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MC-Sparse: Deconstructing and Closing the Dense-Sparse Attention Gap in Diffusion Transformers

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/10/2026, 05:18:24 UTC.

Briefings

MC-Sparse is a training-free framework designed to improve diffusion transformer performance by selecting individual key-value tokens while grouping similar queries into tile-aligned groups. It achieves higher fidelity to dense-attention outputs and larger denoising speedups compared to existing sparse-attention baselines, with negligible quality loss observed in video and 3D generation models.

Key points

• MC-Sparse selects individual key-value tokens while organizing similar queries into tile-aligned groups for efficient GPU execution.
• The framework caches metadata including query groups, KV indices, and residuals between dense and sparse attention outputs to reuse them across denoising steps.

Context

Sparse attention is a primary approach to reducing latency in diffusion transformers for long-sequence tasks like video and high-resolution 3D generation. Existing methods often degrade generation quality at high sparsity levels, prompting the need for a training-free solution that balances speed and fidelity.

Limitations

The study relies on controlled oracle comparisons to trace degradation sources and validate MC-Sparse's performance. Results are limited to video and 3D generation models, and specific numerical values for quality metrics are not provided in the evidence.

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