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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
assert torch.cuda.is_available()
device = "cuda"
dtype = torch.float16
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(device, dtype)
unet.load_state_dict(load_file(hf_hub_download(repo, opts["4 Steps"][0]), device=device))
pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=dtype, variant="fp16").to(device)
@spaces.GPU(enable_queue=True)
def generate_image(prompt, option):
global step_loaded
ckpt, step = opts[option]
if step != step_loaded:
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>" +
"<p><center>Lightning-fast text-to-image generation</center></p>" +
"<p><center><a href='https://huggingface.co/ByteDance/SDXL-Lightning'>https://huggingface.co/ByteDance/SDXL-Lightning</a></center></p>"
)
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() |