A unified deep learning model for image captioning and text-to-image synthesis

Deep learning models have significantly advanced various artificial intelligence tasks, including text-to-image generation and image caption‎ing. However, there remains a semantic gap between textual descriptions and visual representations, which affects the accuracy and coherence of generated images and captions. This paper proposes a novel deep learning model that integrates Stable Diffusion, Convolutional ‎Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks to enhance both text-to-image generation and image captioning ‎tasks. The model employs CNN-LSTM architecture for feature extraction, while Stable Diffusion refines the output iteratively to improve ‎coherence and realism. The approach generates diverse and high-quality images from text inputs and produces accurate captions for images. ‎Experimental evaluations demonstrate the effectiveness of our approach. The image captioning model achieved a BLEU score of 0.89, highlighting its high accuracy. The text-to-image generation results also exhibit substantial improvements in visual realism and semantic alignment. The proposed model offers a robust framework for multimodal AI applications, advancing both content synthesis and understanding ‎in multimedia tasks. These findings underscore the potential of deep learning in bridging the gap between textual and visual modalities, contributing to more effective and versatile AI-driven multimedia solutions‎.