Recent text-to-image customization works have proven successful in generating images of given concepts by fine-tuning diffusion models on a few examples. However, tuning-based methods inherently tend to overfit the concepts, resulting in failure to create the concept under multiple conditions (*e.g.*, headphone is missing when generating "a <sks>`dog wearing a headphone"). Interestingly, we notice that the base model before fine-tuning exhibits the capability to compose the base concept with other elements (*e.g.*, "a dog wearing a headphone"), implying that the compositional ability only disappears after personalization tuning. We observe a semantic shift in the customized concept after fine-tuning, indicating that the personalized concept is not aligned with the original concept, and further show through theoretical analyses that this semantic shift leads to increased difficulty in sampling the joint conditional probability distribution, resulting in the loss of the compositional ability. Inspired by this finding, we present **ClassDiffusion**, a technique that leverages a **semantic preservation loss** to explicitly regulate the concept space when learning a new concept. Although simple, this approach effectively prevents semantic drift during the fine-tuning process of the target concepts. Extensive qualitative and quantitative experiments demonstrate that the use of semantic preservation loss effectively improves the compositional abilities of fine-tuning models. Lastly, we also extend our ClassDiffusion to personalized video generation, demonstrating its flexibility.

Advertising is pervasive in everyday life. Some advertisements are not as readily comprehensible, as they convey a deeper message or purpose, which is referred to as “meaningful advertising.” These ads often aim to create an emotional connection with the audience or promote a social cause. Developing a method for automatically understanding meaningful advertising would be advantageous for the dissemination and creation of such ads. However, current models of ad understanding primarily focus on the superficial aspects of images. In this article, we introduce AdGPT, a model that leverages visual expert analysis to guide Large Language Models (LLMs) in generating adaptive reasoning chains. Informed by these chains of thought, the model can intelligently comprehend meaningful ads regarding category, content, and sentiment. To assess the effectiveness of our approach, we extract a subset of meaningful ads from the widely used Pitt’s ad images for analysis. Beyond employing traditional ad understanding metrics to evaluate the LLMs’ comprehensive ad comprehension, we also develop a novel generative metric that aligns with user study evaluations for consistent performance assessment. Experiments show that our methods outperform existing state-of-the-art (SOTA) approaches directly linking visual expert models and LLMs and large-scale visual-language models. Code is available at https://github.com/Rbrq03/AdGPT .