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import gradio as gr
import torch
from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
from huggingface_hub import hf_hub_download
import spaces
# Constants
base = "stabilityai/stable-diffusion-xl-base-1.0"
repo = "ByteDance/SDXL-Lightning"
ckpt = "sdxl_lightning_4step_unet.pth"
# 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")
# 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 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",
title="Text-to-Image with SDXL Lightning ⚡",
description=description
)
demo.queue(max_size=20)
demo.launch()
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