Bridging Diffusion Guidance and Anderson Acceleration via Hopfield Dynamics

Researchers have developed Geometry Aware Attention Guidance (GAG), a new method that improves diffusion model generation quality by optimizing attention-space extrapolation. The approach models attention dynamics as fixed-point iterations within Modern Hopfield Networks and applies Anderson Acceleration to stabilize the process while reducing computational costs.

• →GAG provides a theoretical framework for attention-space extrapolation in diffusion models by connecting it to Modern Hopfield Networks.
• →The method decomposes attention updates into parallel and orthogonal components to stabilize acceleration and improve guidance efficiency.
• →GAG offers a plug-and-play solution that integrates with existing frameworks while significantly enhancing generation quality.
• →The approach addresses computational efficiency issues with Classifier-Free Guidance in distilled or single-step models.
• →The research establishes Anderson Acceleration as a special case of attention-space extrapolation dynamics.