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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 · ORCID
classification cs.CV
keywords imagesexpansionprompttext-to-imagegenerationdiversegeneratedmodel
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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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Forward citations

Cited by 6 Pith papers

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

  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.

  4. Motion Prompting: Controlling Video Generation with Motion Trajectories

    cs.CV 2024-12 conditional novelty 5.0 of 10

    A single-stage ControlNet on the Lumiere video model, conditioned only on dense point tracks, generalizes to sparse and dense trajectory control for object, camera, and transferred motions.

  5. Text to Image Generation and Editing: A Survey

    cs.CV 2025-05 conditional novelty 3.0 of 10

    A broad survey of text-to-image generation and editing research from 2021 to 2024, organized by architecture and comparison tables.

  6. Hierarchical Vision-Language Alignment for Text-to-Image Generation via Diffusion Models

    cs.CV 2025-01 reject novelty 3.0 of 10

    VLAD combines contrastive vision-language alignment with hierarchical diffusion guidance and claims improved text-to-image generation, but the reported FID numbers in Table I do not support 'consistently outperforms a...

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