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Efficient Inference in Text-to-Image Diffusion Models by Selective Cross-Attention Caching


Core Concepts
Cross-attention in text-to-image diffusion models can be selectively cached and reused to significantly improve inference efficiency without compromising generation quality.
Abstract
The study explores the role of cross-attention during the inference process of text-conditional diffusion models. The key findings are: Cross-attention outputs converge to a fixed point after the first few inference steps. This observation divides the entire inference process into two stages: Semantics-planning stage: The model relies on cross-attention to plan text-oriented visual semantics. Fidelity-improving stage: The model generates images from the previously planned semantics. Cross-attention is redundant in the fidelity-improving stage. Bypassing cross-attention in this stage can reduce computation complexity without compromising generation quality. Based on these findings, the authors propose a simple and training-free method called TGATE (Temporally Gating Cross-Attention) to improve the efficiency of text-to-image diffusion models: TGATE caches the cross-attention outputs once they converge and reuses them in the fidelity-improving stage, eliminating redundant cross-attention computations. TGATE can reduce the number of Multiple-Accumulate Operations (MACs) by up to 65T per image and eliminate 0.5B parameters in the fidelity-improving stage, resulting in around 50% latency reduction compared to the baseline model. TGATE maintains or even slightly improves the generation quality, as measured by the Fréchet Inception Distance (FID) on the MS-COCO validation set. The authors further demonstrate the effectiveness and broad applicability of TGATE by integrating it with various base models, noise schedulers, and acceleration techniques, consistently achieving improved efficiency without compromising performance.
Stats
Cross-attention maps converge within the first 5-10 inference steps. TGATE can reduce 65T MACs per image and eliminate 0.5B parameters in the fidelity-improving stage. TGATE can reduce the latency by around 50% compared to the baseline model (SDXL).
Quotes
"Cross-attention is redundant in the fidelity-improving stage. During the semantics-planning stage, cross-attention plays a crucial role in creating meaningful semantics. Yet, in the latter stage, cross-attention converges and has a minor impact on the generation process." "Bypassing cross-attention during the fidelity-improving stage can potentially reduce the computation cost while maintaining the image generation quality."

Deeper Inquiries

How can the insights from this study be applied to other generative models beyond text-to-image diffusion, such as video generation or multi-modal generation?

The insights from this study can be applied to other generative models beyond text-to-image diffusion by considering the role of cross-attention in the inference process. For video generation models, where sequential frames are generated, understanding the convergence of cross-attention can help optimize the generation process. By identifying the point at which cross-attention converges and becomes less impactful, models can be designed to efficiently generate video sequences. This can lead to faster inference and improved generation quality. In the case of multi-modal generation, where models generate outputs based on multiple input modalities, the findings on the redundancy of cross-attention in later inference steps can be leveraged. By caching and reusing cross-attention outputs, models can reduce computation complexity and improve efficiency in generating multi-modal outputs. This approach can be particularly beneficial in scenarios where multiple modalities need to be integrated for generating diverse outputs. Overall, the insights from this study can be generalized to various generative models by optimizing the use of cross-attention, improving efficiency, and enhancing the quality of generated outputs.

What are the potential drawbacks or limitations of the TGATE method, and how could they be addressed in future research?

One potential drawback of the TGATE method is the trade-off between efficiency and generation quality. While TGATE improves efficiency by caching and reusing cross-attention outputs, there may be a slight compromise in the fidelity of generated images. Future research could focus on fine-tuning the caching strategy to minimize any impact on generation quality while still achieving significant efficiency gains. Another limitation of TGATE is its applicability to specific architectures and models. The method may not be universally applicable to all generative models, especially those with complex architectures or unique attention mechanisms. Future research could explore the adaptability of TGATE to a wider range of models and architectures, ensuring its effectiveness across different domains and tasks. Additionally, the scalability of TGATE to larger datasets and more complex scenarios could be a potential challenge. As models scale up in size and complexity, the caching and reuse of cross-attention outputs may become more challenging. Future research could investigate strategies to optimize TGATE for scalability and robustness in diverse settings.

Could the convergence of cross-attention be leveraged to develop more efficient training strategies for text-to-image diffusion models?

The convergence of cross-attention in text-to-image diffusion models presents an opportunity to develop more efficient training strategies. By understanding that cross-attention becomes less impactful after a certain number of inference steps, training strategies can be optimized to focus on the initial semantics-planning stage where cross-attention plays a crucial role. One potential training strategy could involve adaptive scheduling of cross-attention updates during training. By prioritizing the training of cross-attention in the early stages and gradually reducing its influence as the model converges, training time and computational resources can be optimized. This adaptive training strategy can help models learn meaningful semantics efficiently while maintaining high generation quality. Furthermore, the convergence of cross-attention can be leveraged to design curriculum learning approaches for text-to-image diffusion models. By gradually introducing cross-attention during training and emphasizing its importance in the semantics-planning stage, models can learn to generate images effectively while minimizing redundant computations in the fidelity-improving stage. Overall, leveraging the convergence of cross-attention in training strategies can lead to more efficient and effective text-to-image diffusion models, improving both training speed and generation quality.
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