# Improving Compositional Attribute Binding in Text-to-Image Generative Models via Enhanced Text Embeddings

arXiv: https://arxiv.org/abs/2406.07844 · HTML: https://mlsanity.com/paper/2406.07844/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [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)
- [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2301.13826/) (2023)
- [Compositional Visual Generation with Composable Diffusion Models](https://mlsanity.com/paper/2206.01714/) (2022)
- [TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering](https://mlsanity.com/paper/2303.11897/) (2023)
- [Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment](https://mlsanity.com/paper/2306.08877/) (2023)
- [Compositional Text-to-Image Synthesis with Attention Map Control of Diffusion Models](https://mlsanity.com/paper/2305.13921/) (2023)
- [Compositional Text-to-Image Generation with Dense Blob Representations](https://mlsanity.com/paper/2405.08246/) (2024)

## Built on by (most cited first, 19 of 19)

- [SAFREE: Training-Free and Adaptive Guard for Safe Text-to-Image And Video Generation](https://mlsanity.com/paper/2410.12761/) (2024)
- [A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models](https://mlsanity.com/paper/2502.17516/) (2025)
- [CLIP Behaves like a Bag-of-Words Model Cross-modally but not Uni-modally](https://mlsanity.com/paper/2502.03566/) (2025)
- [SliderEdit: Continuous Image Editing with Fine-Grained Instruction Control](https://mlsanity.com/paper/2511.09715/) (2025)
- [Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!](https://mlsanity.com/paper/2410.20972/) (2024)
- [Localizing Knowledge in Diffusion Transformers](https://mlsanity.com/paper/2505.18832/) (2025)
- [Enhancing Text-to-Image Diffusion Transformer via Split-Text Conditioning](https://mlsanity.com/paper/2505.19261/) (2025)
- [AgentComp: From Agentic Reasoning to Compositional Mastery in Text-to-Image Models](https://mlsanity.com/paper/2512.09081/) (2025)
- [ActionParty: Multi-Subject Action Binding in Generative Video Games](https://mlsanity.com/paper/2604.02330/) (2026)
- [Analyzing CLIP's Performance Limitations in Multi-Object Scenarios: A Controlled High-Resolution Study](https://mlsanity.com/paper/2502.19828/) (2025)
- [Follow the Flow: On Information Flow Across Textual Tokens in Text-to-Image Models](https://mlsanity.com/paper/2504.01137/) (2025)
- [Rare Text Semantics Were Always There in Your Diffusion Transformer](https://mlsanity.com/paper/2510.03886/) (2025)
- [MaskAttn-SDXL: Controllable Region-Level Text-To-Image Generation](https://mlsanity.com/paper/2509.15357/) (2025)
- [Spatially-Grounded Flow Matching: Structured Source Distributions for Image Generation](https://mlsanity.com/paper/2608.15452/) (2026)
- [RADIANCE: Relative Adaptive Denoising with IP-Adapter for Novel Concept Enhancement](https://mlsanity.com/paper/2607.05088/) (2026)
- [ContextBias: Controlled Evaluation of Bias Persistence Under Context Shift in Text-to-Image Models](https://mlsanity.com/paper/2608.29847/) (2026)
- [Panoptic Scene Program Diffusion Transformer](https://mlsanity.com/paper/2609.31780/) (2026)
- [Where to Look Is Not How to Fix: Pre-Denoising Diagnostics and Modality-Dependent Control in Diffusion Composition](https://mlsanity.com/paper/2610.03068/) (2026)
- [ORCA: Hunting Compositional Failures in Text-to-Image Diffusion](https://mlsanity.com/paper/2610.09841/) (2026)

## Abstract (arXiv)

Text-to-image diffusion-based generative models have the stunning ability to generate photo-realistic images and achieve state-of-the-art low FID scores on challenging image generation benchmarks. However, one of the primary failure modes of these text-to-image generative models is in composing attributes, objects, and their associated relationships accurately into an image. In our paper, we investigate compositional attribute binding failures, where the model fails to correctly associate descriptive attributes (such as color, shape, or texture) with the corresponding objects in the generated images, and highlight that imperfect text conditioning with CLIP text-encoder is one of the primary reasons behind the inability of these models to generate high-fidelity compositional scenes. In particular, we show that (i) there exists an optimal text-embedding space that can generate highly coherent compositional scenes showing that the output space of the CLIP text-encoder is sub-optimal, and (ii) the final token embeddings in CLIP are erroneous as they often include attention contributions from unrelated tokens in compositional prompts. Our main finding shows that significant compositional improvements can be achieved (without harming the model's FID score) by fine-tuning only a simple and parameter-efficient linear projection on CLIP's representation space in Stable-Diffusion variants using a small set of compositional image-text pairs.