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, noise_level, 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}-noise{noise_level:.02f}.png") if os.path.exists(grid_filepath): grid_count = 2 grid_filepath = os.path.join(save_dir, f'{subject_name}-{prompt_sig}-noise{noise_level:.02f}-{grid_count}.jpg') while os.path.exists(grid_filepath): grid_count += 1 grid_filepath = os.path.join(save_dir, f'{subject_name}-{prompt_sig}-noise{noise_level:.02f}-{grid_count}.jpg') 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("--base_model_path", type=str, default='runwayml/stable-diffusion-v1-5', help="Type of checkpoints to use (default: SD 1.5)") parser.add_argument("--embman_ckpt", type=str, required=True, help="Path to the checkpoint of the embedding manager") parser.add_argument("--subject", type=str, required=True) 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='noise_level', 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=1, 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_vectors", type=int, default=16, help="Number of vectors used to represent the subject.") 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}") adaface = AdaFaceWrapper("text2img", args.base_model_path, args.embman_ckpt, args.device, args.subject_string, args.num_vectors, args.num_inference_steps) 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_face_embs = torch.randn(1, 512) pre_face_embs = rand_face_embs if args.randface else None noise = torch.randn(args.out_image_count, 4, 64, 64).cuda() # args.noise_level: 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.generate_adaface_embeddings(image_paths, image_folder, pre_face_embs, args.randface, out_id_embs_scale=args.id_cfg_scale, noise_level=args.noise_level, update_text_encoder=True) images = adaface(noise, args.prompt, 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.noise_level)