Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention (2408.00760v2)

Abstract: Conditional diffusion models have shown remarkable success in visual content generation, producing high-quality samples across various domains, largely due to classifier-free guidance (CFG). Recent attempts to extend guidance to unconditional models have relied on heuristic techniques, resulting in suboptimal generation quality and unintended effects. In this work, we propose Smoothed Energy Guidance (SEG), a novel training- and condition-free approach that leverages the energy-based perspective of the self-attention mechanism to enhance image generation. By defining the energy of self-attention, we introduce a method to reduce the curvature of the energy landscape of attention and use the output as the unconditional prediction. Practically, we control the curvature of the energy landscape by adjusting the Gaussian kernel parameter while keeping the guidance scale parameter fixed. Additionally, we present a query blurring method that is equivalent to blurring the entire attention weights without incurring quadratic complexity in the number of tokens. In our experiments, SEG achieves a Pareto improvement in both quality and the reduction of side effects. The code is available at https://github.com/SusungHong/SEG-SDXL.

Smoothed Energy Guidance: A Study on Diffusion Models

The paper, "Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention," introduces a novel methodology centered on enhancing diffusion models for visual content generation. The emphasis is on overcoming limitations associated with current guidance methods, particularly in unconditional generation scenarios.

Core Contribution

The authors propose Smoothed Energy Guidance (SEG), a mechanism that shifts from reliance on classifier-free guidance (CFG) predominantly used in conditional models. SEG harnesses the energy-based model perspective of self-attention to modulate the generation process. It achieves this by manipulating the energy curvature associated with self-attention, thereby providing a more controlled and nuanced guidance for image generation.

Methodology

SEG operates by blurring the attention weights in diffusion models using a Gaussian kernel. This blurring impacts the energy landscape of the attention mechanism, reducing curvature—differentiating the original sharp landscape from a smoother one, and allowing a condition-free approach to guidance.

Key to the success of SEG is its ability to control generation quality through the Gaussian kernel parameter, $\sigma$, with the guidance scale parameter held constant. This control effectively attenuates energy landscape curvature, harnessing the inherent representation capabilities of diffusion models without the overhead of conditional training or heuristic modifications.

Numerical Results

The empirical results demonstrate that SEG improves upon both the quality of generated samples and reduces undesirable side effects. Under experimental conditions, SEG outperformed traditional CFG and newer methodologies like SAG and PAG, notably in achieving superior FID scores. Moreover, SEG proved adept in maintaining image fidelity even with high $\sigma$ values, mitigating issues such as saturation and structural distortion that often mar other approaches.

Implications

Practically, SEG suggests a significant advancement in unconditional and conditional image synthesis. By enabling diffusion models to generate high-quality samples without explicit conditions, this approach broadens the applicability of such models across various domains. It also offers computational advantages since SEG circumvents the necessity of additional training data typically required for CFG.

Theoretically, SEG enriches the understanding of the self-attention mechanism within generative models. By associating energy minimization with gradient steps in the diffusion process, it introduces a framework where landscape curvature becomes a manipulable factor, potentially leading to new explorations in model interpretability and efficiency.

Speculations on Future Developments

Looking forward, the utility of SEG in more complex tasks such as video or 3D content generation appears promising. Extending the approach to temporal and spatial domains could yield further enhancements. Additionally, integrating SEG with other emergent strategies, such as multi-modal representations, could facilitate richer and contextually aware generative models.

Conclusion

In summary, Smoothed Energy Guidance introduces a compelling development in diffusion models. It effectively bridges the gap between unconditional and conditional generation, offering improvements in both quality and usability. The methodology's reliance on a theoretically grounded energy-based perspective might pave the way for future research efforts targeting similar challenges in generative modeling.