Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention
Abstract: This study is part of the MLRC Reproducibility Challenge 2025, aiming to reproduce and improve the results from a NeurIPS 2024 submission \textit{Smoothed Energy Guidance (SEG): Guiding Diffusion Models with Reduced Energy Curvature of Attention}. The work proposed in the SEG paper faced key limitations, including the lack of an ablation study for optimal kernel size selection and unexplored alternative blurring strategies within diffusion models, which could offer valuable insights into enhancing image quality and model robustness. Furthermore, the approach employed unnecessary smoothing throughout all iterations of the denoising process, which not only diminished the clarity of the output but also resulted in increased computational costs. To address these issues, we conducted a detailed ablation study and explored more efficient alternatives, including Exponential Moving Average (EMA) and BoxBlur using integral images, to improve computational efficiency while maintaining image quality. Our findings provide insights into optimizing smooth energy guidance in diffusion models, reducing computational overhead while improving image quality.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Qing_Qu2
Submission Number: 4319
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