Update app.py

app.py

@@ -31,7 +31,7 @@ generator = torch.Generator(device="cuda")
 
 #pipe.enable_model_cpu_offload()
 
-def infer(use_custom_model, model_name, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, seed):
+def infer(use_custom_model, model_name, custom_lora_weight, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, seed):
     if use_custom_model:
         custom_model = model_name
 
@@ -52,7 +52,7 @@ def infer(use_custom_model, model_name, image_in, prompt, negative_prompt, prepr
         image = Image.fromarray(image)
 
     if use_custom_model:
-        lora_scale= 0.9
+        lora_scale=custom_lora_weight
 
         images = pipe(
             prompt, 
@@ -93,8 +93,7 @@ with gr.Blocks(css=css) as demo:
 Use StableDiffusion XL with ControlNet pretrained LoRas 
 
         """)
-        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.")
-        model_name = gr.Textbox(label="Custom Model to use", placeholder="username/my_custom_public_model")
+        
         image_in = gr.Image(source="upload", type="filepath")
         with gr.Row():
             with gr.Column():
@@ -105,13 +104,15 @@ Use StableDiffusion XL with ControlNet pretrained LoRas
                 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, type="float")
                 seed = gr.Slider(label="seed", minimum=0, maximum=500000, step=1, value=42)
-
+        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.")
+        model_name = gr.Textbox(label="Custom Model to use", placeholder="username/my_custom_public_model")
+        custom_lora_weight = gr.Slider(label="Custom weights", minimum=0.1, maximum=0.9, step=0.1, value=0.9)
         submit_btn = gr.Button("Submit")
         result = gr.Image(label="Result")
 
     submit_btn.click(
         fn = infer,
-        inputs = [use_custom_model, model_name, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, seed],
+        inputs = [use_custom_model, model_name, custom_lora_weight, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, seed],
         outputs = [result]
     )