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Faster Diffusion via Temporal Attention Decomposition

Published 3 Apr 2024 in cs.CV | (2404.02747v3)

Abstract: We explore the role of attention mechanism during inference in text-conditional diffusion models. Empirical observations suggest that cross-attention outputs converge to a fixed point after several inference steps. The convergence time naturally divides the entire inference process into two phases: an initial phase for planning text-oriented visual semantics, which are then translated into images in a subsequent fidelity-improving phase. Cross-attention is essential in the initial phase but almost irrelevant thereafter. However, self-attention initially plays a minor role but becomes crucial in the second phase. These findings yield a simple and training-free method known as temporally gating the attention (TGATE), which efficiently generates images by caching and reusing attention outputs at scheduled time steps. Experimental results show when widely applied to various existing text-conditional diffusion models, TGATE accelerates these models by 10%-50%. The code of TGATE is available at https://github.com/HaozheLiu-ST/T-GATE.

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Citations (11)
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Summary

  • The paper introduces Tgate, which decomposes the inference process into a semantics-planning and a fidelity-improving stage to boost efficiency.
  • By caching converged cross-attention outputs, Tgate significantly reduces computational demands while maintaining or slightly improving FID scores.
  • The findings challenge the continuous application of cross-attention, paving the way for more streamlined architectures and mobile-friendly generative models.

Analysis of "Cross-Attention Makes Inference Cumbersome in Text-to-Image Diffusion Models"

The paper "Cross-Attention Makes Inference Cumbersome in Text-to-Image Diffusion Models" presents a critical examination of the role of cross-attention mechanisms during the inference process in text-conditional diffusion models. The authors propose a training-free method, Tgate, that aims to enhance the efficiency of these models by strategically managing the use of cross-attention.

Key Findings

The research reveals that the cross-attention outputs in diffusion models converge to a steady state after a few initial inference steps. This observation leads to a conceptual bifurcation of the inference process into two distinct stages:

  1. Semantics-Planning Stage: Early steps where cross-attention is crucial for planning the text-guided visual semantics.
  2. Fidelity-Improving Stage: Subsequent steps where ignoring the text conditions does not degrade performance and can reduce computational complexity.

The proposed method, Tgate, effectively leverages this bipartite process by caching the cross-attention outputs once they converge and reusing them without further computation during the fidelity-improving stage.

Methodology and Empirical Results

Through comprehensive experimentation on the MS-COCO dataset, the authors demonstrate that Tgate maintains model performance while significantly reducing computational demands. Notable results indicate:

  • A substantial reduction in Multiply-Accumulate Operations (MACs) and parameters, leading to improved latency across tested models.
  • Empirical measures showing slight improvements in FID scores when compared to baseline models without Tgate.
  • Validation of cross-attention’s redundancy at later stages, offering a refined perspective on its role during diffusion processes.

Tgate exhibits compatibility with various modern text-conditional models, such as SD-2.1 and SDXL, demonstrating its versatility and applicability. Moreover, it integrates well with existing acceleration methods, including Latent Consistency Models and DeepCache, providing further speed enhancements.

Implications for Future Research

The findings challenge traditional assumptions about the necessity of continuous cross-attention throughout the inference process in diffusion models. By reevaluating the uniform application of cross-attention, the research opens avenues for more efficient architectural designs, particularly in high-resolution and extensive token-length settings. It suggests a potential reduction in computational bottlenecks especially relevant for mobile applications.

Future Developments

Potential future investigations may center around:

  • Adaptive selection of gate steps based on varying model architectures or input characteristics.
  • Expanding the method's applicability to other generative models where cross-attention plays a pivotal role.
  • Exploring training enhancements that inherently incorporate this bipartite inference strategy, optimizing training paradigms for efficiency from inception.

In summary, the paper contributes an innovative method for optimizing inference in text-conditional diffusion models, underpinned by a thorough temporal analysis of cross-attention's role. The implications of Tgate extend beyond computational efficiency, prompting a reconsideration of cross-attention's fundamental place in modern AI frameworks.

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