TASQ: Temporal-Adaptive Bit Sparsification Quantization for Diffusion Models

Seokho Han; Dongwei Wang; Jinhee Kim; Yiran Chen; Kang Eun Jeon; Huanrui Yang; Jong Hwan Ko

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arXiv · 2608.03057

TASQ: Temporal-Adaptive Bit Sparsification Quantization for Diffusion Models

Seokho Han·Dongwei Wang·Jinhee Kim·Yiran Chen·Kang Eun Jeon·Huanrui Yang·Jong Hwan Ko

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Abstract

Static quantization assigns one weight precision to every denoising step. To preserve quality, that precision must accommodate the most quantization-sensitive step, even though many other steps can tolerate fewer bits. The resulting model may satisfy its memory budget, but it repeatedly pays worst-case arithmetic throughout the denoising trajectory. We introduce Temporal-Adaptive Bit Sparsification Quantization (TASQ) to separate these two costs. TASQ stores one shared maximum-precision weight buffer and learns a Temporal-Spatial LSB Mask that selects a lower effective precision for each layer and denoising stage by truncating least-significant bits. Storage therefore remains fixed by the worst case, while BitOPs decrease at less sensitive stages without per-stage weight copies or runtime search. A Temporal-Precision Engine maps the learned schedule to bit-serial execution, where cycles scale with effective precision and switching precision has no measured cycle overhead. On PixArt-Sigma, SANA-1.6B, and SDXL-Turbo, TASQ achieves quality comparable to static quantization with less computation. Together with the Temporal-Precision Engine, it reduces execution cycles by 25 to 50 percent over static quantization and by 6.1 to 7.5x over a naive static 8-bit bit-serial execution. Code is available at https://github.com/seokho-han/tasq.

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BibTeX RIS

Seokho Han, Dongwei Wang, Jinhee Kim, Yiran Chen, Kang Eun Jeon, Huanrui Yang, Jong Hwan Ko. 2026-08-04. TASQ: Temporal-Adaptive Bit Sparsification Quantization for Diffusion Models. https://arxiv.org/abs/2608.03057

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