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, preprocessor, controlnet_conditioning_scale, guidance_scale, seed):
+def infer(use_custom_model, model_name, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, seed):
     if use_custom_model:
         custom_model = model_name
 
@@ -39,7 +39,7 @@ def infer(use_custom_model, model_name, image_in, prompt, preprocessor, controln
         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"
+    negative_prompt = negative_prompt
 
     if preprocessor == "canny":
 
@@ -99,9 +99,10 @@ Use StableDiffusion XL with ControlNet pretrained LoRas
         with gr.Row():
             with gr.Column():
                 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")
                 guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=7.5)
             with gr.Column():
-                preprocessor = gr.Dropdown(label="Preprocessor", choices=["canny"], value="canny", interactive=False)  
+                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)
@@ -111,7 +112,7 @@ Use StableDiffusion XL with ControlNet pretrained LoRas
 
     submit_btn.click(
         fn = infer,
-        inputs = [use_custom_model, model_name, image_in, prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, seed],
+        inputs = [use_custom_model, model_name, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, seed],
         outputs = [result]
     )