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import gradio as gr | |
import torch | |
import spaces | |
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler | |
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker | |
from diffusers.image_processor import VaeImageProcessor | |
from transformers import CLIPImageProcessor | |
from huggingface_hub import hf_hub_download | |
from safetensors.torch import load_file | |
from PIL import Image | |
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), | |
} | |
# Default to load 4-step model. | |
step_loaded = 4 | |
unet = UNet2DConditionModel.from_config(base, subfolder="unet") | |
unet.load_state_dict(load_file(hf_hub_download(repo, opts["4 Steps"][0]))) | |
pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=dtype, variant="fp16").to(device, dtype) | |
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") | |
# Safety checker. | |
safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker").to(device, dtype) | |
feature_extractor=CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32") | |
image_processor = VaeImageProcessor(vae_scale_factor=8) | |
# Inference function. | |
def generate(prompt, option, progress=gr.Progress()): | |
global step_loaded | |
print(prompt, option) | |
ckpt, step = opts[option] | |
progress((0, step)) | |
if step != step_loaded: | |
print(f"Switching checkpoint from {step_loaded} to {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 | |
def inference_callback(p, i, t, kwargs): | |
progress((i+1, step)) | |
return kwargs | |
results = pipe(prompt, num_inference_steps=step, guidance_scale=0, callback_on_step_end=inference_callback, output_type="pt") | |
# Safety check | |
feature_extractor_input = image_processor.postprocess(results.images, output_type="pil") | |
safety_checker_input = feature_extractor(feature_extractor_input, return_tensors="pt") | |
images, has_nsfw_concept = safety_checker( | |
images=results.images, clip_input=safety_checker_input.pixel_values.to(device, dtype) | |
) | |
if has_nsfw_concept[0]: | |
gr.Warning("Safety checker triggered. Image may contain violent or sexual content.") | |
print(f"Safety checker triggered on prompt: {prompt}") | |
return images[0] | |
with gr.Blocks(css="style.css") 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-Lightning Generated Image") | |
prompt.submit( | |
fn=generate, | |
inputs=[prompt, option], | |
outputs=img, | |
) | |
submit.click( | |
fn=generate, | |
inputs=[prompt, option], | |
outputs=img, | |
) | |
gr.Examples( | |
fn=generate, | |
examples=[ | |
["An owl perches quietly on a twisted branch deep within an ancient forest.", "1 Step"], | |
["A lion in the galaxy, octane render", "2 Steps"], | |
["A dolphin leaps through the waves, set against a backdrop of bright blues and teal hues.", "2 Steps"], | |
["A girl smiling", "4 Steps"], | |
["An astronaut riding a horse", "4 Steps"], | |
["A fish on a bicycle, colorful art", "4 Steps"], | |
["A close-up of an Asian lady with sunglasses.", "4 Steps"], | |
["Rabbit portrait in a forest, fantasy", "4 Steps"], | |
["A panda swimming", "4 Steps"], | |
["Man portrait, ethereal", "8 Steps"], | |
], | |
inputs=[prompt, option], | |
outputs=img, | |
cache_examples=True, | |
) | |
gr.HTML( | |
"<p><small><center>This demo is built together by the community</center></small></p>" | |
) | |
demo.queue().launch() |