DVD-Quant: Data-free Video Diffusion Transformers Quantization
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915838766874624 |
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| author | Li, Zhiteng Li, Hanxuan Wu, Junyi Liu, Kai Qin, Haotong Kong, Linghe Chen, Guihai Zhang, Yulun Yang, Xiaokang |
| author_facet | Li, Zhiteng Li, Hanxuan Wu, Junyi Liu, Kai Qin, Haotong Kong, Linghe Chen, Guihai Zhang, Yulun Yang, Xiaokang |
| contents | Diffusion Transformers (DiTs) have emerged as the state-of-the-art architecture for video generation, yet their computational and memory demands hinder practical deployment. While post-training quantization (PTQ) presents a promising approach to accelerate Video DiT models, existing methods suffer from two critical limitations: (1) dependence on computation-heavy and inflexible calibration procedures, and (2) considerable performance deterioration after quantization. To address these challenges, we propose DVD-Quant, a novel Data-free quantization framework for Video DiTs. Our approach integrates three key innovations: (1) Bounded-init Grid Refinement (BGR) and (2) Auto-scaling Rotated Quantization (ARQ) for calibration data-free quantization error reduction, as well as (3) $δ$-Guided Bit Switching ($δ$-GBS) for adaptive bit-width allocation. Extensive experiments across multiple video generation benchmarks demonstrate that DVD-Quant achieves an approximately 2$\times$ speedup over full-precision baselines on advanced DiT models while maintaining visual fidelity. Notably, DVD-Quant is the first to enable W4A4 PTQ for Video DiTs without compromising video quality. Code and models will be available at https://github.com/lhxcs/DVD-Quant. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18663 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DVD-Quant: Data-free Video Diffusion Transformers Quantization Li, Zhiteng Li, Hanxuan Wu, Junyi Liu, Kai Qin, Haotong Kong, Linghe Chen, Guihai Zhang, Yulun Yang, Xiaokang Computer Vision and Pattern Recognition Diffusion Transformers (DiTs) have emerged as the state-of-the-art architecture for video generation, yet their computational and memory demands hinder practical deployment. While post-training quantization (PTQ) presents a promising approach to accelerate Video DiT models, existing methods suffer from two critical limitations: (1) dependence on computation-heavy and inflexible calibration procedures, and (2) considerable performance deterioration after quantization. To address these challenges, we propose DVD-Quant, a novel Data-free quantization framework for Video DiTs. Our approach integrates three key innovations: (1) Bounded-init Grid Refinement (BGR) and (2) Auto-scaling Rotated Quantization (ARQ) for calibration data-free quantization error reduction, as well as (3) $δ$-Guided Bit Switching ($δ$-GBS) for adaptive bit-width allocation. Extensive experiments across multiple video generation benchmarks demonstrate that DVD-Quant achieves an approximately 2$\times$ speedup over full-precision baselines on advanced DiT models while maintaining visual fidelity. Notably, DVD-Quant is the first to enable W4A4 PTQ for Video DiTs without compromising video quality. Code and models will be available at https://github.com/lhxcs/DVD-Quant. |
| title | DVD-Quant: Data-free Video Diffusion Transformers Quantization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.18663 |