DiTFastAttn: Attention Compression for Diffusion Transformer Models
(2406.08552)Abstract
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.
Overview
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The paper 'DiTFastAttn: Attention Compression for Diffusion Transformer Models' introduces a novel post-training model compression method called DiTFastAttn, aimed at reducing computational inefficiencies in diffusion transformers (DiTs) without extensive retraining.
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Three key techniques are proposed to address identified redundancies: Window Attention with Residual Caching (WA-RS), Attention Sharing across Timesteps (AST), and Attention Sharing across CFG (ASC), which target spatial, temporal, and conditional redundancies respectively.
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Extensive experimental evaluations demonstrate significant reductions in attention computation with minimal loss in generative quality, making DiTFastAttn highly valuable for resource-constrained environments and real-time applications.
DiTFastAttn: Attention Compression for Diffusion Transformer Models
Introduction
The paper "DiTFastAttn: Attention Compression for Diffusion Transformer Models" presents a novel approach aimed at addressing the computational inefficiencies inherent in diffusion transformers (DiTs), particularly focusing on the quadratic complexity of the self-attention mechanism. While DiTs excel in image and video generation tasks, their practical application is often hampered by substantial computational demands, especially at higher resolutions. This work introduces DiTFastAttn, a post-training model compression method, designed to mitigate these inefficiencies without requiring extensive retraining.
Key Contributions
The study identifies three main redundancies in the attention computation of DiTs during the inference process:
- Spatial Redundancy: Many attention heads predominantly capture local spatial information. Consequently, attention values for distant tokens tend toward zero.
- Temporal Redundancy: Attention outputs across neighboring timesteps exhibit high similarity.
- Conditional Redundancy: Conditional and unconditional inferences present significant overlap in attention outputs.
To tackle these redundancies, the authors propose three corresponding techniques:
- Window Attention with Residual Caching (WA-RS): This method reduces spatial redundancy by employing window-based attention in certain layers and preserving long-range dependencies using cached residuals between full and window attention outputs.
- Attention Sharing across Timesteps (AST): This technique exploits the similarity between neighboring timesteps, reusing cached attention outputs to accelerate subsequent computations.
- Attention Sharing across CFG (ASC): By reusing attention outputs from conditional inference during unconditional inference in classifier-free guidance (CFG), this approach eliminates redundant computations.
Experimental Evaluation
The authors conducted extensive evaluations using multiple diffusion transformer models: DiT-2-XL-512, PixArt-Sigma-1024, and OpenSora. Key performance metrics include FID, IS, and CLIP score for image generation, alongside overall computational efficiency measured in FLOPs and latency.
Results:
- For image generation tasks, DiTFastAttn demonstrated significant reductions in attention computation with minimal loss in generative quality. Notably, PixArt-Sigma-2K managed up to an 88% reduction in attention computation and up to a 1.6x speedup in high-resolution scenarios.
- In video generation tasks using OpenSora, DiTFastAttn effectively reduced attention computation while maintaining visual quality, though aggressive compression configurations exhibited slight quality degradations.
Implications
Practical Implications: The ability to compress attention computations without retraining makes DiTFastAttn especially valuable for deploying DiTs in resource-constrained environments. This is critical for applications requiring real-time processing or when operating on edge devices with limited computational power.
Theoretical Implications: The study contributes to a deeper understanding of redundancies in transformer models, potentially guiding future research focused on efficient architecture designs and further compression techniques for attention mechanisms.
Future Directions
The success of DiTFastAttn opens several avenues for further exploration:
- Training-aware Compression Methods: Extending the current post-training approach to incorporate training-aware techniques could mitigate the performance drop observed in more aggressive compression settings.
- Exploring Beyond Attention: While DiTFastAttn focuses on attention mechanisms, other components of the transformer architecture may also present opportunities for similar computational optimizations.
- Kernel-level Optimizations: Enhancing the underlying kernel implementations could provide additional speedups, further improving the practicality of the approach.
Conclusion
DiTFastAttn presents a robust solution to the computational challenges faced by diffusion transformers in high-resolution image and video generation. By identifying and addressing specific redundancies within the attention mechanism, the proposed methods achieve substantial reductions in computation while maintaining output quality. These advancements hold promise for broader applications of DiTs, paving the way for more efficient and accessible generative models.
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