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import gradio as gr | |
import torch | |
import spaces | |
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler | |
from huggingface_hub import hf_hub_download | |
from safetensors.torch import load_file | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
base = "stabilityai/stable-diffusion-xl-base-1.0" | |
repo = "ByteDance/SDXL-Lightning" | |
opts = { | |
"1 Step" : ("sdxl_lightning_1step_unet_x0.safetensors", 1), | |
"2 Steps" : ("sdxl_lightning_2step_unet.safetensors", 2), | |
"4 Steps" : ("sdxl_lightning_4step_unet.safetensors", 4), | |
"8 Steps" : ("sdxl_lightning_8step_unet.safetensors", 8), | |
} | |
step_loaded = 4 | |
unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16) | |
unet.load_state_dict(load_file(hf_hub_download(repo, opts["4 Steps"][0]), device=device)) | |
pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to(device) | |
def generate_image(prompt, option): | |
ckpt, step = opts[option] | |
if step_loaded != step: | |
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample" if step == 1 else "epsilon") | |
pipe.unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device)) | |
step_loaded = step | |
return pipe(prompt, num_inference_steps=step, guidance_scale=0).images[0] | |
with gr.Blocks() as demo: | |
gr.HTML("<h1><center>SDXL-Lightning</center></h1>") | |
gr.HTML("<p><center>Lightning-fast text-to-image generation.</center></p>") | |
gr.HTML("<p><center><a href='https://huggingface.co/ByteDance/SDXL-Lightning'>https://huggingface.co/ByteDance/SDXL-Lightning</a></center></p>") | |
with gr.Group(): | |
with gr.Row(): | |
prompt = gr.Textbox( | |
label="Text prompt", | |
scale=8 | |
) | |
option = gr.Dropdown( | |
label="Inference steps", | |
choices=["1 Step", "2 Steps", "4 Steps", "8 Steps"], | |
value="4 Steps", | |
interactive=True | |
) | |
submit = gr.Button( | |
scale=1, | |
variant="primary" | |
) | |
img = gr.Image(label="SDXL-Lightening Generated Image") | |
prompt.submit( | |
fn=generate_image, | |
inputs=[prompt, option], | |
outputs=img, | |
) | |
submit.click( | |
fn=generate_image, | |
inputs=[prompt, option], | |
outputs=img, | |
) | |
demo.queue().launch() |