Create app.py

app.py

@@ -0,0 +1,52 @@
+import cv2
+from insightface.app import FaceAnalysis
+import torch
+from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL
+from PIL import Image
+from ip_adapter.ip_adapter_faceid import IPAdapterFaceID
+
+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()