update

app.py

@@ -49,7 +49,7 @@ ReVersion
 '''
 DETAILDESCRIPTION='''
 We propose a new task, **Relation Inversion**: Given a few exemplar images, where a relation co-exists in every image, we aim to find a relation prompt **\<R>** to capture this interaction, and apply the relation to new entities to synthesize new scenes.
-You can choose an inverted relation in the drop down menu, and use **\<R>** in your prompt for relation-specific text-to-image generation.
+Here we give several pre-trained relation prompts for you to play with. You can choose a set of exemplar images from the examples, and use **\<R>** in your prompt for relation-specific text-to-image generation.
 '''
 
 
@@ -86,7 +86,7 @@ def create_inference_demo(func: inference_fn) -> gr.Blocks:
         with gr.Row():
             with gr.Column():
                 exemplar_img = gr.Image(
-                    label='Exemplar Image',
+                    label='Exemplar Images',
                     type='pil',
                     interaction=False
                 )
@@ -103,11 +103,11 @@ def create_inference_demo(func: inference_fn) -> gr.Blocks:
                     ]
                 )
 
-                model_id = gr.Dropdown(
-                    choices=['painted_on', 'carved_by', 'inside'],
-                    value='painted_on',
-                    label='Relation',
-                    visible=True)
+                # model_id = gr.Dropdown(
+                #     choices=['painted_on', 'carved_by', 'inside'],
+                #     value='painted_on',
+                #     label='Relation',
+                #     visible=True)
                 prompt = gr.Textbox(
                     label='Prompt',
                     max_lines=1,