# Prompt Expansion for Adaptive Text-to-Image Generation

arXiv: https://arxiv.org/abs/2312.16720 · HTML: https://mlsanity.com/paper/2312.16720/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 27 Dec 2023.

## Builds on (most cited first, 17 of 17)

- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [PaLI: A Jointly-Scaled Multilingual Language-Image Model](https://mlsanity.com/paper/2209.06794/) (2022)
- [Neural Text Generation with Unlikelihood Training](https://mlsanity.com/paper/1908.04319/) (2019)
- [Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery](https://mlsanity.com/paper/2302.03668/) (2023)
- [UL2: Unifying Language Learning Paradigms](https://mlsanity.com/paper/2205.05131/) (2022)
- [Social Biases through the Text-to-Image Generation Lens](https://mlsanity.com/paper/2304.06034/) (2023)
- [Underspecification in Scene Description-to-Depiction Tasks](https://mlsanity.com/paper/2210.05815/) (2022)

## Built on by (most cited first, 24 of 24)

- [Motion Prompting: Controlling Video Generation with Motion Trajectories](https://mlsanity.com/paper/2412.02700/) (2024)
- [T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT](https://mlsanity.com/paper/2505.00703/) (2025)
- [SnapMoGen: Human Motion Generation from Expressive Texts](https://mlsanity.com/paper/2507.09122/) (2025)
- [Inference-time Physics Alignment of Video Generative Models with Latent World Models](https://mlsanity.com/paper/2601.10553/) (2026)
- [Proactive Agents for Multi-Turn Text-to-Image Generation Under Uncertainty](https://mlsanity.com/paper/2412.06771/) (2024)
- [Maestro: Self-Improving Text-to-Image Generation via Agent Orchestration](https://mlsanity.com/paper/2509.10704/) (2025)
- [WorldGPT: A Sora-Inspired Video AI Agent as Rich World Models from Text and Image Inputs](https://mlsanity.com/paper/2403.07944/) (2024)
- [Bifrost-1: Bridging Multimodal LLMs and Diffusion Models with Patch-level CLIP Latents](https://mlsanity.com/paper/2508.05954/) (2025)
- [VisualPrompter: Semantic-Aware Prompt Optimization with Visual Feedback for Text-to-Image Synthesis](https://mlsanity.com/paper/2506.23138/) (2025)
- [The Intricate Dance of Prompt Complexity, Quality, Diversity, and Consistency in T2I Models](https://mlsanity.com/paper/2510.19557/) (2025)
- [Reusing Computation in Text-to-Image Diffusion for Efficient Generation of Image Sets](https://mlsanity.com/paper/2508.21032/) (2025)
- [POET: Preference Optimization for Enhanced Text-to-Image Generation](https://mlsanity.com/paper/2510.12041/) (2025)
- [Personalized Reward Modeling for Text-to-Image Generation](https://mlsanity.com/paper/2511.19458/) (2025)
- [Dynamic and Super-Personalized Media Ecosystem Driven by Generative AI: Unpredictable Plays Never Repeating The Same](https://mlsanity.com/paper/2402.12412/) (2024)
- [Un-Doubling Diffusion: LLM-guided Disambiguation of Homonym Duplication](https://mlsanity.com/paper/2509.21262/) (2025)
- [Evolve to Inspire: Novelty Search for Diverse Image Generation](https://mlsanity.com/paper/2511.00686/) (2025)
- [Diversifying Long Prompt Image Generation through Structured Prompt Embedding Space Sampling](https://mlsanity.com/paper/2511.20251/) (2025)
- [One Rewrite to Fix Them All? Type-Aware Repair Allocation for Text-to-Image Prompt Optimization](https://mlsanity.com/paper/2607.18724/) (2026)
- [Hierarchical Vision-Language Alignment for Text-to-Image Generation via Diffusion Models](https://mlsanity.com/paper/2501.00917/) (2025)
- [Adaptive Prompt Elicitation for Text-to-Image Generation](https://mlsanity.com/paper/2602.04713/) (2026)
- [Conditioning Matters: Stabilizing Inversion and Attention in Diffusion Image Editing](https://mlsanity.com/paper/2606.14125/) (2026)
- [AutoRef: Harness Optimization for Agentic Multi-Reference Image Generation](https://mlsanity.com/paper/2609.35530/) (2026)
- [VIF-Bench: Evaluating Visual Instruction Following in Multi-Reference Image Generation](https://mlsanity.com/paper/2609.37709/) (2026)
- [A Matched-Budget Audit Framework for Recaptioned Image-Text Supervision Distributions](https://mlsanity.com/paper/2610.00952/) (2026)

## Abstract (arXiv)

Text-to-image generation models are powerful but difficult to use. Users craft specific prompts to get better images, though the images can be repetitive. This paper proposes a Prompt Expansion framework that helps users generate high-quality, diverse images with less effort. The Prompt Expansion model takes a text query as input and outputs a set of expanded text prompts that are optimized such that when passed to a text-to-image model, generates a wider variety of appealing images. We conduct a human evaluation study that shows that images generated through Prompt Expansion are more aesthetically pleasing and diverse than those generated by baseline methods. Overall, this paper presents a novel and effective approach to improving the text-to-image generation experience.