MMDiff: Extending Diffusion Transformers for Multi-Modal Generation
Diffusion transformers have demonstrated remarkable generative capabilities, yet the rich perceptual representations computed across their denoising trajectory are discarded once the content is rendered. We present MMDiff, a framework that transforms a frozen diffusion transformer into a multi-modal generative system that jointly produces images alongside any combination of dense perceptual modalities using lightweight decoder heads. Our central finding is that perceptual information is temporally distributed along the denoising trajectory, and that multi-timestep feature fusion with spatially varying aggregation weights is essential, improving semantic segmentation results by up to 28.7%...
| Authors | Yagmur Akarken, Orest Kupyn, Christian Rupprecht |
|---|---|
| Primary category | cs.CV |
| Published | 15 June 2026 |
| Last updated at source | 8 July 2026 |
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