Update app.py

app.py

@@ -176,8 +176,6 @@ with gr.Blocks() as demo:
                 canvas = gr.Image(value = "./img.png", type="numpy",  label="Draw Mask", show_label=True, height=LENGTH, width=LENGTH, interactive=True)
                 result_info0 = gr.Text(label="Response")
                 segment_button  = gr.Button("Run segmentation")
-                
-
 
                 flag = gr.State(False)
 
@@ -201,16 +199,17 @@ with gr.Blocks() as demo:
                 
                 opt_flag = gr.State(0)
                 gr.Markdown("""<p style="text-align: center; font-size: 20px">Optimization settings (SD)</p>""")
-                num_tokens = gr.Number(value="5", label="num tokens to represent each object", interactive= True)
-                num_tokens_global = num_tokens
-                embedding_learning_rate = gr.Textbox(value="0.00005", label="Embedding optimization: Learning rate", interactive= True )
-                max_emb_train_steps =  gr.Number(value="15", label="embedding optimization: Training steps", interactive= True )
-                
-                diffusion_model_learning_rate = gr.Textbox(value="0.00002", label="UNet Optimization: Learning rate", interactive= True )
-                max_diffusion_train_steps = gr.Number(value="10", label="UNet Optimization: Learning rate: Training steps", interactive= True )
-                
-                train_batch_size = gr.Number(value="20", label="Batch size", interactive= True )
-                gradient_accumulation_steps=gr.Number(value="2", label="Gradient accumulation", interactive= True )
+                with gr.Accordion(label="Advanced settings", open=False):
+                    num_tokens = gr.Number(value="5", label="num tokens to represent each object", interactive= True)
+                    num_tokens_global = num_tokens
+                    embedding_learning_rate = gr.Textbox(value="0.00005", label="Embedding optimization: Learning rate", interactive= True )
+                    max_emb_train_steps =  gr.Number(value="15", label="embedding optimization: Training steps", interactive= True )
+                    
+                    diffusion_model_learning_rate = gr.Textbox(value="0.00002", label="UNet Optimization: Learning rate", interactive= True )
+                    max_diffusion_train_steps = gr.Number(value="10", label="UNet Optimization: Learning rate: Training steps", interactive= True )
+                    
+                    train_batch_size = gr.Number(value="20", label="Batch size", interactive= True )
+                    gradient_accumulation_steps=gr.Number(value="2", label="Gradient accumulation", interactive= True )
                 
                 add_button  = gr.Button("Run optimization")
                 def run_optimization_wrapper (
@@ -260,14 +259,14 @@ with gr.Blocks() as demo:
                 
                 with gr.Column():
                     gr.Markdown("""<p style="text-align: center; font-size: 20px">Editing setting (SD)</p>""")
-                    
                     tgt_prompt =  gr.Textbox(value="White bag", label="Editing: Text prompt", interactive= True )
-                    slider2 = gr.Slider(0, 20, step=1, label = 'mask id',  visible=False)
-                    #tgt_index = gr.Number(value="0", label="Editing: Object index", interactive= True )
-                    guidance_scale = gr.Textbox(value="6", label="Editing: CFG guidance scale", interactive= True )
-                    num_sampling_steps = gr.Number(value="50", label="Editing: Sampling steps", interactive= True )
-                    edge_thickness = gr.Number(value="10", label="Editing: Edge thickness", interactive= True )
-                    strength = gr.Textbox(value="0.5", label="Editing: Mask strength", interactive= True )
+                    with gr.Accordion(label="Advanced settings", open=False):
+                        slider2 = gr.Slider(0, 20, step=1, label = 'mask id',  visible=False)
+                        #tgt_index = gr.Number(value="0", label="Editing: Object index", interactive= True )
+                        guidance_scale = gr.Textbox(value="6", label="Editing: CFG guidance scale", interactive= True )
+                        num_sampling_steps = gr.Number(value="50", label="Editing: Sampling steps", interactive= True )
+                        edge_thickness = gr.Number(value="10", label="Editing: Edge thickness", interactive= True )
+                        strength = gr.Textbox(value="0.5", label="Editing: Mask strength", interactive= True )
                     
                     add_button2  = gr.Button("Run Editing (Check log for progress.)",interactive = False)
                     def run_edit_text_wrapper(