# Text to Image Generation and Editing: A Survey

arXiv: https://arxiv.org/abs/2505.02527 · HTML: https://mlsanity.com/paper/2505.02527/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2025.

## Built on by (most cited first, 8 of 8)

- [AtelierEval: Agentic Evaluation of Humans & LLMs as Text-to-Image Prompters](https://mlsanity.com/paper/2605.22645/) (2026)
- [An LLM-LVLM Driven Agent for Iterative and Fine-Grained Image Editing](https://mlsanity.com/paper/2508.17435/) (2025)
- [EmoCtrl: Controllable Emotional Image Content Generation](https://mlsanity.com/paper/2512.22437/) (2025)
- [Persona-aware and Explainable Bikeability Assessment: A Vision-Language Model Approach](https://mlsanity.com/paper/2601.03534/) (2026)
- [TYPO: Instruction-Dense Visual Jailbreaks against Commercial Closed-Source Image-Generation Models](https://mlsanity.com/paper/2607.24897/) (2026)
- [Dominant vs. Dominated: Concept-Level Generative Collapse in Diffusion Models](https://mlsanity.com/paper/2512.20666/) (2025)
- [PortraitGen: Exemplar-Driven GRPO with Dual-Reward Guidance for Photorealistic Portrait Generation](https://mlsanity.com/paper/2606.26930/) (2026)
- [Appearance Pointers -- Multimodal Region Control of Diffusion Transformers](https://mlsanity.com/paper/2607.19344/) (2026)

## Abstract (arXiv)

Text-to-image generation (T2I) refers to the text-guided generation of high-quality images. In the past few years, T2I has attracted widespread attention and numerous works have emerged. In this survey, we comprehensively review 141 works conducted from 2021 to 2024. First, we introduce four foundation model architectures of T2I (autoregression, non-autoregression, GAN and diffusion) and the commonly used key technologies (autoencoder, attention and classifier-free guidance). Secondly, we systematically compare the methods of these studies in two directions, T2I generation and T2I editing, including the encoders and the key technologies they use. In addition, we also compare the performance of these researches side by side in terms of datasets, evaluation metrics, training resources, and inference speed. In addition to the four foundation models, we survey other works on T2I, such as energy-based models and recent Mamba and multimodality. We also investigate the potential social impact of T2I and provide some solutions. Finally, we propose unique insights of improving the performance of T2I models and possible future development directions. In summary, this survey is the first systematic and comprehensive overview of T2I, aiming to provide a valuable guide for future researchers and stimulate continued progress in this field.