SDXL-Lightning: Progressive Adversarial Diffusion Distillation
<|reference_start|>SDXL-Lightning: Progressive Adversarial Diffusion Distillation: We propose a diffusion distillation method that achieves new state-of-the-art in one-step/few-step 1024px text-to-image generation based on SDXL. Our method combines progressive and adversarial distillation to achieve a balance between quality and mode coverage. In this paper, we discuss the theoretical analysis, discriminator design, model formulation, and training techniques. We open-source our distilled SDXL-Lightning models both as LoRA and full UNet weights.<|reference_end|>
@article{lin2024sdxl-lightning:,
    title={SDXL-Lightning: Progressive Adversarial Diffusion Distillation},
    author={Shanchuan Lin, Anran Wang, Xiao Yang},
    journal={arXiv preprint arXiv:2402.13929},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13929},
    primaryClass={cs.CV cs.AI cs.LG}
}