import torch import numpy as np import random import os from diffusers.utils import load_image from diffusers import DDIMScheduler from huggingface_hub import hf_hub_download import spaces import gradio as gr from pipeline import PhotoMakerStableDiffusionXLPipeline from style_template import styles # global variable base_model_path = 'SG161222/RealVisXL_V3.0' device = "cuda" if torch.cuda.is_available() else "cpu" MAX_SEED = np.iinfo(np.int32).max STYLE_NAMES = list(styles.keys()) DEFAULT_STYLE_NAME = "Photographic (Default)" # download PhotoMaker checkpoint to cache photomaker_ckpt = hf_hub_download(repo_id="TencentARC/PhotoMaker", filename="photomaker-v1.bin", repo_type="model") pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained( base_model_path, torch_dtype=torch.bfloat16, use_safetensors=True, variant="fp16", ).to(device) pipe.load_photomaker_adapter( os.path.dirname(photomaker_ckpt), subfolder="", weight_name=os.path.basename(photomaker_ckpt), trigger_word="img" ) pipe.id_encoder.to(device) pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) # pipe.set_adapters(["photomaker"], adapter_weights=[1.0]) pipe.fuse_lora() @spaces.GPU def generate_image(upload_images, prompt, negative_prompt, style_name, num_steps, style_strength_ratio, num_outputs, guidance_scale, seed, progress=gr.Progress(track_tqdm=True)): # 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!") # apply the style template prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt) # Update nsfw negative prompt negative_prompt = f"nsfw, naked, {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)) generator = torch.Generator(device=device).manual_seed(seed) print("Start inference...") 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, 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, ).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 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 tips = r""" """ # 4. When generating realistic photos, if it's not real enough, try switching to our other gradio application [PhotoMaker-Realistic](). 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.File( label="Drag (Select) 1 or more photos of your face", file_types=["image"], file_count="multiple" ) 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) submit = gr.Button("Submit") 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]) 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=[files, prompt, negative_prompt, style, num_steps, style_strength_ratio, num_outputs, guidance_scale, seed], 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()