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About this paper
Shai Yehezkel, Omer Dahary, Andrey Voynov, Daniel Cohen-Or
Researchers from Tel Aviv University and Google DeepMind introduce an annealing guidance scheduler, a learned method that dynamically adjusts the guidance scale in denoising diffusion models. This approach adaptively controls the generation process, leading to improved image quality and enhanced alignment with complex text prompts.
Key insights
- Dynamically adjusting the guidance scale based on the evolving latent signal and prompt alignment during denoising can significantly improve both image quality and adherence to text prompts.
- The magnitude of the difference between conditional and unconditional noise predictions ($||\delta_t||$) serves as a reliable signal to gauge prompt alignment and guide the generative process.
- A learning-based scheduler can overcome limitations of previous fixed or heuristically constrained guidance methods, enabling robust and unconstrained adaptation of the guidance scale.
Summary by alphaXiv.
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