SDXL Lightning
by ByteDance
Open source · 152k downloads · 2142 likes
4.2
(2142 reviews)ImageAPI & LocalAbout
SDXL Lightning is a text-to-image generation model optimized to produce high-quality 1024-pixel images in just a few computational steps. Distilled from Stable Diffusion XL, it offers versions tailored to different needs, ranging from 1 to 8 steps, with remarkable quality even at just 2 steps. Available as full models or LoRAs, it allows flexible integration into various environments like ComfyUI or Diffusers. What sets it apart is its exceptional speed without compromising visual quality, making it ideal for applications requiring fast and precise renders. Its innovative approach makes it a powerful tool for creators, developers, or researchers in generative AI.
Documentation
SDXL-Lightning
SDXL-Lightning is a lightning-fast text-to-image generation model. It can generate high-quality 1024px images in a few steps. For more information, please refer to our research paper: SDXL-Lightning: Progressive Adversarial Diffusion Distillation. We open-source the model as part of the research.
Our models are distilled from stabilityai/stable-diffusion-xl-base-1.0. This repository contains checkpoints for 1-step, 2-step, 4-step, and 8-step distilled models. The generation quality of our 2-step, 4-step, and 8-step model is amazing. Our 1-step model is more experimental.
We provide both full UNet and LoRA checkpoints. The full UNet models have the best quality while the LoRA models can be applied to other base models.
Demos
- Generate with all configurations, best quality: Demo
Checkpoints
sdxl_lightning_Nstep.safetensors: All-in-one checkpoint, for ComfyUI.sdxl_lightning_Nstep_unet.safetensors: UNet checkpoint only, for Diffusers.sdxl_lightning_Nstep_lora.safetensors: LoRA checkpoint, for Diffusers and ComfyUI.
Diffusers Usage
Please always use the correct checkpoint for the corresponding inference steps.
2-Step, 4-Step, 8-Step UNet
Python
import torch
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
base = "stabilityai/stable-diffusion-xl-base-1.0"
repo = "ByteDance/SDXL-Lightning"
ckpt = "sdxl_lightning_4step_unet.safetensors" # Use the correct ckpt for your step setting!
# Load model.
unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda"))
pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
# Ensure sampler uses "trailing" timesteps.
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
# Ensure using the same inference steps as the loaded model and CFG set to 0.
pipe("A girl smiling", num_inference_steps=4, guidance_scale=0).images[0].save("output.png")
2-Step, 4-Step, 8-Step LoRA
Use LoRA only if you are using non-SDXL base models. Otherwise use our UNet checkpoint for better quality.
Python
import torch
from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
from huggingface_hub import hf_hub_download
base = "stabilityai/stable-diffusion-xl-base-1.0"
repo = "ByteDance/SDXL-Lightning"
ckpt = "sdxl_lightning_4step_lora.safetensors" # Use the correct ckpt for your step setting!
# Load model.
pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda")
pipe.load_lora_weights(hf_hub_download(repo, ckpt))
pipe.fuse_lora()
# Ensure sampler uses "trailing" timesteps.
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
# Ensure using the same inference steps as the loaded model and CFG set to 0.
pipe("A girl smiling", num_inference_steps=4, guidance_scale=0).images[0].save("output.png")
1-Step UNet
The 1-step model is only experimental and the quality is much less stable. Consider using the 2-step model for much better quality.
The 1-step model uses "sample" prediction instead of "epsilon" prediction! The scheduler needs to be configured correctly.
Python
import torch
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
base = "stabilityai/stable-diffusion-xl-base-1.0"
repo = "ByteDance/SDXL-Lightning"
ckpt = "sdxl_lightning_1step_unet_x0.safetensors" # Use the correct ckpt for your step setting!
# Load model.
unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda"))
pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
# Ensure sampler uses "trailing" timesteps and "sample" prediction type.
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample")
# Ensure using the same inference steps as the loaded model and CFG set to 0.
pipe("A girl smiling", num_inference_steps=1, guidance_scale=0).images[0].save("output.png")
ComfyUI Usage
Please always use the correct checkpoint for the corresponding inference steps. Please use Euler sampler with sgm_uniform scheduler.
2-Step, 4-Step, 8-Step Full
- Download the full checkpoint (
sdxl_lightning_Nstep.safetensors) to/ComfyUI/models/checkpoints. - Download our ComfyUI full workflow.
2-Step, 4-Step, 8-Step LoRA
Use LoRA only if you are using non-SDXL base models. Otherwise use our full checkpoint for better quality.
- Prepare your own base model.
- Download the LoRA checkpoint (
sdxl_lightning_Nstep_lora.safetensors) to/ComfyUI/models/loras - Download our ComfyUI LoRA workflow.
1-Step
The 1-step model is only experimental and the quality is much less stable. Consider using the 2-step model for much better quality.
- Update your ComfyUI to the latest version.
- Download the full checkpoint (
sdxl_lightning_1step_x0.safetensors) to/ComfyUI/models/checkpoints. - Download our ComfyUI full 1-step workflow.
Cite Our Work
INI
@misc{lin2024sdxllightning,
title={SDXL-Lightning: Progressive Adversarial Diffusion Distillation},
author={Shanchuan Lin and Anran Wang and Xiao Yang},
year={2024},
eprint={2402.13929},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Capabilities & Tags
diffuserstext-to-imagestable-diffusion
Links & Resources