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.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) @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"""