added sliders

app.py

@@ -9,7 +9,7 @@ from torchvision import transforms
 # from huggingface_hub import login
 # login()
 
-controlnet_conditioning_scale = 1.0 
+# controlnet_conditioning_scale = 1.0 
 
 controlnet = ControlNetModel.from_pretrained(
     "briaai/ControlNet-Canny",
@@ -46,13 +46,13 @@ def get_canny_filter(image):
     canny_image = Image.fromarray(image)
     return canny_image
 
-def process(input_image, prompt):
+def process(input_image, prompt, num_steps, controlnet_conditioning_scale):
     # resize input_image to 1024x1024
     input_image = resize_image(input_image)
     
     canny_image = get_canny_filter(input_image)
     images = pipe(
-        prompt,image=canny_image, controlnet_conditioning_scale=controlnet_conditioning_scale,
+        prompt,image=canny_image, num_inference_steps=num_steps, controlnet_conditioning_scale=controlnet_conditioning_scale,
         ).images
 
     return [canny_image,images[0]]
@@ -70,12 +70,14 @@ with block:
         with gr.Column():
             input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
             prompt = gr.Textbox(label="Prompt")
+            num_steps = gr.Slider(label="Number of steps", minimum=25, maximum=100, value=50, step=1)
+            controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05)
             run_button = gr.Button(value="Run")
             
             
         with gr.Column():
             result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[2], height='auto')
-    ips = [input_image, prompt]
+    ips = [input_image, prompt, num_steps, controlnet_conditioning_scale]
     run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
 
 block.launch(debug = True)