Current Text-to-Image (T2I) generation methods struggle to accurately create images with complex object relationships and scene compositions. To overcome these challenges, we propose KAIG, a novel text-to-image generative model that integrates a knowledge graph into the image generation process. Unlike traditional models, KAIG uses structured knowledge to enhance the retrieval of relevant information, enabling the generation of high-quality, contextually rich, and semantically consistent images from multi-modal inputs. We introduce a two-stage training strategy: first, condition adapters are trained to align multi-modal inputs, followed by fine-tuning the entire diffusion model. This approach ensures precise alignment between retrieved conditions and the image generation process, leading to an efficient and scalable pipeline. Our experiments on two popular datasets, MS-COCO and CUB-200-2011, show that KAIG consistently outperforms existing methods in both image quality and consistency. Notably, KAIG can seamlessly integrate with any pre-trained diffusion model, requiring minimal additional training while achieving superior results. Ultimately, KAIG demonstrates strong potential for addressing key limitations in current T2I models and advancing the field of image synthesis.

We study an emerging and intriguing problem of multimodal temporal event forecasting with large language models. Compared to using text or graph modalities, the investigation of utilizing images for temporal event forecasting has not been fully explored, especially in the era of large language models (LLMs). To bridge this gap, we are particularly interested in two key questions of: 1) why images will help in temporal event forecasting, and 2) how to integrate images into the LLM-based forecasting framework. To answer these research questions, we propose to identify two essential functions that images play in the scenario of temporal event forecasting, i.e., highlighting and complementary. Then, we develop a novel framework, named MM-Forecast. It employs an Image Function Identification module to recognize these functions as verbal descriptions using multimodal large language models (MLLMs), and subsequently incorporates these function descriptions into LLM-based forecasting models. To evaluate our approach, we construct a new multimodal dataset, MidEast-TE-mm, by extending an existing event dataset MidEast-TE-mini with images. Empirical studies demonstrate that our MM-Forecast can correctly identify the image functions, and further more, incorporating these verbal function descriptions significantly improves the forecasting performance. The dataset, code, and prompts are available at https://github.com/LuminosityX/MM-Forecast.