Update app.py

app.py

@@ -1,27 +1,22 @@
-import sys
-sys.path.append('./')
-
 import torch
 import random
 import spaces
 import gradio as gr
 
 from diffusers import AutoPipelineForText2Image
-from transformers import CLIPVisionModelWithProjection
 from diffusers.utils import load_image
-from torchvision import transforms
 
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+device = "cuda" 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=torch.float32).to(device)
-pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
+pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=dtype).to("cuda")
+pipe.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(enable_queue=True)
+@spaces.GPU()
 def create_image(image_pil,
                  prompt,
                  n_prompt,
@@ -47,20 +42,19 @@ def create_image(image_pil,
                 "down": {"block_2": [0.0, control_scale]},
                 "up": {"block_0": [0.0, control_scale, 0.0]},
             }
-        pipeline.set_ip_adapter_scale(scale)
+        pipe.set_ip_adapter_scale(scale)
 
     style_image = load_image(image_pil)
-    generator = torch.Generator(device=device).manual_seed(randomize_seed_fn(seed, True))
+    generator = torch.Generator().manual_seed(randomize_seed_fn(seed, True))
     
     
-    image = pipeline(
+    image = pipe(
         prompt=prompt,
         ip_adapter_image=style_image,
         negative_prompt=n_prompt,
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
         generator=generator,
-        device=device
     )
     return image