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Text-to-image generation (TTI) refers to the usage of models that couldprocess text input and generate high fidelity images based on textdescriptions. Text-to-image generation using neural networks could be tracedback to the emergence of Generative Adversial Network (GAN), followed by theautoregressive Transformer. Diffusion models are one prominent type ofgenerative model used for the generation of images through the systematicintroduction of noises with repeating steps. As an effect of the impressiveresults of diffusion models on image synthesis, it has been cemented as themajor image decoder used by text-to-image models and brought text-to-imagegeneration to the forefront of machine-learning (ML) research. In the era oflarge models, scaling up model size and the integration with large languagemodels have further improved the performance of TTI models, resulting thegeneration result nearly indistinguishable from real-world images,revolutionizing the way we retrieval images. Our explorative study hasincentivised us to think that there are further ways of scaling text-to-imagemodels with the combination of innovative model architectures and predictionenhancement techniques. We have divided the work of this survey into five mainsections wherein we detail the frameworks of major literature in order to delveinto the different types of text-to-image generation methods. Following this weprovide a detailed comparison and critique of these methods and offer possiblepathways of improvement for future work. In the future work, we argue that TTIdevelopment could yield impressive productivity improvements for creation,particularly in the context of the AIGC era, and could be extended to morecomplex tasks such as video generation and 3D generation.

RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model