Update app.py

app.py

@@ -17,7 +17,7 @@ using our implementation of the RAFT model. We will also see how to convert the
 predicted flows to RGB images for visualization.
 """
 
-from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel
+from diffusers import DiffusionPipeline, ControlNetModel
 from diffusers import UniPCMultistepScheduler
 
 import cv2
@@ -48,8 +48,8 @@ high_threshold = 200
 
 # Models
 controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
-pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
+pipe = DiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, custom_pipeline="stable_diffusion_controlnet_img2img", safety_checker=None, torch_dtype=torch.float16
 )
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)