Update app.py

app.py

@@ -15,9 +15,8 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     return seed
 
 device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-print(torch.cuda.is_available())
 pipeline = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16).to(device)
-pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
+pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin", device=device)
 
 @spaces.GPU()
 def create_image(image_pil,
@@ -30,9 +29,6 @@ def create_image(image_pil,
                  seed,
                  target="Load only style blocks",
                  ):
-    print('start')
-    torch.cuda.set_device(device)
-    print('torch device set')
     if target !="Load original IP-Adapter":
         if target=="Load only style blocks":
             scale = {
@@ -48,15 +44,9 @@ def create_image(image_pil,
                 "up": {"block_0": [0.0, control_scale, 0.0]},
             }
         pipeline.set_ip_adapter_scale(scale)
-    
-    print('ipscale set')
-    
-    style_image = load_image(image_pil)
 
-    print('image loaded')
-    generator = torch.Generator().manual_seed(randomize_seed_fn(seed, False))
-    print('generator loaded')
-    print(pipeline.model.device)
+    style_image = load_image(image_pil).to(device)
+    generator = torch.Generator(device=deive).manual_seed(randomize_seed_fn(seed, False))
     image = pipeline(
         prompt=prompt,
         ip_adapter_image=style_image,
@@ -64,6 +54,7 @@ def create_image(image_pil,
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
         generator=generator,
+        device=device
     )
     return image