Update app.py

app.py

@@ -7,18 +7,29 @@ import spaces
 import gradio as gr
 
 from diffusers import AutoPipelineForText2Image
-from diffusers.utils import load_image
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, 2000)
-    return seed
+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
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, 2000)
+    return seed
+
 @spaces.GPU()
 def create_image(image_pil,
                  prompt,
@@ -46,7 +57,7 @@ def create_image(image_pil,
             }
         pipeline.set_ip_adapter_scale(scale)
 
-    style_image = load_image(image_pil)
+    style_image = preprocess_image(image_pil)
     generator = torch.Generator(device=device).manual_seed(randomize_seed_fn(seed, False))
     image = pipeline(
         prompt=prompt,