{"ID":2868929,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16131","arxiv_id":"2509.16131","title":"Dynamic Classifier-Free Diffusion Guidance via Online Feedback","abstract":"Classifier-free guidance (CFG) is a cornerstone of text-to-image diffusion models, yet its effectiveness is limited by the use of static guidance scales. This \"one-size-fits-all\" approach fails to adapt to the diverse requirements of different prompts; moreover, prior solutions like gradient-based correction or fixed heuristic schedules introduce additional complexities and fail to generalize. In this work, we challeng this static paradigm by introducing a framework for dynamic CFG scheduling. Our method leverages online feedback from a suite of general-purpose and specialized small-scale latent-space evaluations, such as CLIP for alignment, a discriminator for fidelity and a human preference reward model, to assess generation quality at each step of the reverse diffusion process. Based on this feedback, we perform a greedy search to select the optimal CFG scale for each timestep, creating a unique guidance schedule tailored to every prompt and sample. We demonstrate the effectiveness of our approach on both small-scale models and the state-of-the-art Imagen 3, showing significant improvements in text alignment, visual quality, text rendering and numerical reasoning. Notably, when compared against the default Imagen 3 baseline, our method achieves up to 53.8% human preference win-rate for overall preference, a figure that increases up to to 55.5% on prompts targeting specific capabilities like text rendering. Our work establishes that the optimal guidance schedule is inherently dynamic and prompt-dependent, and provides an efficient and generalizable framework to achieve it.","short_abstract":"Classifier-free guidance (CFG) is a cornerstone of text-to-image diffusion models, yet its effectiveness is limited by the use of static guidance scales. This \"one-size-fits-all\" approach fails to adapt to the diverse requirements of different prompts; moreover, prior solutions like gradient-based correction or fixed h...","url_abs":"https://arxiv.org/abs/2509.16131","url_pdf":"https://arxiv.org/pdf/2509.16131v2","authors":"[\"Pinelopi Papalampidi\",\"Olivia Wiles\",\"Ira Ktena\",\"Aleksandar Shtedritski\",\"Emanuele Bugliarello\",\"Ivana Kajic\",\"Isabela Albuquerque\",\"Aida Nematzadeh\"]","published":"2025-09-19T16:27:19Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}