AViTS: Adaptive Spatiotemporal Token Selection for Efficient Dynamic-Resolution Generation
Diffusion Transformers (DiTs) achieve high-quality generation but are costly due to iterative sampling. Dynamic-resolution sampling reduces early-stage cost by denoising at low resolution; however, uniformly upsampling all latent tokens at resolution transitions incurs redundant computation and may degrade fine-detail consistency. Existing partial upsampling strategies typically rely on local latent structure cues or single-step statistics, making it difficult to jointly capture token-text semantic relevance and token-wise representation dynamics across diffusion steps. We propose AViTS, an adaptive spatiotemporal token selection framework for dynamic-resolution DiTs. AViTS models spatial...
| Authors | Haoran Qin, Zhengan Yan, Shikang Zheng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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