6Bit-Diffusion: Inference-Time Mixed-Precision Quantization for Video Diffusion Models
Diffusion transformers have demonstrated remarkable capabilities in generating videos. However, their practical deployment is severely constrained by high memory usage and computational cost. Post-Training Quantization provides a practical way to reduce memory usage and boost computation speed. Existing quantization methods typically apply a static bit-width allocation, overlooking the quantization difficulty of activations across diffusion timesteps, leading to a suboptimal trade-off between efficiency and quality. In this paper, we propose a inference time NVFP4/INT8 Mixed-Precision Quantization framework. We find a strong linear correlation between a block's input-output difference and the quantization sensitivity of its internal linear layers. Based on this insight, we design a lightweight predictor that dynamically allocates NVFP4 to temporally stable layers to maximize memory compression, while selectively preserving INT8 for volatile layers to ensure robustness. This adaptive precision strategy enables aggressive quantization without compromising generation quality. Beside this, we observe that the residual between the input and output of a Transformer block exhibits high temporal consistency across timesteps. Leveraging this temporal redundancy, we introduce Temporal Delta Cache (TDC) to skip computations for these invariant blocks, further reducing the computational cost. Extensive experiments demonstrate that our method achieves 1.92times end-to-end acceleration and 3.32times memory reduction, setting a new baseline for efficient inference in Video DiTs.
69c50e8d4ae7eef815b9dc80
Rundong Su
69c50e8d4ae7eef815b9dc81
Jintao Zhang
claimed_verified
2026-03-26T13:42:07.593Z
66c0a08bac74db25de8427ec
https://cdn-avatars.huggingface.co/v1/production/uploads/66c0a08bac74db25de8427ec/9D-piDBZqSt6KNkHImmkv.jpeg
Jintao Zhang
jt-zhang
user
jt-zhang
69c50e8d4ae7eef815b9dc82
Zhihang Yuan
69c50e8d4ae7eef815b9dc83
Haojie Duanmu
69c50e8d4ae7eef815b9dc84
Jianfei Chen
69c50e8d4ae7eef815b9dc85
Jun Zhu
6Bit-Diffusion: Inference-Time Mixed-Precision Quantization for Video Diffusion Models
Diffusion transformers have demonstrated remarkable capabilities in generating videos. However, their practical deployment is severely constrained by high memory usage and computational cost. Post-Training Quantization provides a practical way to reduce memory usage and boost computation speed. Existing quantization methods typically apply a static bit-width allocation, overlooking the quantization difficulty of activations across diffusion timesteps, leading to a suboptimal trade-off between efficiency and quality. In this paper, we propose a inference time NVFP4/INT8 Mixed-Precision Quantization framework. We find a strong linear correlation between a block's input-output difference and the quantization sensitivity of its internal linear layers. Based on this insight, we design a lightweight predictor that dynamically allocates NVFP4 to temporally stable layers to maximize memory compression, while selectively preserving INT8 for volatile layers to ensure robustness. This adaptive precision strategy enables aggressive quantization without compromising generation quality. Beside this, we observe that the residual between the input and output of a Transformer block exhibits high temporal consistency across timesteps. Leveraging this temporal redundancy, we introduce Temporal Delta Cache (TDC) to skip computations for these invariant blocks, further reducing the computational cost. Extensive experiments demonstrate that our method achieves 1.92times end-to-end acceleration and 3.32times memory reduction, setting a new baseline for efficient inference in Video DiTs.
A mixed-precision quantization framework with temporal delta cache reduces memory usage and computational cost for video diffusion transformers while maintaining generation quality.
diffusion transformers post-Training Quantization NVFP4 INT8 mixed-precision quantization temporal delta cache Transformer blocks residual learning temporal consistency computational cost reduction memory compression