Multimodal semantic understanding and image generation algorithm based on transformer architecture

In this paper, a multimodal semantic understanding and image generation algorithm based on Transformer architecture is proposed to solve the bottleneck of traditional methods in cross-modal alignment and semantic modeling. The algorithm adopts joint coding-decoding architecture, including three core components: multimodal encoder, semantic fusion module and conditional diffusion decoder. Multi-modal encoder uses dual-stream structure based on Transformer to process text and image input respectively, and realizes feature alignment through cross-modal attention mechanism. The semantic fusion module consists of several stacked shared parameter Transformer layers to enhance the understanding and fusion of cross-modal information. The conditional diffusion decoder uses the diffusion model based on Transformer to generate images under the guidance of text semantics. By designing a dynamic alignment module combining self-attention and mutual attention, the algorithm enhances the interactive ability between text and image features, and realizes the joint optimization of semantic understanding and image generation through the Transformer layer sharing parameters. In addition, the conditional diffusion model is combined with Transformer to improve the semantic controllability and diversity of the generated image. The experimental results show that the performance of the algorithm IS significantly improved on the standard data set, with the CLIP score of 0.854, the Inception score (IS) of 10.7, the Frechet Inception distance (FID) as low as 18.3, and the reasoning speed of 14.6 FPS, which completely exceeds the baseline models including CLIP-ViT, DALL-E and Stable Diffusion.