import torch import spaces from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL from ip_adapter.ip_adapter_faceid import IPAdapterFaceID, IPAdapterFaceIDPlus from huggingface_hub import hf_hub_download from insightface.app import FaceAnalysis from insightface.utils import face_align import gradio as gr import cv2 base_model_path = "SG161222/Realistic_Vision_V4.0_noVAE" vae_model_path = "stabilityai/sd-vae-ft-mse" image_encoder_path = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K" ip_ckpt = hf_hub_download(repo_id="h94/IP-Adapter-FaceID", filename="ip-adapter-faceid_sd15.bin", repo_type="model") ip_plus_ckpt = hf_hub_download(repo_id="h94/IP-Adapter-FaceID", filename="ip-adapter-faceid-plusv2_sd15.bin", repo_type="model") device = "cuda" noise_scheduler = 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, ) vae = AutoencoderKL.from_pretrained(vae_model_path).to(dtype=torch.float16) pipe = StableDiffusionPipeline.from_pretrained( base_model_path, torch_dtype=torch.float16, scheduler=noise_scheduler, vae=vae, ) ip_model = IPAdapterFaceID(pipe, ip_ckpt, device) ip_model_plus = IPAdapterFaceIDPlus(pipe, image_encoder_path, ip_plus_ckpt, device) @spaces.GPU(enable_queue=True) def generate_image(images, prompt, negative_prompt, preserve_face_structure, progress=gr.Progress(track_tqdm=True)): pipe.to(device) app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) app.prepare(ctx_id=0, det_size=(640, 640)) faceid_all_embeds = [] first_iteration = True for image in images: face = cv2.imread(image) faces = app.get(face) faceid_embed = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0) faceid_all_embeds.append(faceid_embed) if(first_iteration): face_image = face_align.norm_crop(face, landmark=faces[0].kps, image_size=224) # you can also segment the face first_iteration = False average_embedding = torch.mean(torch.stack(faceid_all_embeds, dim=0), dim=0) if(not preserve_face_structure): image = ip_model.generate( prompt=prompt, negative_prompt=negative_prompt, faceid_embeds=average_embedding, width=512, height=512, num_inference_steps=30 ) else: image = ip_model_plus.generate( prompt=prompt, negative_prompt=negative_prompt, faceid_embeds=average_embedding, face_image=face_image, shortcut=True, s_scale=1.5, width=512, height=512, num_inference_steps=30 ) print(image) return image css = ''' h1{margin-bottom: 0 !important} ''' demo = gr.Interface( css=css, fn=generate_image, inputs=[ gr.Files( label="Drag 1 or more photos of your face", file_types=["image"] ), gr.Textbox(label="Prompt", info="Try something like 'a photo of a man/woman/person'", placeholder="A photo of a [man/woman/person]..."), gr.Textbox(label="Negative Prompt", placeholder="low quality"), gr.Checkbox(label="Preserve Face Structure", value=False), ], outputs=[gr.Gallery(label="Generated Image")], title="IP-Adapter-FaceID demo", description="Demo for the [h94/IP-Adapter-FaceID model](https://huggingface.co/h94/IP-Adapter-FaceID)", allow_flagging=False, ) demo.launch()