Author

Denis Korzhenkov

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Preprint Sep 2026

Where Compute Matters: Heterogeneous Attention for Efficient Video Diffusion

Efficient video generation requires reducing the quadratic cost of self-attention over long spatio-temporal token sequences. Existing efficient-attention methods typically apply the same computation pattern to every token, even though denoising difficulty varies substantially across video regions and evolves throughout...

Olga Zatsarynna, Denis Korzhenkov, Juergen Gall et al. · 0 citations

Preprint Aug 2026

SQuad: Sub-Quadratic Attention Distillation for Efficient Video Generation

This work proposes SQuad, a Sub-Quadratic Attention Distillation framework that achieves a complexity of $\mathcal{O}(n\sqrt{n})$ in the resulting distilled Attention, naturally balancing the efficiency v/s expressivity trade-off.

Animesh Karnewar, Denis Korzhenkov, A. Habibian et al. · 0 citations

Preprint Jul 2026

MobileWan: Closing the Quality Gap for Mobile Video Diffusion

MobileWan becomes the first 5B-scale video diffusion model deployable on a commercial mobile device and proposes a learnable attention head pruning method based on binary per-head gates optimized end-to-end using a noise-biased sparsity objective and distillation-based finetuning.

Mohsen Ghafoorian, Denis Korzhenkov, Adil Karjauv et al. · 1 citation