DiTFastAttn: Attention Compression for Diffusion Transformer Models
<|reference_start|>DiTFastAttn: Attention Compression for Diffusion Transformer Models: Diffusion Transformers (DiT) excel at image and video generation but face computational challenges due to self-attention's quadratic complexity. We propose DiTFastAttn, a novel post-training compression method to alleviate DiT's computational bottleneck. We identify three key redundancies in the attention computation during DiT inference: 1. spatial redundancy, where many attention heads focus on local information; 2. temporal redundancy, with high similarity between neighboring steps' attention outputs; 3. conditional redundancy, where conditional and unconditional inferences exhibit significant similarity. To tackle these redundancies, we propose three techniques: 1. Window Attention with Residual Caching to reduce spatial redundancy; 2. Temporal Similarity Reduction to exploit the similarity between steps; 3. Conditional Redundancy Elimination to skip redundant computations during conditional generation. To demonstrate the effectiveness of DiTFastAttn, we apply it to DiT, PixArt-Sigma for image generation tasks, and OpenSora for video generation tasks. Evaluation results show that for image generation, our method reduces up to 88\% of the FLOPs and achieves up to 1.6x speedup at high resolution generation.<|reference_end|>
@article{yuan2024ditfastattn:,
    title={DiTFastAttn: Attention Compression for Diffusion Transformer Models},
    author={Zhihang Yuan, Hanling Zhang, Pu Lu, Xuefei Ning, Linfeng Zhang,
  Tianchen Zhao, Shengen Yan, Guohao Dai, Yu Wang},
    journal={arXiv preprint arXiv:2406.08552},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08552},
    primaryClass={cs.CV}
}