Update app.py

app.py

@@ -83,8 +83,8 @@ if not isinstance(eva_transform_std, (list, tuple)):
 eva_transform_mean = eva_transform_mean
 eva_transform_std = eva_transform_std
 
-face_main_model = FaceAnalysis(name='antelopev2', root=os.path.join(model_path, "face_encoder"), providers=['CUDAExecutionProvider'])
-handler_ante = insightface.model_zoo.get_model(f'{model_path}/face_encoder/models/antelopev2/glintr100.onnx', providers=['CUDAExecutionProvider'])
+face_main_model = FaceAnalysis(name='antelopev2', root=os.path.join(model_path, "face_encoder"), providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+handler_ante = insightface.model_zoo.get_model(f'{model_path}/face_encoder/models/antelopev2/glintr100.onnx', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
 face_main_model.prepare(ctx_id=0, det_size=(640, 640))
 handler_ante.prepare(ctx_id=0)
     
@@ -227,7 +227,7 @@ with gr.Blocks() as demo:
         with gr.Column():
             with gr.Accordion("IPT2V: Face Input", open=True):
                 image_input = gr.Image(label="Input Image (should contain clear face)")
-                prompt = gr.Textbox(label="Prompt (Less than 200 Words)", placeholder="Enter your prompt here", lines=5)
+                prompt = gr.Textbox(label="Prompt (Less than 200 Words)", placeholder="Enter your prompt here. ConsisID has high requirements for prompt quality. You can use GPT-4o to refine the input text prompt, example can be found on our github.", lines=5)
             with gr.Accordion("Examples", open=False):
                 examples_component_images = gr.Examples(
                     examples_images,