End-to-End Training for Unified Tokenization and Latent Denoising
Latent diffusion models (LDMs) enable high-fidelity synthesis by operating in learned latent spaces. However, training state-of-the-art LDMs requires complex staging: a tokenizer must be trained first, before the diffusion model can be trained in the frozen latent space. We propose UNITE - an autoencoder architecture for unified tokenization and latent diffusion. UNITE consists of a Generative Encoder that serves as both image tokenizer and latent generator via weight sharing. Our key insight is that tokenization and generation can be viewed as the same latent inference problem under different conditioning regimes: tokenization infers latents from fully observed images, whereas generation...
| Authors | Shivam Duggal, Xingjian Bai, Zongze Wu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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