app.py

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 os
from PIL import Image

SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", "0") == "1"

# Constants
base = "stabilityai/stable-diffusion-xl-base-1.0"
repo = "ByteDance/SDXL-Lightning"
checkpoints = {
    "1-Step" : ["sdxl_lightning_1step_unet_x0.safetensors", 1],
    "2-Step" : ["sdxl_lightning_2step_unet.safetensors", 2],
    "4-Step" : ["sdxl_lightning_4step_unet.safetensors", 4],
    "8-Step" : ["sdxl_lightning_8step_unet.safetensors", 8],
}
loaded = None

# Ensure model and scheduler are initialized in GPU-enabled function
if torch.cuda.is_available():
    pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda")

# Function 
@spaces.GPU(enable_queue=True)
def generate_image(prompt, ckpt):
    global loaded
    print(prompt, ckpt)

    checkpoint = checkpoints[ckpt][0]
    num_inference_steps = checkpoints[ckpt][1]

    if loaded != num_inference_steps:
        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]

# Gradio Interface
description = """
This demo utilizes the SDXL-Lightning model by ByteDance, which is a lightning-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
"""

with gr.Blocks(css="style.css") as demo:
    gr.HTML("<h1><center>Text-to-Image with SDXL-Lightning ⚡</center></h1>")
    gr.Markdown(description)
    with gr.Group():
        with gr.Row():
            prompt = gr.Textbox(label='Enter your prompt (English)', scale=8)
            ckpt = gr.Dropdown(label='Select inference steps',choices=['1-Step', '2-Step', '4-Step', '8-Step'], value='4-Step', interactive=True)
            submit = gr.Button(scale=1, variant='primary')
    img = gr.Image(label='SDXL-Lightning Generated Image')

    prompt.submit(fn=generate_image,
                 inputs=[prompt, ckpt],
                 outputs=img,
                 )
    submit.click(fn=generate_image,
                 inputs=[prompt, ckpt],
                 outputs=img,
                 )
    
demo.queue().launch()