Update app.py

app.py

@@ -3,26 +3,29 @@ from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDisc
 from huggingface_hub import hf_hub_download
 from safetensors.torch import load_file
 import gradio as gr
+import spaces
+
 
 base = "stabilityai/stable-diffusion-xl-base-1.0"
 repo = "ByteDance/SDXL-Lightning"
 ckpt = "sdxl_lightning_1step_unet_x0.safetensors" # Use the correct ckpt for your step setting!
 
 # Load model.
-
-def generate():
+@spaces.GPU
+def generate(prompt):
     unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
     unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda"))
     pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
     # Ensure sampler uses "trailing" timesteps and "sample" prediction type.
     pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample")
-
-# Ensure using the same inference steps as the loaded model and CFG set to 0.
-
-
-def greet(prompt):
     image = pipe(prompt, num_inference_steps=1, guidance_scale=0).images[0].save("output.png")
     return image
+# Ensure using the same inference steps as the loaded model and CFG set to 0.
+
+# @spaces.GPU
+# def greet(prompt):
+#     image = pipe(prompt, num_inference_steps=1, guidance_scale=0).images[0].save("output.png")
+#     return image
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="image")
+demo = gr.Interface(fn=generate, inputs="text", outputs="image")
 demo.launch()