The field of diffusion models is rapidly advancing, with a focus on improving the robustness and quality of generated images and graphs. Recent research has shown that diffusion models can be susceptible to adversarial noise, highlighting the need for more robust fine-tuning strategies. To address this, researchers have proposed innovative methods to increase the reliability of pre-trained transformers, such as fine-tuning strategies that can protect against adversarial data. Furthermore, the development of new diffusion models, such as the Heat Diffusion Model, has enabled the generation of higher-quality images that preserve details and generate more realistic samples. Additionally, researchers have explored the application of diffusion models to graph generation, including the development of discrete diffusion posterior sampling for paths in layered graphs, which guarantees that generated samples are indeed paths. The study of conditional dependence via auto-regressive diffusion models has also been initiated, which has shown promising results in capturing high-level relationships in data. Notable papers in this area include:

• Fine-Tuning Adversarially-Robust Transformers for Single-Image Dehazing, which proposes two lightweight fine-tuning strategies to increase the robustness of pre-trained transformers.
• Image Generation Method Based on Heat Diffusion Models, which introduces the Heat Diffusion Model that generates higher-quality samples compared to existing models.