load_model

app.py

@@ -11,13 +11,14 @@ import random
 import spaces
 
 pipe = None
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
 def load_model():
   global pipe
   pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
       "yeq6x/animagine_position_map",
       controlnet=ControlNetModel.from_pretrained("yeq6x/Image2PositionColor_v3"),
-  ).to("cuda")
+  ).to(device)
   pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
   
 load_model()
@@ -136,7 +137,6 @@ 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))