Instance-Specific Classifier-Free Guidance for Text-to-Image Diffusion/Flow-Based Editing

Paper · https://arxiv.org/abs/2610.23905

arXiv:2610.23905v1 Announce Type: new Abstract: In recent years, a variety of prompt-based image editing methods have been developed based on text-to-image (T2I) diffusion/flow models. Image editing introduces an inherent trade-off between adhering to the original source image and realizing the change specified by the target prompt, which are typically measured by the LPIPS and CLIP-T metrics, respectively. Intuitively, this trade-off can be controlled by the classifier-free guidance (CFG) scale. Most existing editing methods, though, use a fixed CFG scale, ignoring that different edits require varying levels of modification. In this paper, we first explore the potential benefits of using an instance-specific CFG by exhaustively re-running prominent editing methods with a wide range of CFG scale values on benchmark datasets. We show that instance-specific setting can significantly improve LPIPS without compromising CLIP-T. Yet, tuning based on repeatedly executing an editing pipeline is inefficient. To prevent slow inference, we train a light-weight neural network that predicts the metrics of the underlying editing method for a given edit instance and CFG scale. At inference time, these predictions enable fast selection of an instance-specific CFG scale. Experiments across multiple editing methods and benchmark settings show the performance advantage of our approach over the baseline methods.

First seen by Oriel on Oct 20, 2026, 05:50 UTC.