Transfer Learning for Text-To-Image Diffusion Models: Methods, Challenges, and Applications
Abstract
This survey paper presents a comprehensive analysis of transfer learning techniques in image synthesis using deep learning methods, with particular emphasis on text-to-image generation models. The field has witnessed remarkable progress through the evolution of Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models. We systematically review state-of-the-art approaches including [1]StackGAN, [2]AttnGAN, DALL•E, Stable Diffusion, and their variants, examining their architectural innovations, training strategies, and performance characteristics. Our analysis covers critical aspects such as semantic alignment between text and images, multi-modal feature fusion, and cross-domain adaptation. We evaluate these methods across standard benchmarks including MS-COCO, CUB-200, and Oxford-102 Flowers datasets, discussing evaluation metrics like FID, IS, CLIP Score, and human evaluation protocols. Furthermore, we identify persistent challenges including computational complexity, semantic gap reduction, dataset limitations, and ethical considerations. The survey concludes with future research directions emphasizing lightweight architectures, multilingual support, improved controllability, and responsible AI deployment in image synthesis applications.
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