import sys sys.path.append('./') from adaface.adaface_wrapper import AdaFaceWrapper import torch import numpy as np import random import gradio as gr import spaces import argparse parser = argparse.ArgumentParser() parser.add_argument("--adaface_encoder_types", type=str, nargs="+", default=["consistentID", "arc2face"], choices=["arc2face", "consistentID"], help="Type(s) of the ID2Ada prompt encoders") parser.add_argument('--adaface_ckpt_path', type=str, default='models/adaface/VGGface2_HQ_masks2024-10-14T16-09-24_zero3-ada-3500.pt', help="Paths to the checkpoints 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="Scales for the ID2Ada prompt encoders") parser.add_argument("--enabled_encoders", type=str, nargs="+", default=None, choices=["arc2face", "consistentID"], help="List of enabled encoders (among the list of adaface_encoder_types). Default: None (all enabled)") parser.add_argument('--model_style_type', type=str, default='realistic', choices=["realistic", "anime", "photorealistic"], help="Type of the base model") parser.add_argument('--extra_unet_dirpaths', type=str, nargs="*", default=[], help="Extra paths to the checkpoints of the UNet models") parser.add_argument('--unet_weights', type=float, nargs="+", default=[1], help="Weights for the UNet models") parser.add_argument("--guidance_scale", type=float, default=8.0, help="The guidance scale for the diffusion model. Default: 8.0") parser.add_argument("--do_neg_id_prompt_weight", type=float, default=0.0, help="The weight of added ID prompt embeddings into the negative prompt. Default: 0, disabled.") parser.add_argument('--gpu', type=int, default=None) parser.add_argument('--ip', type=str, default="0.0.0.0") args = parser.parse_args() model_style_type2base_model_path = { "realistic": "models/rv51/realisticVisionV51_v51VAE_dste8.safetensors", "anime": "models/aingdiffusion/aingdiffusion_v170_ar.safetensors", "photorealistic": "models/sar/sar.safetensors" # LDM format. Needs to be converted. } base_model_path = model_style_type2base_model_path[args.model_style_type] # global variable MAX_SEED = np.iinfo(np.int32).max device = "cuda" if args.gpu is None else f"cuda:{args.gpu}" print(f"Device: {device}") global adaface adaface = AdaFaceWrapper(pipeline_name="text2img", base_model_path=base_model_path, adaface_encoder_types=args.adaface_encoder_types, adaface_ckpt_paths=args.adaface_ckpt_path, adaface_encoder_cfg_scales=args.adaface_encoder_cfg_scales, enabled_encoders=args.enabled_encoders, unet_types=None, extra_unet_dirpaths=args.extra_unet_dirpaths, unet_weights=args.unet_weights, device='cpu') def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: if randomize_seed: seed = random.randint(0, MAX_SEED) return seed def swap_to_gallery(images): # Update uploaded_files_gallery, show files, hide clear_button_column # Or: # Update uploaded_init_img_gallery, show init_img_files, hide init_clear_button_column return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(value=images, visible=False) def remove_back_to_files(): # Hide uploaded_files_gallery, show clear_button_column, hide files, reset init_img_selected_idx # Or: # Hide uploaded_init_img_gallery, hide init_clear_button_column, show init_img_files, reset init_img_selected_idx return gr.update(visible=False), gr.update(visible=False), gr.update(value=None, visible=True) @spaces.GPU def generate_image(image_paths, guidance_scale, do_neg_id_prompt_weight, perturb_std, num_images, prompt, negative_prompt, enhance_face, seed, progress=gr.Progress(track_tqdm=True)): global adaface adaface.to(device) if image_paths is None or len(image_paths) == 0: raise gr.Error(f"Cannot find any input face image! Please upload a face image.") if prompt is None: prompt = "" adaface_subj_embs = \ adaface.prepare_adaface_embeddings(image_paths=image_paths, face_id_embs=None, avg_at_stage='id_emb', perturb_at_stage='img_prompt_emb', perturb_std=perturb_std, update_text_encoder=True) if adaface_subj_embs is None: raise gr.Error(f"Failed to detect any faces! Please try with other images") # Sometimes the pipeline is on CPU, although we've put it on CUDA (due to some offloading mechanism). # Therefore we set the generator to the correct device. generator = torch.Generator(device=device).manual_seed(seed) print(f"Manual seed: {seed}. do_neg_id_prompt_weight: {do_neg_id_prompt_weight}.") # Generate two images each time for the user to select from. noise = torch.randn(num_images, 3, 512, 512, device=device, generator=generator) #print(noise.abs().sum()) # samples: A list of PIL Image instances. if enhance_face and "face portrait" not in prompt: if "portrait" in prompt: # Enhance the face features by replacing "portrait" with "face portrait". prompt = prompt.replace("portrait", "face portrait") else: prompt = "face portrait, " + prompt generator = torch.Generator(device=adaface.pipeline._execution_device).manual_seed(seed) samples = adaface(noise, prompt, negative_prompt, do_neg_id_prompt_weight=do_neg_id_prompt_weight, guidance_scale=guidance_scale, out_image_count=num_images, generator=generator, verbose=True) return samples def check_prompt_and_model_type(prompt, model_style_type): global adaface model_style_type = model_style_type.lower() base_model_path = model_style_type2base_model_path[model_style_type] # If the base model type is changed, reload the model. if model_style_type != args.model_style_type: adaface = AdaFaceWrapper(pipeline_name="text2img", base_model_path=base_model_path, adaface_encoder_types=args.adaface_encoder_types, adaface_ckpt_paths=args.adaface_ckpt_path, device='cpu') # Update base model type. args.model_style_type = model_style_type if not prompt: raise gr.Error("Prompt cannot be blank") ### Description title = r"""

AdaFace: A Versatile Face Encoder for Zero-Shot Diffusion Model Personalization

""" description = r""" Official demo for our working paper AdaFace: A Versatile Face Encoder for Zero-Shot Diffusion Model Personalization.
❗️**What's New**❗️ - Support switching between two model styles: **Realistic** and **Anime**. - If you just changed the model style, the first image/video generation will take extra 20~30 seconds for loading new model weight. ❗️**Tips**❗️ 1. Upload one or more images of a person. If multiple faces are detected, we use the largest one. 2. Check "Enhance Face" to highlight fine facial features. 3. If the face dominates the image, try increasing 'Weight of ID prompt in the negative prompt'. 4. AdaFace Text-to-Video: AdaFace-Animate Hugging Face Spaces **TODO:** - ControlNet integration. """ css = ''' .gradio-container {width: 95% !important}, .custom-gallery { height: 800px; width: 100%; margin: 10px auto; padding: 10px; overflow-y: auto; } ''' with gr.Blocks(css=css, theme=gr.themes.Origin()) as demo: # description gr.Markdown(title) gr.Markdown(description) with gr.Row(): with gr.Column(): # upload face image # img_file = gr.Image(label="Upload a photo with a face", type="filepath") img_files = gr.File( label="Drag / Select 1 or more photos of a person's face", file_types=["image"], file_count="multiple" ) uploaded_files_gallery = gr.Gallery(label="Subject images", visible=False, columns=3, rows=1, height=300) with gr.Column(visible=False) as clear_button_column: remove_and_reupload = gr.ClearButton(value="Remove and upload subject images", components=img_files, size="sm") prompt = gr.Dropdown(label="Prompt", info="Try something like 'walking on the beach'. If the face is not in focus, try checking 'enhance face'.", value="portrait, ((best quality)), ((masterpiece)), ((realistic)), highlighted hair, futuristic silver armor suit, confident stance, high-resolution, living room, smiling, head tilted, perfect smooth skin", allow_custom_value=True, filterable=False, choices=[ "portrait, ((best quality)), ((masterpiece)), ((realistic)), highlighted hair, futuristic silver armor suit, confident stance, high-resolution, living room, smiling, head tilted, perfect smooth skin", "portrait, walking on the beach, sunset, orange sky", "portrait, in a white apron and chef hat, garnishing a gourmet dish", "portrait, dancing pose among folks in a park, waving hands", "portrait, in iron man costume, the sky ablaze with hues of orange and purple", "portrait, jedi wielding a lightsaber, star wars, eye level shot", "portrait, playing guitar on a boat, ocean waves", "portrait, with a passion for reading, curled up with a book in a cozy nook near a window", "portrait, running pose in a park, eye level shot", "portrait, in superman costume, the sky ablaze with hues of orange and purple" ]) enhance_face = gr.Checkbox(label="Enhance face", value=False, info="Enhance the face features by prepending 'face portrait' to the prompt") submit = gr.Button("Submit", variant="primary") negative_prompt = gr.Textbox( label="Negative Prompt", value="flaws in the eyes, flaws in the face, lowres, non-HDRi, low quality, worst quality, artifacts, noise, text, watermark, glitch, mutated, ugly, disfigured, hands, partially rendered objects, partially rendered eyes, deformed eyeballs, cross-eyed, blurry, mutation, duplicate, out of frame, cropped, mutilated, bad anatomy, deformed, bad proportions, nude, naked, nsfw, topless, bare breasts", ) guidance_scale = gr.Slider( label="Guidance scale", minimum=1.0, maximum=12.0, step=1.0, value=args.guidance_scale, ) do_neg_id_prompt_weight = gr.Slider( label="Weight of ID prompt in the negative prompt", minimum=0.0, maximum=0.3, step=0.1, value=args.do_neg_id_prompt_weight, visible=True, ) model_style_type = gr.Dropdown( label="Base Model Style Type", info="Switching the base model type will take 10~20 seconds to reload the model", value=args.model_style_type.capitalize(), choices=["Realistic", "Anime", "Photorealistic"], allow_custom_value=False, filterable=False, ) perturb_std = gr.Slider( label="Std of noise added to input (may help stablize face embeddings)", minimum=0.0, maximum=0.05, step=0.025, value=0.0, visible=False, ) num_images = gr.Slider( label="Number of output images", minimum=1, maximum=6, step=1, value=4, ) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True, info="Uncheck for reproducible results") with gr.Column(): out_gallery = gr.Gallery(label="Generated Images", interactive=False, columns=2, rows=2, height=800, elem_classes="custom-gallery") img_files.upload(fn=swap_to_gallery, inputs=img_files, outputs=[uploaded_files_gallery, clear_button_column, img_files]) remove_and_reupload.click(fn=remove_back_to_files, outputs=[uploaded_files_gallery, clear_button_column, img_files]) submit.click(fn=check_prompt_and_model_type, inputs=[prompt, model_style_type],outputs=None).success( fn=randomize_seed_fn, inputs=[seed, randomize_seed], outputs=seed, queue=False, api_name=False, ).then( fn=generate_image, inputs=[img_files, guidance_scale, do_neg_id_prompt_weight, perturb_std, num_images, prompt, negative_prompt, enhance_face, seed], outputs=[out_gallery] ) demo.launch(share=True, server_name=args.ip, ssl_verify=False)