Plug-and-Play Diffusion Distillation
<|reference_start|>Plug-and-Play Diffusion Distillation: Diffusion models have shown tremendous results in image generation. However, due to the iterative nature of the diffusion process and its reliance on classifier-free guidance, inference times are slow. In this paper, we propose a new distillation approach for guided diffusion models in which an external lightweight guide model is trained while the original text-to-image model remains frozen. We show that our method reduces the inference computation of classifier-free guided latent-space diffusion models by almost half, and only requires 1\% trainable parameters of the base model. Furthermore, once trained, our guide model can be applied to various fine-tuned, domain-specific versions of the base diffusion model without the need for additional training: this "plug-and-play" functionality drastically improves inference computation while maintaining the visual fidelity of generated images. Empirically, we show that our approach is able to produce visually appealing results and achieve a comparable FID score to the teacher with as few as 8 to 16 steps.<|reference_end|>
@article{hsiao2024plug-and-play,
    title={Plug-and-Play Diffusion Distillation},
    author={Yi-Ting Hsiao, Siavash Khodadadeh, Kevin Duarte, Wei-An Lin, Hui Qu,
  Mingi Kwon, Ratheesh Kalarot},
    journal={arXiv preprint arXiv:2406.01954},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01954},
    primaryClass={cs.CV}
}