DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents
<|reference_start|>DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents: Diffusion models (DMs) have revolutionized generative learning. They utilize a diffusion process to encode data into a simple Gaussian distribution. However, encoding a complex, potentially multimodal data distribution into a single continuous Gaussian distribution arguably represents an unnecessarily challenging learning problem. We propose Discrete-Continuous Latent Variable Diffusion Models (DisCo-Diff) to simplify this task by introducing complementary discrete latent variables. We augment DMs with learnable discrete latents, inferred with an encoder, and train DM and encoder end-to-end. DisCo-Diff does not rely on pre-trained networks, making the framework universally applicable. The discrete latents significantly simplify learning the DM's complex noise-to-data mapping by reducing the curvature of the DM's generative ODE. An additional autoregressive transformer models the distribution of the discrete latents, a simple step because DisCo-Diff requires only few discrete variables with small codebooks. We validate DisCo-Diff on toy data, several image synthesis tasks as well as molecular docking, and find that introducing discrete latents consistently improves model performance. For example, DisCo-Diff achieves state-of-the-art FID scores on class-conditioned ImageNet-64/128 datasets with ODE sampler.<|reference_end|>
@article{xu2024disco-diff:,
    title={DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents},
    author={Yilun Xu, Gabriele Corso, Tommi Jaakkola, Arash Vahdat, Karsten Kreis},
    journal={arXiv preprint arXiv:2407.03300},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03300},
    primaryClass={cs.LG cs.AI cs.CV}
}