Toward the Frontiers of Reliable Diffusion Sampling via Adversarial Sinkhorn Attention Guidance

The introduction of Adversarial Sinkhorn Attention Guidance (ASAG) marks a significant advancement in the field of diffusion models, which are pivotal for generative tasks such as text-to-image synthesis. Traditional methods, while effective, often lack a principled foundation and depend on heuristic perturbations that can degrade output quality. ASAG addresses these shortcomings by reinterpreting attention scores through optimal transport principles and injecting adversarial costs into self-attention layers. This innovative approach not only reduces pixel-wise similarity between queries and keys but also leads to consistent improvements in sample quality. The implications of ASAG extend beyond mere enhancements in output; it also enhances controllability and fidelity in downstream applications like IP-Adapter and ControlNet, all while being lightweight and plug-and-play, thus not requiring any model retraining. This positions ASAG as a transformative tool in the landscape of AI-driven…