import spaces import torch import torchvision.transforms.functional as TF import numpy as np import random import os import sys from diffusers.utils import load_image from diffusers import EulerDiscreteScheduler, T2IAdapter from huggingface_hub import hf_hub_download import gradio as gr from pipeline_t2i_adapter import PhotoMakerStableDiffusionXLAdapterPipeline from face_utils import FaceAnalysis2, analyze_faces from style_template import styles from aspect_ratio_template import aspect_ratios # global variable base_model_path = 'SG161222/RealVisXL_V5.0' face_detector = FaceAnalysis2(providers=['CPUExecutionProvider', 'CUDAExecutionProvider'], allowed_modules=['detection', 'recognition']) face_detector.prepare(ctx_id=0, det_size=(640, 640)) try: if torch.cuda.is_available(): device = "cuda" elif sys.platform == "darwin" and torch.backends.mps.is_available(): device = "mps" else: device = "cpu" except: device = "cpu" MAX_SEED = np.iinfo(np.int32).max STYLE_NAMES = list(styles.keys()) DEFAULT_STYLE_NAME = "Photographic (Default)" ASPECT_RATIO_LABELS = list(aspect_ratios) DEFAULT_ASPECT_RATIO = ASPECT_RATIO_LABELS[0] enable_doodle_arg = False photomaker_ckpt = hf_hub_download(repo_id="TencentARC/PhotoMaker-V2", filename="photomaker-v2.bin", repo_type="model") if torch.cuda.is_available() and torch.cuda.is_bf16_supported(): torch_dtype = torch.bfloat16 else: torch_dtype = torch.float16 if device == "mps": torch_dtype = torch.float16 # load adapter adapter = T2IAdapter.from_pretrained( "TencentARC/t2i-adapter-sketch-sdxl-1.0", torch_dtype=torch_dtype, variant="fp16" ).to(device) pipe = PhotoMakerStableDiffusionXLAdapterPipeline.from_pretrained( base_model_path, adapter=adapter, torch_dtype=torch_dtype, use_safetensors=True, variant="fp16", ).to(device) pipe.unet = pipe.unet.to(device=device, dtype=torch_dtype) pipe.text_encoder = pipe.text_encoder.to(device=device, dtype=torch_dtype) pipe.text_encoder_2 = pipe.text_encoder_2.to(device=device, dtype=torch_dtype) pipe.vae = pipe.vae.to(device=device, dtype=torch_dtype) pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead") pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead") pipe.load_photomaker_adapter( os.path.dirname(photomaker_ckpt), subfolder="", weight_name=os.path.basename(photomaker_ckpt), trigger_word="img", pm_version="v2", ) pipe.id_encoder.to(device) pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config) # pipe.set_adapters(["photomaker"], adapter_weights=[1.0]) pipe.fuse_lora() pipe.to(device) pipe.enable_vae_slicing() pipe.enable_vae_tiling() @spaces.GPU(duration=120) def generate_image( upload_images, prompt, negative_prompt, aspect_ratio_name, style_name, num_steps, style_strength_ratio, num_outputs, guidance_scale, seed, use_doodle, sketch_image, adapter_conditioning_scale, adapter_conditioning_factor, progress=gr.Progress(track_tqdm=True) ): if use_doodle: sketch_image = sketch_image["composite"] r, g, b, a = sketch_image.split() sketch_image = a.convert("RGB") sketch_image = TF.to_tensor(sketch_image) > 0.5 # Inversion sketch_image = TF.to_pil_image(sketch_image.to(torch.float32)) adapter_conditioning_scale = adapter_conditioning_scale adapter_conditioning_factor = adapter_conditioning_factor else: adapter_conditioning_scale = 0. adapter_conditioning_factor = 0. sketch_image = None # check the trigger word image_token_id = pipe.tokenizer.convert_tokens_to_ids(pipe.trigger_word) input_ids = pipe.tokenizer.encode(prompt) if image_token_id not in input_ids: raise gr.Error(f"Cannot find the trigger word '{pipe.trigger_word}' in text prompt! Please refer to step 2️⃣") if input_ids.count(image_token_id) > 1: raise gr.Error(f"Cannot use multiple trigger words '{pipe.trigger_word}' in text prompt!") # determine output dimensions by the aspect ratio output_w, output_h = aspect_ratios[aspect_ratio_name] print(f"[Debug] Generate image using aspect ratio [{aspect_ratio_name}] => {output_w} x {output_h}") # apply the style template prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt) if upload_images is None: raise gr.Error(f"Cannot find any input face image! Please refer to step 1️⃣") input_id_images = [] for img in upload_images: input_id_images.append(load_image(img)) id_embed_list = [] for img in input_id_images: img = np.array(img) img = img[:, :, ::-1] faces = analyze_faces(face_detector, img) if len(faces) > 0: id_embed_list.append(torch.from_numpy((faces[0]['embedding']))) if len(id_embed_list) == 0: raise gr.Error(f"No face detected, please update the input face image(s)") id_embeds = torch.stack(id_embed_list) generator = torch.Generator(device=device).manual_seed(seed) print("Start inference...") print(f"[Debug] Seed: {seed}") print(f"[Debug] Prompt: {prompt}, \n[Debug] Neg Prompt: {negative_prompt}") start_merge_step = int(float(style_strength_ratio) / 100 * num_steps) if start_merge_step > 30: start_merge_step = 30 print(start_merge_step) images = pipe( prompt=prompt, width=output_w, height=output_h, input_id_images=input_id_images, negative_prompt=negative_prompt, num_images_per_prompt=num_outputs, num_inference_steps=num_steps, start_merge_step=start_merge_step, generator=generator, guidance_scale=guidance_scale, id_embeds=id_embeds, image=sketch_image, adapter_conditioning_scale=adapter_conditioning_scale, adapter_conditioning_factor=adapter_conditioning_factor, ).images return images, gr.update(visible=True) def swap_to_gallery(images): return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(visible=False) def upload_example_to_gallery(images, prompt, style, negative_prompt): return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(visible=False) def remove_back_to_files(): return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True) def change_doodle_space(use_doodle): if use_doodle: return gr.update(visible=True) else: return gr.update(visible=False) def remove_tips(): return gr.update(visible=False) def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: if randomize_seed: seed = random.randint(0, MAX_SEED) return seed def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]: p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) return p.replace("{prompt}", positive), n + ' ' + negative def get_image_path_list(folder_name): image_basename_list = os.listdir(folder_name) image_path_list = sorted([os.path.join(folder_name, basename) for basename in image_basename_list]) return image_path_list def get_example(): case = [ [ get_image_path_list('./examples/scarletthead_woman'), "instagram photo, portrait photo of a woman img, colorful, perfect face, natural skin, hard shadows, film grain", "(No style)", "(asymmetry, worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth", ], [ get_image_path_list('./examples/newton_man'), "sci-fi, closeup portrait photo of a man img wearing the sunglasses in Iron man suit, face, slim body, high quality, film grain", "(No style)", "(asymmetry, worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth", ], ] return case ### Description and style logo = r"""
PhotoMaker logo
""" title = r"""

PhotoMaker V2: Improved ID Fidelity and Better Controllability than PhotoMaker V1

""" description = r""" Official 🤗 Gradio demo for PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding.
How to use PhotoMaker V2 can be found in 🎬 this video 🎬.

For previous version of PhotoMaker, you could use our original gradio demos [PhotoMaker](https://huggingface.co/spaces/TencentARC/PhotoMaker) and [PhotoMaker-Style](https://huggingface.co/spaces/TencentARC/PhotoMaker-Style).
❗️❗️❗️[Important] Personalization steps:
1️⃣ Upload images of someone you want to customize. One image is ok, but more is better. Although we do not perform face detection, the face in the uploaded image should occupy the majority of the image.
2️⃣ Enter a text prompt, making sure to follow the class word you want to customize with the trigger word: `img`, such as: `man img` or `woman img` or `girl img`.
3️⃣ Choose your preferred style template.
4️⃣ (Optional: but new feature) Select the ‘Enable Drawing Doodle...’ option and draw on the canvas
5️⃣ Click the Submit button to start customizing. """ article = r""" If PhotoMaker V2 is helpful, please help to ⭐ the Github Repo. Thanks! [![GitHub Stars](https://img.shields.io/github/stars/TencentARC/PhotoMaker?style=social)](https://github.com/TencentARC/PhotoMaker) --- 📝 **Citation**
If our work is useful for your research, please consider citing: ```bibtex @article{li2023photomaker, title={PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding}, author={Li, Zhen and Cao, Mingdeng and Wang, Xintao and Qi, Zhongang and Cheng, Ming-Ming and Shan, Ying}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2024} } ``` 📋 **License**
Apache-2.0 LICENSE. Please refer to the [LICENSE file](https://huggingface.co/TencentARC/PhotoMaker/blob/main/LICENSE) for details. 📧 **Contact**
If you have any questions, please feel free to reach me out at zhenli1031@gmail.com. """ tips = r""" ### Usage tips of PhotoMaker 1. Upload **more photos**of the person to be customized to **improve ID fidelty**. 2. If you find that the image quality is poor when using doodle for control, you can reduce the conditioning scale and factor of the adapter. If you have any issues, leave the issue in the discussion page of the space. For a more stable (queue-free) experience, you can duplicate the space. """ # We have provided some generate examples and comparisons at: [this website](). css = ''' .gradio-container {width: 85% !important} ''' with gr.Blocks(css=css) as demo: gr.Markdown(logo) gr.Markdown(title) gr.Markdown(description) # gr.DuplicateButton( # value="Duplicate Space for private use ", # elem_id="duplicate-button", # visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", # ) with gr.Row(): with gr.Column(): files = gr.Files( label="Drag (Select) 1 or more photos of your face", file_types=["image"] ) uploaded_files = gr.Gallery(label="Your images", visible=False, columns=5, rows=1, height=200) with gr.Column(visible=False) as clear_button: remove_and_reupload = gr.ClearButton(value="Remove and upload new ones", components=files, size="sm") prompt = gr.Textbox(label="Prompt", info="Try something like 'a photo of a man/woman img', 'img' is the trigger word.", placeholder="A photo of a [man/woman img]...") style = gr.Dropdown(label="Style template", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME) aspect_ratio = gr.Dropdown(label="Output aspect ratio", choices=ASPECT_RATIO_LABELS, value=DEFAULT_ASPECT_RATIO) submit = gr.Button("Submit") enable_doodle = gr.Checkbox( label="Enable Drawing Doodle for Control", value=enable_doodle_arg, info="After enabling this option, PhotoMaker will generate content based on your doodle on the canvas, driven by the T2I-Adapter (Quality may be decreased)", ) with gr.Accordion("T2I-Adapter-Doodle (Optional)", visible=False) as doodle_space: with gr.Row(): sketch_image = gr.Sketchpad( label="Canvas", type="pil", crop_size=[1024,1024], layers=False, canvas_size=(350, 350), brush=gr.Brush(default_size=5, colors=["#000000"], color_mode="fixed") ) with gr.Group(): adapter_conditioning_scale = gr.Slider( label="Adapter conditioning scale", minimum=0.5, maximum=1, step=0.1, value=0.7, ) adapter_conditioning_factor = gr.Slider( label="Adapter conditioning factor", info="Fraction of timesteps for which adapter should be applied", minimum=0.5, maximum=1, step=0.1, value=0.8, ) with gr.Accordion(open=False, label="Advanced Options"): negative_prompt = gr.Textbox( label="Negative Prompt", placeholder="low quality", value="nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry", ) num_steps = gr.Slider( label="Number of sample steps", minimum=20, maximum=100, step=1, value=50, ) style_strength_ratio = gr.Slider( label="Style strength (%)", minimum=15, maximum=50, step=1, value=20, ) num_outputs = gr.Slider( label="Number of output images", minimum=1, maximum=4, step=1, value=2, ) guidance_scale = gr.Slider( label="Guidance scale", minimum=0.1, maximum=10.0, step=0.1, value=5, ) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Column(): gallery = gr.Gallery(label="Generated Images") usage_tips = gr.Markdown(label="Usage tips of PhotoMaker", value=tips ,visible=False) files.upload(fn=swap_to_gallery, inputs=files, outputs=[uploaded_files, clear_button, files]) remove_and_reupload.click(fn=remove_back_to_files, outputs=[uploaded_files, clear_button, files]) enable_doodle.select(fn=change_doodle_space, inputs=enable_doodle, outputs=doodle_space) input_list = [ files, prompt, negative_prompt, aspect_ratio, style, num_steps, style_strength_ratio, num_outputs, guidance_scale, seed, enable_doodle, sketch_image, adapter_conditioning_scale, adapter_conditioning_factor ] submit.click( fn=remove_tips, outputs=usage_tips, ).then( fn=randomize_seed_fn, inputs=[seed, randomize_seed], outputs=seed, queue=False, api_name=False, ).then( fn=generate_image, inputs=input_list, outputs=[gallery, usage_tips] ) gr.Examples( examples=get_example(), inputs=[files, prompt, style, negative_prompt], run_on_click=True, fn=upload_example_to_gallery, outputs=[uploaded_files, clear_button, files], ) gr.Markdown(article) demo.launch()