load_model

app.py

@@ -14,14 +14,16 @@ pipe = None
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 def load_model():
-  global pipe
+  global device
   pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
       "yeq6x/animagine_position_map",
       controlnet=ControlNetModel.from_pretrained("yeq6x/Image2PositionColor_v3"),
   ).to(device)
   pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
   
-load_model()
+  return pipe
+
+pipe = load_model()
 
 def convert_pil_to_opencv(pil_image):
   return np.array(pil_image)
@@ -137,6 +139,7 @@ def outpaint_image(image):
 
 @spaces.GPU
 def predict_image(cond_image, prompt, negative_prompt):
+  global pipe
   generator = torch.Generator()
   generator.manual_seed(random.randint(0, 2147483647))