import os import cv2 import torch import numpy as np import gradio as gr import diffusers import huggingface_hub as hf from modules import scripts, processing, shared, devices MODELS = { 'FaceID Base': 'h94/IP-Adapter-FaceID/ip-adapter-faceid_sd15.bin', 'FaceID Plus': 'h94/IP-Adapter-FaceID/ip-adapter-faceid-plus_sd15.bin', 'FaceID Plus v2': 'h94/IP-Adapter-FaceID/ip-adapter-faceid-plusv2_sd15.bin', 'FaceID XL': 'h94/IP-Adapter-FaceID/ip-adapter-faceid_sdxl.bin' } app = None ip_model = None ip_model_name = None ip_model_tokens = None ip_model_rank = None def dependencies(): from installer import installed, install packages = [ ('insightface', 'insightface'), ('git+https://github.com/tencent-ailab/IP-Adapter.git', 'ip_adapter'), ] for pkg in packages: if not installed(pkg[1], reload=False, quiet=True): install(pkg[0], pkg[1], ignore=True) class Script(scripts.Script): def title(self): return 'FaceID' def show(self, is_img2img): return True if shared.backend == shared.Backend.DIFFUSERS else False # return signature is array of gradio components def ui(self, _is_img2img): with gr.Row(): model = gr.Dropdown(choices=list(MODELS), label='Model', value='FaceID Base') with gr.Row(visible=True): override = gr.Checkbox(label='Override sampler', value=True) cache = gr.Checkbox(label='Cache model', value=True) with gr.Row(visible=True): scale = gr.Slider(label='Strength', minimum=0.0, maximum=1.0, step=0.01, value=1.0) structure = gr.Slider(label='Structure', minimum=0.0, maximum=1.0, step=0.01, value=1.0) with gr.Row(visible=False): rank = gr.Slider(label='Rank', minimum=4, maximum=256, step=4, value=128) tokens = gr.Slider(label='Tokens', minimum=1, maximum=16, step=1, value=4) with gr.Row(): image = gr.Image(image_mode='RGB', label='Image', source='upload', type='pil', width=512) return [model, scale, image, override, rank, tokens, structure, cache] def run(self, p: processing.StableDiffusionProcessing, model, scale, image, override, rank, tokens, structure, cache): # pylint: disable=arguments-differ, unused-argument dependencies() try: import onnxruntime from insightface.app import FaceAnalysis from insightface.utils import face_align from ip_adapter.ip_adapter_faceid import IPAdapterFaceID, IPAdapterFaceIDPlus, IPAdapterFaceIDXL except Exception as e: shared.log.error(f'FaceID: {e}') return None if image is None: shared.log.error('FaceID: no init_images') return None if shared.sd_model_type != 'sd' and shared.sd_model_type != 'sdxl': shared.log.error('FaceID: base model not supported') return None global app, ip_model, ip_model_name, ip_model_tokens, ip_model_rank # pylint: disable=global-statement if app is None: shared.log.debug(f"ONNX: device={onnxruntime.get_device()} providers={onnxruntime.get_available_providers()}") app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) onnxruntime.set_default_logger_severity(3) app.prepare(ctx_id=0, det_thresh=0.5, det_size=(640, 640)) if isinstance(image, str): from modules.api.api import decode_base64_to_image image = decode_base64_to_image(image) image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) faces = app.get(image) if len(faces) == 0: shared.log.error('FaceID: no faces found') return None for face in faces: shared.log.debug(f'FaceID face: score={face.det_score:.2f} gender={"female" if face.gender==0 else "male"} age={face.age} bbox={face.bbox}') face_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0) face_image = face_align.norm_crop(image, landmark=faces[0].kps, image_size=224) # you can also segment the face ip_ckpt = MODELS[model] folder, filename = os.path.split(ip_ckpt) basename, _ext = os.path.splitext(filename) model_path = hf.hf_hub_download(repo_id=folder, filename=filename, cache_dir=shared.opts.diffusers_dir) if model_path is None: shared.log.error(f'FaceID download failed: model={model} file={ip_ckpt}') return None processing.process_init(p) if override: shared.sd_model.scheduler = diffusers.DDIMScheduler( num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, steps_offset=1, ) shortcut = None if ip_model is None or ip_model_name != model or ip_model_tokens != tokens or ip_model_rank != rank or not cache: shared.log.debug(f'FaceID load: model={model} file={ip_ckpt} tokens={tokens} rank={rank}') if 'Plus' in model: image_encoder_path = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K" ip_model = IPAdapterFaceIDPlus( sd_pipe=shared.sd_model, image_encoder_path=image_encoder_path, ip_ckpt=model_path, lora_rank=rank, num_tokens=tokens, device=devices.device, torch_dtype=devices.dtype, ) shortcut = 'v2' in model elif 'XL' in model: ip_model = IPAdapterFaceIDXL( sd_pipe=shared.sd_model, ip_ckpt=model_path, lora_rank=rank, num_tokens=tokens, device=devices.device, torch_dtype=devices.dtype, ) else: ip_model = IPAdapterFaceID( sd_pipe=shared.sd_model, ip_ckpt=model_path, lora_rank=rank, num_tokens=tokens, device=devices.device, torch_dtype=devices.dtype, ) ip_model_name = model ip_model_tokens = tokens ip_model_rank = rank else: shared.log.debug(f'FaceID cached: model={model} file={ip_ckpt} tokens={tokens} rank={rank}') # main generate dict ip_model_dict = { 'prompt': p.all_prompts[0], 'negative_prompt': p.all_negative_prompts[0], 'num_samples': p.batch_size, 'width': p.width, 'height': p.height, 'num_inference_steps': p.steps, 'scale': scale, 'guidance_scale': p.cfg_scale, 'seed': int(p.all_seeds[0]), 'faceid_embeds': face_embeds.shape, } # optional generate dict if shortcut is not None: ip_model_dict['shortcut'] = shortcut if 'Plus' in model: ip_model_dict['s_scale'] = structure ip_model_dict['face_image'] = face_image.shape shared.log.debug(f'FaceID args: {ip_model_dict}') if 'Plus' in model: ip_model_dict['face_image'] = face_image ip_model_dict['faceid_embeds'] = face_embeds # run generate images = ip_model.generate(**ip_model_dict) if not cache: ip_model = None ip_model_name = None devices.torch_gc() p.extra_generation_params["IP Adapter"] = f'{basename}:{scale}' for i, face in enumerate(faces): p.extra_generation_params[f"FaceID {i} score"] = f'{face.det_score:.2f}' p.extra_generation_params[f"FaceID {i} gender"] = "female" if face.gender==0 else "male" p.extra_generation_params[f"FaceID {i} age"] = face.age processed = processing.Processed( p, images_list=images, seed=p.seed, subseed=p.subseed, index_of_first_image=0, ) processed.info = processed.infotext(p, 0) processed.infotexts = [processed.info] return processed