Denoising Diffusion Generative Models Secretly Calculate Attentions
Denoising diffusion models are the dominant architecture for image generation, whereas most natural language generation and modeling are primarily handled by well-known transformer architectures employing attention mechanism. Here, we show that diffusion models also inherently use an attention mechanism very similar to that of transformers. Therefore, attention emerges as a universal machine learning principle, based on a general training objective. We also show similarities in basic functional principle of auto-encoders and attention-based models. These equivalences allows us to interchange these designs based on practical requirements. As an example, we can reformulate the diffusion fra...
| Authors | Farzan Haddadi, Leila Monfared, Ebrahim Rezaii et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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