Update app.py

app.py

@@ -29,6 +29,12 @@ controlnet = ControlNetModel.from_pretrained(
     torch_dtype=torch.float16
 )
 
+def check_use_custom_or_no(value):
+    if value is True:
+        return gr.update(visible=True)
+    else:
+        return gr.update(visible=False)
+
 def get_files(file_paths):
     last_files = {}  # Dictionary to store the last file for each path
 
@@ -243,41 +249,10 @@ with gr.Blocks(css=css) as demo:
 <p style="text-align: center;">Use StableDiffusion XL with <a href="https://huggingface.co/collections/diffusers/sdxl-controlnets-64f9c35846f3f06f5abe351f">Diffusers' SDXL ControlNets</a></p>
 
         """)
-        
-        image_in = gr.Image(source="upload", type="filepath")
-        
-        with gr.Row():
-            
-            with gr.Column():
-                with gr.Group():
-                    prompt = gr.Textbox(label="Prompt")
-                    negative_prompt = gr.Textbox(label="Negative prompt", value="extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured")
-                with gr.Group():
-                    guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=7.5)
-                    inf_steps = gr.Slider(label="Inference Steps", minimum="25", maximum="50", step=1, value=25)
-                    custom_lora_weight = gr.Slider(label="Custom model weights", minimum=0.1, maximum=0.9, step=0.1, value=0.9)
 
-            with gr.Column():
-                with gr.Group():
-                    preprocessor = gr.Dropdown(label="Preprocessor", choices=["canny"], value="canny", interactive=False, info="For the moment, only canny is available")  
-                    controlnet_conditioning_scale = gr.Slider(label="Controlnet conditioning Scale", minimum=0.1, maximum=0.9, step=0.01, value=0.5)
-                with gr.Group():
-                    seed = gr.Slider(
-                        label="Seed",
-                        info = "-1 denotes a random seed",
-                        minimum=-1,
-                        maximum=423538377342,
-                        step=1,
-                        value=-1
-                    )
-                    last_used_seed = gr.Number(
-                        label = "Last used seed",
-                        info = "the seed used in the last generation",
-                    )
-        
         use_custom_model = gr.Checkbox(label="Use a public custom model ?(optional)", value=False, info="To use a private model, you'll prefer to duplicate the space with your own access token.")
         
-        with gr.Box(visible=True) as custom_model_box:
+        with gr.Box(visible=False) as custom_model_box:
             with gr.Row():
                 with gr.Column():
                     if not is_shared_ui:
@@ -320,10 +295,49 @@ with gr.Blocks(css=css) as demo:
                         )
                     trigger_word = gr.Textbox(label="Trigger word", interactive=False, visible=False)
         
+        image_in = gr.Image(source="upload", type="filepath")
+        
+        with gr.Row():
+            
+            with gr.Column():
+                with gr.Group():
+                    prompt = gr.Textbox(label="Prompt")
+                    negative_prompt = gr.Textbox(label="Negative prompt", value="extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured")
+                with gr.Group():
+                    guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=7.5)
+                    inf_steps = gr.Slider(label="Inference Steps", minimum="25", maximum="50", step=1, value=25)
+                    custom_lora_weight = gr.Slider(label="Custom model weights", minimum=0.1, maximum=0.9, step=0.1, value=0.9)
+
+            with gr.Column():
+                with gr.Group():
+                    preprocessor = gr.Dropdown(label="Preprocessor", choices=["canny"], value="canny", interactive=False, info="For the moment, only canny is available")  
+                    controlnet_conditioning_scale = gr.Slider(label="Controlnet conditioning Scale", minimum=0.1, maximum=0.9, step=0.01, value=0.5)
+                with gr.Group():
+                    seed = gr.Slider(
+                        label="Seed",
+                        info = "-1 denotes a random seed",
+                        minimum=-1,
+                        maximum=423538377342,
+                        step=1,
+                        value=-1
+                    )
+                    last_used_seed = gr.Number(
+                        label = "Last used seed",
+                        info = "the seed used in the last generation",
+                    )
+        
+        
+        
         submit_btn = gr.Button("Submit")
         
         result = gr.Image(label="Result")
-
+    
+    use_custom_model.change(
+        fn = check_custom_or_no,
+        inputs =[use_custom_model],
+        outputs = [custom_model_box],
+        queue = False
+    )
     custom_model.blur(
         fn=custom_model_changed,
         inputs = [custom_model, previous_model],