adaface/adaface_infer.py

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)