# Progressive Prompt Detailing for Improved Alignment in Text-to-Image Generative Models

arXiv: https://arxiv.org/abs/2503.17794 · HTML: https://mlsanity.com/paper/2503.17794/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 24 references that are in the map.
- First posted on arXiv 22 Mar 2025.

## Builds on (most cited first, 19 of 19)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [Evaluating Text-to-Visual Generation with Image-to-Text Generation](https://mlsanity.com/paper/2404.01291/) (2024)
- [Long-CLIP: Unlocking the Long-Text Capability of CLIP](https://mlsanity.com/paper/2403.15378/) (2024)
- [Understanding the Latent Space of Diffusion Models through the Lens of Riemannian Geometry](https://mlsanity.com/paper/2307.12868/) (2023)
- [Paragraph-to-Image Generation with Information-Enriched Diffusion Model](https://mlsanity.com/paper/2311.14284/) (2023)
- [Text encoders bottleneck compositionality in contrastive vision-language models](https://mlsanity.com/paper/2305.14897/) (2023)
- [Magnet: We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function](https://mlsanity.com/paper/2409.19967/) (2024)
- [Manipulating Embeddings of Stable Diffusion Prompts](https://mlsanity.com/paper/2308.12059/) (2023)
- [Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!](https://mlsanity.com/paper/2410.20972/) (2024)
- [CoMPaSS: Enhancing Spatial Understanding in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2412.13195/) (2024)
- [Visual Concept-driven Image Generation with Text-to-Image Diffusion Model](https://mlsanity.com/paper/2402.11487/) (2024)
- [Unlocking Spatial Comprehension in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2311.17937/) (2023)

## Built on by (most cited first, 4 of 4)

- [Enhancing Text-to-Image Diffusion Transformer via Split-Text Conditioning](https://mlsanity.com/paper/2505.19261/) (2025)
- [DDiT: Dynamic Patch Scheduling for Efficient Diffusion Transformers](https://mlsanity.com/paper/2602.16968/) (2026)
- [FlowSteer: Conditioning Flow Field for Consistent Image Restoration](https://mlsanity.com/paper/2512.08125/) (2025)
- [Memory-Efficient Fine-Tuning Diffusion Transformers via Dynamic Patch Sampling and Block Skipping](https://mlsanity.com/paper/2603.20755/) (2026)

## Abstract (arXiv)

Text-to-image generative models often struggle with long prompts detailing complex scenes, diverse objects with distinct visual characteristics and spatial relationships. In this work, we propose SCoPE (Scheduled interpolation of Coarse-to-fine Prompt Embeddings), a training-free method to improve text-to-image alignment by progressively refining the input prompt in a coarse-to-fine-grained manner. Given a detailed input prompt, we first decompose it into multiple sub-prompts which evolve from describing broad scene layout to highly intricate details. During inference, we interpolate between these sub-prompts and thus progressively introduce finer-grained details into the generated image. Our training-free plug-and-play approach significantly enhances prompt alignment, achieves an average improvement of more than +8 in Visual Question Answering (VQA) scores over the Stable Diffusion baselines on 83% of the prompts from the GenAI-Bench dataset.