Text-to-image generation models represent the next step of evolution in image synthesis, offering a natural way to achieve flexible yet fine-grained control over the result. One emerging area of research is the fast adaptation of large text-to-image models to smaller datasets or new visual concepts. However, many efficient methods of adaptation have a long training time, which limits their practical applications, slows down research experiments, and spends excessive GPU resources. In this work, we study the training dynamics of popular text-to-image personalization methods (such as Textual Inversion or DreamBooth), aiming to speed them up. We observe that most concepts are learned at early stages and do not improve in quality later, but standard model convergence metrics fail to indicate that. Instead, we propose a simple drop-in early stopping criterion that only requires computing the regular training objective on a fixed set of inputs for all training iterations. Our experiments on Stable Diffusion for a range of concepts and for three personalization methods demonstrate the competitive performance of our approach, making adaptation up to 8 times faster with no significant drops in quality.

翻译：文本到图像生成模型代表了图像合成领域的新一代演进，提供了一种天然的方式来灵活且精细地控制生成结果。当前一个新兴研究方向是让大型文本到图像模型快速适应小规模数据集或新视觉概念。然而，许多高效的适应方法训练时间较长，这限制了其实际应用，拖慢了研究实验进度，并消耗了大量GPU资源。本研究通过分析主流文本到图像个性化方法（如Textual Inversion和DreamBooth）的训练动态，旨在加速其训练进程。我们发现大多数概念在训练早期阶段即可被学习，后期质量提升有限，但标准的模型收敛指标无法有效反映这一现象。为此，我们提出一种简单的即插即用早期停止准则，仅需在每次训练迭代时计算固定输入集上的常规训练目标函数即可。在Stable Diffusion模型上针对一系列概念及三种个性化方法展开的实验表明，我们的方法具有竞争性表现，可将适应速度提升至多8倍，且未造成明显的质量下降。