Create app.py

app.py

@@ -0,0 +1,40 @@
+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"
+
+    # 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")
+
+    # Generate image
+    image = pipe(prompt, num_inference_steps=4, guidance_scale=0).images[0]
+    
+    return image
+
+#gradio
+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", 
+    title="Text-to-Image with SDXL Lightning ⚡",
+    description=description
+)
+
+demo.queue(max_size=20)
+demo.launch()