Update app.py

app.py

@@ -89,17 +89,17 @@ def generate(
             mask_image = load_image(mask_url)
 
         if use_controlnet:
-            image = load_image(img_url)
-            image = np.array(image)
-            
-            # get canny image
-            image = cv2.Canny(image, 100, 200)
-            image = image[:, :, None]
-            image = np.concatenate([image, image, image], axis=2)
-            canny_image = Image.fromarray(image)
-        
             controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
             pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16)
+
+            image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
+            np_image = np.array(image)
+            
+            # get canny image
+            np_image = cv2.Canny(np_image, 100, 200)
+            np_image = np_image[:, :, None]
+            np_image = np.concatenate([np_image, np_image, np_image], axis=2)
+            canny_image = Image.fromarray(np_image)
             
         if use_lora:
             pipe.load_lora_weights(lora)
@@ -124,20 +124,13 @@ def generate(
         negative_prompt_2 = None  # type: ignore
 
     if use_controlnet:
-        images = pipe(
-            prompt=prompt,
+        image = pipe(
+            "futuristic-looking woman",
+            num_inference_steps=20,
+            generator=generator,
             image=image,
             control_image=canny_image,
-            negative_prompt=negative_prompt,
-            prompt_2=prompt_2,
-            width=width,
-            height=height,
-            negative_prompt_2=negative_prompt_2,
-            guidance_scale=guidance_scale_base,
-            num_inference_steps=num_inference_steps_base,
-            generator=generator,
         ).images[0]
-        return images
 
 with gr.Blocks(theme=gr.themes.Soft(), css="style.css") as demo:
     gr.HTML(