import torch import spaces from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL from ip_adapter.ip_adapter_faceid import IPAdapterFaceID from huggingface_hub import hf_hub_download from insightface.app import FaceAnalysis import gradio as gr import cv2 base_model_path = "SG161222/Realistic_Vision_V4.0_noVAE" vae_model_path = "stabilityai/sd-vae-ft-mse" ip_ckpt = hf_hub_download(repo_id='h94/IP-Adapter-FaceID', filename="ip-adapter-faceid_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, #feature_extractor=None, #safety_checker=None ) ip_model = IPAdapterFaceID(pipe, ip_ckpt, device) @spaces.GPU def generate_image(images, prompt, negative_prompt): pipe.to(device) app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) app.prepare(ctx_id=0, det_size=(640, 640)) faceid_all_embeds = [] 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) average_embedding = torch.mean(torch.stack(faceid_all_embeds, dim=0), dim=0) image = ip_model.generate( prompt=prompt, negative_prompt=negative_prompt, faceid_embeds=average_embedding, width=512, height=512, num_inference_steps=30 ) print(image) return image demo = gr.Interface(fn=generate_image, inputs=[gr.Files(label="Drag 1 or more photos of your face", file_types="image"),gr.Textbox(label="Prompt"), gr.Textbox(label="Negative Prompt")], 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()