Update app.py

app.py

@@ -36,7 +36,7 @@ def infer(use_custom_model, model_name, image_in, prompt, preprocessor, controln
         custom_model = model_name
 
         # This is where you load your trained weights
-        pipe.load_lora_weights(custom_model, weight_name="pytorch_lora_weights.safetensors", use_auth_token=True)
+        pipe.load_lora_weights(custom_model, use_auth_token=True)
     
     prompt = prompt
     negative_prompt = "extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured"
@@ -83,6 +83,7 @@ css="""
 #col-container{
     margin: 0 auto;
     max-width: 680px;
+    text-align: center;
 }
 """
 with gr.Blocks(css=css) as demo:
@@ -92,11 +93,11 @@ with gr.Blocks(css=css) as demo:
 Use StableDiffusion XL with ControlNet pretrained LoRas 
 
         """)
-        use_custom_model = gr.Checkbox(label="Use a custom model ?", value=False)
+        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="Model to use", placeholder="username/my_model")
         image_in = gr.Image(source="upload", type="filepath")
         prompt = gr.Textbox(label="Prompt")
-        preprocessor = gr.Dropdown(label="Preprocessor", choices=["canny"], value="canny")
+        preprocessor = gr.Dropdown(label="Preprocessor", choices=["canny"], value="canny", interactive=False)
         guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=7.5, type="float")
         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)