Q-VDiT: Towards Accurate Quantization and Distillation of Video-Generation Diffusion Transformers
This work addresses the problem of efficient video generation for edge device deployment, representing an incremental improvement with specific gains in quantization performance.
The paper tackles the challenge of deploying large video-generation diffusion transformers on edge devices by proposing Q-VDiT, a quantization framework that addresses information loss and optimization misalignment, achieving a scene consistency score of 23.40 and outperforming state-of-the-art methods by 1.9×.
Diffusion transformers (DiT) have demonstrated exceptional performance in video generation. However, their large number of parameters and high computational complexity limit their deployment on edge devices. Quantization can reduce storage requirements and accelerate inference by lowering the bit-width of model parameters. Yet, existing quantization methods for image generation models do not generalize well to video generation tasks. We identify two primary challenges: the loss of information during quantization and the misalignment between optimization objectives and the unique requirements of video generation. To address these challenges, we present Q-VDiT, a quantization framework specifically designed for video DiT models. From the quantization perspective, we propose the Token-aware Quantization Estimator (TQE), which compensates for quantization errors in both the token and feature dimensions. From the optimization perspective, we introduce Temporal Maintenance Distillation (TMD), which preserves the spatiotemporal correlations between frames and enables the optimization of each frame with respect to the overall video context. Our W3A6 Q-VDiT achieves a scene consistency of 23.40, setting a new benchmark and outperforming current state-of-the-art quantization methods by 1.9$\times$. Code will be available at https://github.com/cantbebetter2/Q-VDiT.