Update app.py

app.py

@@ -1,10 +1,10 @@
-from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
+import gradio as gr
+import torch
+from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
 from huggingface_hub import hf_hub_download
 from safetensors.torch import load_file
 import spaces
-import gradio as gr    
-import torch
-import PIL
+from PIL import Image
 
 # Constants
 base = "stabilityai/stable-diffusion-xl-base-1.0"
@@ -23,8 +23,7 @@ CSS = """
 
 # Ensure model and scheduler are initialized in GPU-enabled function
 if torch.cuda.is_available():
-    unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
-    pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
+    pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda")
 
 
 # Function 
@@ -37,12 +36,13 @@ def generate_image(prompt, ckpt):
     num_inference_steps = checkpoints[ckpt][1]
 
     if loaded != num_inference_steps:
-        pipe.unet.load_state_dict(torch.load(hf_hub_download(repo, checkpoint), map_location="cuda"))
         pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample" if num_inference_steps==1 else "epsilon")
+        pipe.unet.load_state_dict(load_file(hf_hub_download(repo, checkpoint), device="cuda"))
         loaded = num_inference_steps
         
     results = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=0)
 
+
     return results.images[0]