AViTS：Adaptive Spatiotemporal Token Selection for Efficient Dynamic-Resolution Generation

Di(cid:27)usion 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 (cid:28)ne-detail consistency. Existing partial upsampling strategies typically rely on local latent structure cues or single-step statistics, making it di(cid:30)cult to jointly capture token(cid:21)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 importance via latent(cid:21)text attention and temporal importance via token-level feature variation across di(cid:27)usion timesteps, and fuses them to enable spatiotemporal importance-aware selective upsampling: it prioritizes resolution re(cid:28)nement for critical tokens while deferring less important ones, thereby reducing redundant high-resolution computation and improving the quality(cid:21)e(cid:30)ciency trade-o(cid:27). AViTS achieves up to 6.34× on FLUX and nearly 9× FLOPs reduction on Qwen-ImageEdit and FLUX.1-Kontext-dev, orthogonal to distillation, quantization, and feature caching, and reaching 14.76× with distilled models. Code: https://github.com/QHR69/AViTS.