import torch from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL from ip_adapter.ip_adapter_faceid import IPAdapterFaceID from insightface.app import FaceAnalysis app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) app.prepare(ctx_id=0, det_size=(640, 640)) 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) def generate_faceid_embeddings(image): #image = cv2.imread("person.jpg") faces = app.get(image) faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0) return faceid_embeds def generate_image(image, prompt, negative_prompt): faceid_embeds = generate_faceid_embeddings(image) images = ip_model.generate( prompt=prompt, negative_prompt=negative_prompt, faceid_embeds=faceid_embeds, width=512, height=512, num_inference_steps=30 ) return images.image[0] demo = gr.Interface(fn=generate_image, inputs=[gr.Image(label="Your face"), gr.Textbox(label="Prompt"), gr.Textbox(label="Negative Prompt")], outputs=[gr.Image(label="Generated Image")]) demo.launch()