T-LoRA: Single Image Diffusion Model Customization Without Overfitting
Vera Soboleva, Aibek Alanov, Andrey Kuznetsov, Konstantin Sobolev
2025-07-11
Summary
This paper introduces T-LoRA, which is a new method to customize image-making AI using just one picture, while avoiding the usual problem of overfitting.
What's the problem?
Normally, when you try to help an AI learn from only one image, it either becomes too focused on the details of that image and doesn't generalize well, or it fails to understand the connection between image and text properly.
What's the solution?
The researchers developed T-LoRA, which uses a special way to adapt the model at each step it learns, and also starts the training with orthogonal (independent) settings. This makes the AI keep the important features of the concept, be flexible to the prompts, and not get stuck just memorizing one image.
Why it matters?
This matters because it makes it much easier and safer to personalize AI image generators with very little data, meaning you can make cool, unique pictures with just one example, and the AI won't lose its general abilities or get confused by text prompts.
Abstract
T-LoRA, a timestep-dependent low-rank adaptation framework, enhances diffusion model personalization with a dynamic fine-tuning strategy and orthogonal initialization, improving concept fidelity and text alignment in data-limited settings.