Distilling Diffusion Models into Conditional GANs
<|reference_start|>Distilling Diffusion Models into Conditional GANs: We propose a method to distill a complex multistep diffusion model into a single-step conditional GAN student model, dramatically accelerating inference, while preserving image quality. Our approach interprets diffusion distillation as a paired image-to-image translation task, using noise-to-image pairs of the diffusion model's ODE trajectory. For efficient regression loss computation, we propose E-LatentLPIPS, a perceptual loss operating directly in diffusion model's latent space, utilizing an ensemble of augmentations. Furthermore, we adapt a diffusion model to construct a multi-scale discriminator with a text alignment loss to build an effective conditional GAN-based formulation. E-LatentLPIPS converges more efficiently than many existing distillation methods, even accounting for dataset construction costs. We demonstrate that our one-step generator outperforms cutting-edge one-step diffusion distillation models -- DMD, SDXL-Turbo, and SDXL-Lightning -- on the zero-shot COCO benchmark.<|reference_end|>
@article{kang2024distilling,
    title={Distilling Diffusion Models into Conditional GANs},
    author={Minguk Kang, Richard Zhang, Connelly Barnes, Sylvain Paris, Suha Kwak,
  Jaesik Park, Eli Shechtman, Jun-Yan Zhu, Taesung Park},
    journal={arXiv preprint arXiv:2405.05967},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05967},
    primaryClass={cs.CV cs.GR cs.LG}
}