompt.replace(" ", "_").replace(",", "_") | |
| grid_filepath = os.path.join(save_dir, f"{subject_name}-{prompt_sig}-perturb{perturb_std:.02f}.png") | |
| if os.path.exists(grid_filepath): | |
| grid_count = 2 | |
| grid_filepath = os.path.join(save_dir, f'{subject_name}-{prompt_sig}-perturb{perturb_std:.02f}-{grid_count}.png') | |
| while os.path.exists(grid_filepath): | |
| grid_count += 1 | |
| grid_filepath = os.path.join(save_dir, f'{subject_name}-{prompt_sig}-perturb{perturb_std:.02f}-{grid_count}.png') | |
| grid_image.save(grid_filepath) | |
| print(f"Saved to {grid_filepath}") | |
| def seed_everything(seed): | |
| np.random.seed(seed) | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed_all(seed) | |
| torch.backends.cudnn.deterministic = True | |
| torch.backends.cudnn.benchmark = False | |
| os.environ["PL_GLOBAL_SEED"] = str(seed) | |
| def parse_args(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--pipeline", type=str, default="text2img", | |
| choices=["text2img", "img2img", "text2img3", "flux"], | |
| help="Type of pipeline to use (default: txt2img)") | |
| parser.add_argument("--base_model_path", type=str, default=None, | |
| help="Type of checkpoints to use (default: None, using the official model)") | |
| parser.add_argument('--adaface_ckpt_paths', type=str, nargs="+", | |
| default=['models/adaface/subjects-celebrity2024-05-16T17-22-46_zero3-ada-30000.pt']) | |
| parser.add_argument("--adaface_encoder_types", type=str, nargs="+", default=["arc2face"], | |
| choices=["arc2face", "consistentID"], help="Type(s) of the ID2Ada prompt encoders") | |
| # If adaface_encoder_cfg_scales is not specified, the weights will be set to 6.0 (consistentID) and 1.0 (arc2face). | |
| parser.add_argument('--adaface_encoder_cfg_scales', type=float, nargs="+", default=None, | |
| help="CFG scales of output embeddings of the ID2Ada prompt encoders") | |
| parser.add_argument("--main_unet_filepath", type=str, default=None, | |
| help="Path to the checkpoint of the main UNet model, if you want to replace the default UNet within --base_model_path") | |
| parser.add_argument("--extra_unet_dirpaths", type=str, nargs="*", | |
| default=['models/ensemble/rv4-unet', 'models/ensemble/ar18-unet'], | |
| help="Extra paths to the checkpoints of the UNet models") | |
| parser.add_argument('--unet_weights', type=float, nargs="+", default=[4, 2, 1], | |
| help="Weights for the UNet models") | |
| parser.add_argument("--subject", type=str) | |
| parser.add_argument("--example_image_count", type=int, default=-1, help="Number of example images to use") | |
| parser.add_argument("--out_image_count", type=int, default=4, help="Number of images to generate") | |
| parser.add_argument("--prompt", type=str, default="a woman z in superman costume") | |
| parser.add_argument("--noise", dest='perturb_std', type=float, default=0) | |
| parser.add_argument("--randface", action="store_true") | |
| parser.add_argument("--scale", dest='guidance_scale', type=float, default=4, | |
| help="Guidance scale for the diffusion model") | |
| parser.add_argument("--id_cfg_scale", type=float, default=6, | |
| help="CFG scale when generating the identity embeddings") | |
| parser.add_argument("--subject_string", | |
| type=str, default="z", | |
| help="Subject placeholder string used in prompts to denote the concept.") | |
| parser.add_argument("--num_images_per_row", type=int, default=4, | |
| help="Number of images to display in a row in the output grid image.") | |
| parser.add_argument("--num_inference_steps", type=int, default=50, | |
| help="Number of DDIM inference steps") | |
| parser.add_argument("--device", type=str, default="cuda", help="Device to run the model on") | |
| parser.add_argument("--seed", type=int, default=42, | |
| help="the seed (for reproducible sampling). Set to -1 to disable.") | |
| args = parser.parse_args() | |
| return args | |
| if __name__ == "__main__": | |
| args = parse_args() | |
| if args.seed != -1: | |
| seed_everything(args.seed) | |
| if re.match(r"^\d+$", args.device): | |
| args.device = f"cuda:{args.device}" | |
| print(f"Using device {args.device}") | |
| if args.pipeline not in ["text2img", "img2img"]: | |
| args.extra_unet_dirpaths = None | |
| args.unet_weights = None | |
| adaface = AdaFaceWrapper(args.pipeline, args.base_model_path, | |
| args.adaface_encoder_types, args.adaface_ckpt_paths, | |
| args.adaface_encoder_cfg_scales, | |
| args.subject_string, args.num_inference_steps, | |
| unet_types=None, | |
| main_unet_filepath=args.main_unet_filepath, | |
| extra_unet_dirpaths=args.extra_unet_dirpaths, | |
| unet_weights=args.unet_weights, device=args.device) | |
| if not args.randface: | |
| image_folder = args.subject | |
| if image_folder.endswith("/"): | |
| image_folder = image_folder[:-1] | |
| if os.path.isfile(image_folder): | |
| # Get the second to the last part of the path | |
| subject_name = os.path.basename(os.path.dirname(image_folder)) | |
| image_paths = [image_folder] | |
| else: | |
| subject_name = os.path.basename(image_folder) | |
| image_types = ["*.jpg", "*.png", "*.jpeg"] | |
| alltype_image_paths = [] | |
| for image_type in image_types: | |
| # glob returns the full path. | |
| image_paths = glob.glob(os.path.join(image_folder, image_type)) | |
| if len(image_paths) > 0: | |
| alltype_image_paths.extend(image_paths) | |
| # Filter out images of "*_mask.png" | |
| alltype_image_paths = [image_path for image_path in alltype_image_paths if "_mask.png" not in image_path] | |
| # image_paths contain at most args.example_image_count full image paths. | |
| if args.example_image_count > 0: | |
| image_paths = alltype_image_paths[:args.example_image_count] | |
| else: | |
| image_paths = alltype_image_paths | |
| else: | |
| subject_name = None | |
| image_paths = None | |
| image_folder = None | |
| subject_name = "randface-" + str(torch.seed()) if args.randface else subject_name | |
| rand_init_id_embs = torch.randn(1, 512) | |
| init_id_embs = rand_init_id_embs if args.randface else None | |
| noise = torch.randn(args.out_image_count, 4, 64, 64).cuda() | |
| # args.perturb_std: the *relative* std of the noise added to the face embeddings. | |
| # A noise level of 0.08 could change gender, but 0.06 is usually safe. | |
| # adaface_subj_embs is not used. It is generated for the purpose of updating the text encoder (within this function call). | |
| adaface_subj_embs = \ | |
| adaface.prepare_adaface_embeddings(image_paths, init_id_embs, | |
| perturb_at_stage='img_prompt_emb', | |
| perturb_std=args.perturb_std, update_text_encoder=True) | |
| images = adaface(noise, args.prompt, None, 'append', args.guidance_scale, args.out_image_count, verbose=True) | |
| save_images(images, args.num_images_per_row, subject_name, f"guide{args.guidance_scale}", args.perturb_std) | |