Update app.py

app.py

@@ -48,7 +48,7 @@ def generate_images(prompt, img_filepath, negative_prompt="monochrome, lowres, b
     faces = app.get(image)
 
     faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)
-    face_image = face_align.norm_crop(image, landmark=faces[0].kps, image_size=200) 
+    face_image = face_align.norm_crop(image, landmark=faces[0].kps, image_size=224) 
     images = ip_model.generate(
         prompt=prompt, negative_prompt=negative_prompt, face_image=face_image, faceid_embeds=faceid_embeds,
         num_samples=n_images, width=512, height=512, num_inference_steps=num_inference_steps, seed=seed,
@@ -59,7 +59,7 @@ def generate_images(prompt, img_filepath, negative_prompt="monochrome, lowres, b
 if __name__ == "__main__":
     ip_model = get_ip_model()
     app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
-    app.prepare(ctx_id=0, det_size=(512, 512), det_thresh=0.2)
+    app.prepare(ctx_id=0, det_size=(640, 640), det_thresh=0.2)
 
 
     with gr.Blocks() as demo:
@@ -85,7 +85,7 @@ if __name__ == "__main__":
             with gr.Column():
                 demo_outputs = []
                 demo_outputs.append(gr.Image(label='generated image'))
-                demo_outputs.append(gr.Image(label='detected face', height=200, width=200))
+                demo_outputs.append(gr.Image(label='detected face', height=224, width=224))
         btn.click(generate_images, inputs=demo_inputs, outputs=demo_outputs)
         sample_prompts = [
             'A wizard casting spells in a coffee shop',