[2506.24108] Navigating with Annealing Guidance Scale in Diffusion Space

Computer Science > Graphics arXiv:2506.24108 (cs) [Submitted on 30 Jun 2025 (v1), last revised 2 Mar 2026 (this version, v2)] Title:Navigating with Annealing Guidance Scale in Diffusion Space Authors:Shai Yehezkel, Omer Dahary, Andrey Voynov, Daniel Cohen-Or View a PDF of the paper titled Navigating with Annealing Guidance Scale in Diffusion Space, by Shai Yehezkel and 3 other authors View PDF HTML (experimental) Abstract:Denoising diffusion models excel at generating high-quality images conditioned on text prompts, yet their effectiveness heavily relies on careful guidance during the sampling process. Classifier-Free Guidance (CFG) provides a widely used mechanism for steering generation by setting the guidance scale, which balances image quality and prompt alignment. However, the choice of the guidance scale has a critical impact on the convergence toward a visually appealing and prompt-adherent image. In this work, we propose an annealing guidance scheduler which dynamically adjusts the guidance scale over time based on the conditional noisy signal. By learning a scheduling policy, our method addresses the temperamental behavior of CFG. Empirical results demonstrate that our guidance scheduler significantly enhances image quality and alignment with the text prompt, advancing the performance of text-to-image generation. Notably, our novel scheduler requires no additional activations or memory consumption, and can seamlessly replace the common classifier-free guidance, of...