HQ-DiT: Efficient Diffusion Transformer with FP4 Hybrid Quantization
<|reference_start|>HQ-DiT: Efficient Diffusion Transformer with FP4 Hybrid Quantization: Diffusion Transformers (DiTs) have recently gained substantial attention in both industrial and academic fields for their superior visual generation capabilities, outperforming traditional diffusion models that use U-Net. However,the enhanced performance of DiTs also comes with high parameter counts and implementation costs, seriously restricting their use on resource-limited devices such as mobile phones. To address these challenges, we introduce the Hybrid Floating-point Quantization for DiT(HQ-DiT), an efficient post-training quantization method that utilizes 4-bit floating-point (FP) precision on both weights and activations for DiT inference. Compared to fixed-point quantization (e.g., INT8), FP quantization, complemented by our proposed clipping range selection mechanism, naturally aligns with the data distribution within DiT, resulting in a minimal quantization error. Furthermore, HQ-DiT also implements a universal identity mathematical transform to mitigate the serious quantization error caused by the outliers. The experimental results demonstrate that DiT can achieve extremely low-precision quantization (i.e., 4 bits) with negligible impact on performance. Our approach marks the first instance where both weights and activations in DiTs are quantized to just 4 bits, with only a 0.12 increase in sFID on ImageNet.<|reference_end|>
@article{liu2024hq-dit:,
    title={HQ-DiT: Efficient Diffusion Transformer with FP4 Hybrid Quantization},
    author={Wenxuan Liu and Sai Qian Zhang},
    journal={arXiv preprint arXiv:2405.19751},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19751},
    primaryClass={cs.CV cs.AI}
}