Optimization of Multimodal Inputs Based on Diffusion Models: Zero-Shot Semantic Image Generation

With the continuous advancement of large models, the scale of data has become increasingly important in semantic segmentation tasks. However, the complexity and high cost of annotating semantic segmentation data pose significant challenges to the expansion of datasets. This study aims to leverage pre-trained diffusion generative models for conditional image generation, where labeled masks are used to generate corresponding synthetic images. This ensures a direct correspondence between input and output, effectively bypassing the annotation stage to reduce the cost of labor-intensive tasks. We employ multimodal conditions, to control the generation results. Additionally, we propose a multimodal alignment scheme to optimize the input control conditions, thereby improving the spatial structural accuracy of the generated results. Furthermore, we explore zero-shot generation tasks and successfully achieve zero-shot generation performance across multiple datasets, demonstrating the effectiveness of our approach. In the zero-shot generation experiments on the Cityscapes dataset, our method achieved a 0.6% improvement in the mIoU evaluation metric. On the ADE20K dataset, the performance improvement reached 2.52%, while on the COCO-Stuff dataset, the improvement was 2.43%.