Latent Discrete Diffusion Models

In this paper, we couple a masked discrete diffusion over tokens with a continuous latent process on learned or pretrained latent embeddings. This augmented process provides a softer signal to drive training and inference, and more importantly enables cross-token modeling, entirely discarded by classical discrete diffusion models. We show overall improvements, and especially in few-step generation.

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