Update app.py

app.py

@@ -2,39 +2,38 @@ import gradio as gr
 import torch
 from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
 from huggingface_hub import hf_hub_download
-from PIL import Image
-import io
 import spaces
 
-@spaces.GPU
-def generate_image(prompt):
-    base = "stabilityai/stable-diffusion-xl-base-1.0"
-    repo = "ByteDance/SDXL-Lightning"
-    ckpt = "sdxl_lightning_4step_unet.pth"
+# Constants
+base = "stabilityai/stable-diffusion-xl-base-1.0"
+repo = "ByteDance/SDXL-Lightning"
+ckpt = "sdxl_lightning_4step_unet.pth"
 
-    # Load model
-    pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda")
-    pipe.unet.load_state_dict(torch.load(hf_hub_download(repo, ckpt), map_location="cuda"))
-    pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+# Model Initialization
+pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda")
+pipe.unet.load_state_dict(torch.load(hf_hub_download(repo, ckpt), map_location="cuda"))
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
 
-    # Generate image
+# Function 
+@spaces.GPU
+def generate_image(prompt):
+    # Generate image using the preloaded model
     image = pipe(prompt, num_inference_steps=4, guidance_scale=0).images[0]
-    
     return image
 
-#gradio
+# Gradio Interface
 description = """
 This demo utilizes the SDXL-Lightning model by ByteDance, which is a fast text-to-image generative model capable of producing high-quality images in 4 steps.
 As a community effort, this demo was put together by AngryPenguin. Link to model: https://huggingface.co/ByteDance/SDXL-Lightning
 """
 
 demo = gr.Interface(
-    fn=generate_image, 
-    inputs="text", 
-    outputs="image", 
+    fn=generate_image,
+    inputs="text",
+    outputs="image",
     title="Text-to-Image with SDXL Lightning ⚡",
     description=description
 )
 
 demo.queue(max_size=20)
-demo.launch()
+demo.launch()