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Prompt Expansion for Adaptive Text-to-Image Generation

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arxiv 2312.16720 v1 pith:FWAUMTE6 submitted 2023-12-27 cs.CV

Siddhartha Datta , Alexander Ku , Deepak Ramachandran , Peter Anderson This is my paper
classification cs.CV
keywords imagesexpansionprompttext-to-imagegenerationdiversegeneratedmodel
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Maestro: Self-Improving Text-to-Image Generation via Agent Orchestration

    cs.AI 2025-09 conditional novelty 6.0 of 10

    A multi-agent prompt-refinement system using pairwise AI judging and targeted edit signals outperforms prior automated methods on complex text-to-image tasks.

  2. Reusing Computation in Text-to-Image Diffusion for Efficient Generation of Image Sets

    cs.CV 2025-08 conditional novelty 6.0 of 10

    Grouping related text prompts into a tree and sharing early denoising steps with averaged embeddings saves 50 to 74 percent of diffusion compute on image-embedding-conditioned models while keeping VQA quality essentia...

  3. EmoStyle: Affective Conditioning of Style-Specialist Experts for Emotional Image Generation

    cs.CV 2026-07 conditional novelty 5.0 of 10

    EmoStyle injects LLM-inferred valence-arousal and emotion labels into Z-Image via AdaLN-style residual modulation over style-bucket LoRA experts, plus VLM candidate ranking, and ranked first on AffectiveArt Track 1.

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