Update app.py

app.py

@@ -62,20 +62,17 @@ This model has been trained on a carefully selected dataset, which includes: gen
     ''')
     with gr.Row():
         with gr.Column():
-            input_image = gr.Image(sources='upload', type="pil", label="Upload", elem_id="image_upload", height=600) 
-            prompt = gr.Textbox(label="Prompt for Background")
-            run_button = gr.Button(value="Generate Background")
-
-        with gr.Column():
-            result_gallery = gr.Image(label='Output', type="pil", show_label=True, elem_id="output-img")
-
-    # Define default values for hidden parameters
-    negative_prompt = "Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers"
-    num_steps = 30
-    controlnet_conditioning_scale = 1.0
-    seed = 309005275  # None for random seed, or specify a fixed integer
+            input_image = gr.Image(sources='upload', type="pil", label="Upload", elem_id="image_upload", height=600) # None for upload, ctrl+v and webcam
+            prompt = gr.Textbox(label="Prompt")
+            negative_prompt = gr.Textbox(label="Negative prompt", value="Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers")
+            num_steps = gr.Slider(label="Number of steps", minimum=10, maximum=100, value=30, step=1)
+            controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05)
+            seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True,)
+            run_button = gr.Button(value="Generate")
             
- 
+            
+        with gr.Column():
+            result_gallery = gr.Image(label='Output', type="pil", show_label=True, elem_id="output-img") 
             # result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[1], height=600)
     ips = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed]
     run_button.click(fn=process, inputs=ips, outputs=[result_gallery])