Update app.py

app.py

@@ -7,30 +7,20 @@ import spaces
 import gradio as gr
 
 from diffusers import AutoPipelineForText2Image
+from diffusers.utils import load_image
 from torchvision import transforms
 
 device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
 dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-pipeline = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=dtype).to(device)
-pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin", device=device, dtype=dtype)
-
-def preprocess_image(image_pil, target_size=(1024, 1024)):
-    # Resize and convert to tensor
-    transform = transforms.Compose([
-        transforms.Resize(target_size, interpolation=transforms.InterpolationMode.LANCZOS),
-        transforms.ToTensor(),
-    ])
-    image = transform(image_pil).unsqueeze(0) 
-    image = image.to(device=device, dtype=dtype)
-    
-    return image
+pipeline = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=dtype, device_map=device)
+pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
 
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
         seed = random.randint(0, 2000)
     return seed
 
-@spaces.GPU()
+@spaces.GPU(enable_queue=True)
 def create_image(image_pil,
                  prompt,
                  n_prompt,
@@ -57,8 +47,9 @@ def create_image(image_pil,
             }
         pipeline.set_ip_adapter_scale(scale)
 
-    style_image = preprocess_image(image_pil)
+    style_image = load_image(image_pil)
     generator = torch.Generator(device=device).manual_seed(randomize_seed_fn(seed, False))
+    
     image = pipeline(
         prompt=prompt,
         ip_adapter_image=style_image,