Title: A Refined Technique for Customizing Diffusion Models

Abstract: Diffusion models have shown remarkable proficiency in generating images. Methods like Dreambooth and textual inversion facilitate model personalization by incorporating specific imagery, allowing for the creation of images featuring particular subjects across a wide array of textual scenarios. The approach we introduce is designed to preserve the model’s pre-existing knowledge while integrating new data. This strategy yields higher-quality results and requires significantly less training time than both Dreambooth and textual inversion.

Source: arXiv Generated at: 2026-06-03 00:00:00 UTC