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bytedance / sdxl-lightning-4step

SDXL-Lightning is a lightning-fast text-to-image generation model that produces high-quality 1024px images in just a few steps, distilled from Stable Diffusion XL.

Model Input

Input

Input prompt

Negative Input prompt

Min: 256 - Max: 1280

Width of output image. Recommended 1024 or 1280

Min: 256 - Max: 1280

Height of output image. Recommended 1024 or 1280

Additional Settings

Customize your input with more control.

Min: 1 - Max: 4

Number of images to output.

scheduler

Min: 1 - Max: 10

Number of denoising steps. 4 for best results

Min: 0 - Max: 50

Scale for classifier-free guidance

Random seed. Leave blank to randomize the seed

Disable safety checker for generated images

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Model Output

Output

preview
Generated in 1.817 seconds
Logs (7 lines)

Model Example Requests

Examples

MSDxozmxpW6bxUBNm35ObhLbKdn9Fourk90wAIbbnV6pTL4aMWJy0fnWAthTf02ET6IgVmnO8nO7Np2MYeYH

Model Details

Model Details

# SDXL-Lightning

**SDXL-Lightning** is a state-of-the-art text-to-image generation model designed for both **speed** and **quality**, capable of producing **high-resolution 1024px images** in remarkably few steps.

Built upon the foundation of [`stabilityai/stable-diffusion-xl-base-1.0`](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), it leverages *Progressive Adversarial Diffusion Distillation* to achieve rapid yet powerful image synthesis.

## Features - 🚀 **Lightning-fast inference** with 1-step, 2-step, 4-step, and 8-step distilled models. - **2-step, 4-step, and 8-step** models deliver outstanding image quality. - **1-step** model is experimental, exploring the limits of extreme efficiency.

- 🧩 **Model formats**: - **Full UNet checkpoints** – deliver the best image quality. - **LoRA checkpoints** – lightweight and easily applied to other base models.

## Open Source We release SDXL-Lightning fully open-sourced as part of our research to empower the community with cutting-edge fast diffusion technology. For more details, please see our paper: **SDXL-Lightning: Progressive Adversarial Diffusion Distillation**.