# Make It Count: Text-to-Image Generation with an Accurate Number of Objects

arXiv: https://arxiv.org/abs/2406.10210 · HTML: https://mlsanity.com/paper/2406.10210/

## Key facts

- Cited by 47 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 29 of its 42 references that are in the map.
- First posted on arXiv 14 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, 29 of 29)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [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)
- [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)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2301.13826/) (2023)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [Aligning Text-to-Image Models using Human Feedback](https://mlsanity.com/paper/2302.12192/) (2023)
- [LayoutGPT: Compositional Visual Planning and Generation with Large Language Models](https://mlsanity.com/paper/2305.15393/) (2023)
- [Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs](https://mlsanity.com/paper/2401.11708/) (2024)
- [Teaching CLIP to Count to Ten](https://mlsanity.com/paper/2302.12066/) (2023)
- [Key-Locked Rank One Editing for Text-to-Image Personalization](https://mlsanity.com/paper/2305.01644/) (2023)
- [ReCo: Region-Controlled Text-to-Image Generation](https://mlsanity.com/paper/2211.15518/) (2022)
- [InstanceDiffusion: Instance-level Control for Image Generation](https://mlsanity.com/paper/2402.03290/) (2024)
- [Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment](https://mlsanity.com/paper/2306.08877/) (2023)
- [Grounded Text-to-Image Synthesis with Attention Refocusing](https://mlsanity.com/paper/2306.05427/) (2023)
- [Training-Free Consistent Text-to-Image Generation](https://mlsanity.com/paper/2402.03286/) (2024)
- [Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm](https://mlsanity.com/paper/1906.03720/) (2019)
- [Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation](https://mlsanity.com/paper/2403.16990/) (2024)
- [DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback](https://mlsanity.com/paper/2311.17946/) (2023)
- [LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts](https://mlsanity.com/paper/2310.10640/) (2023)
- [DALLE-2 is Seeing Double: Flaws in Word-to-Concept Mapping in Text2Image Models](https://mlsanity.com/paper/2210.10606/) (2022)
- [Counting Guidance for High Fidelity Text-to-Image Synthesis](https://mlsanity.com/paper/2306.17567/) (2023)
- [Improving Compositional Text-to-image Generation with Large Vision-Language Models](https://mlsanity.com/paper/2310.06311/) (2023)
- [Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis](https://mlsanity.com/paper/2311.17126/) (2023)
- [DeepBox: Learning Objectness with Convolutional Networks](https://mlsanity.com/paper/1505.02146/) (2015)
- [Obtaining Favorable Layouts for Multiple Object Generation](https://mlsanity.com/paper/2405.00791/) (2024)

## Built on by (most cited first, 30 of 47)

- [IMAGHarmony: Controllable Image Editing with Consistent Object Quantity and Layout](https://mlsanity.com/paper/2506.01949/) (2025)
- [FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video Generation](https://mlsanity.com/paper/2506.01144/) (2025)
- [Your Vision-Language Model Can't Even Count to 20: Exposing the Failures of VLMs in Compositional Counting](https://mlsanity.com/paper/2510.04401/) (2025)
- [Psi-Sampler: Initial Particle Sampling for SMC-Based Inference-Time Reward Alignment in Score Models](https://mlsanity.com/paper/2506.01320/) (2025)
- [Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation](https://mlsanity.com/paper/2411.18301/) (2024)
- [YOLO-Count: Differentiable Object Counting for Text-to-Image Generation](https://mlsanity.com/paper/2508.00728/) (2025)
- [OverLayBench: A Benchmark for Layout-to-Image Generation with Dense Overlaps](https://mlsanity.com/paper/2509.19282/) (2025)
- [Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!](https://mlsanity.com/paper/2410.20972/) (2024)
- [Lay-Your-Scene: Natural Scene Layout Generation with Diffusion Transformers](https://mlsanity.com/paper/2505.04718/) (2025)
- [Towards Self-Improvement of Diffusion Models via Group Preference Optimization](https://mlsanity.com/paper/2505.11070/) (2025)
- [SpatialReward: Verifiable Spatial Reward Modeling for Fine-Grained Spatial Consistency in Text-to-Image Generation](https://mlsanity.com/paper/2603.22228/) (2026)
- [Generating Pedagogically Meaningful Visuals for Math Word Problems: A New Benchmark and Analysis of Text-to-Image Models](https://mlsanity.com/paper/2506.03735/) (2025)
- [Detection-Driven Object Count Optimization for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2408.11721/) (2024)
- [Cost-Aware Routing for Efficient Text-To-Image Generation](https://mlsanity.com/paper/2506.14753/) (2025)
- [Too Easily Fooled? Prompt Injection Breaks LLMs on Frustratingly Simple Multiple-Choice Questions](https://mlsanity.com/paper/2508.13214/) (2025)
- [CountLoop: Training-Free High-Instance Image Generation via Iterative Agent Guidance](https://mlsanity.com/paper/2508.16644/) (2025)
- [TP-Blend: Textual-Prompt Attention Pairing for Precise Object-Style Blending in Diffusion Models](https://mlsanity.com/paper/2601.08011/) (2026)
- [QUOTA: Quantifying Objects with Text-to-Image Models for Any Domain](https://mlsanity.com/paper/2411.19534/) (2024)
- [CountDiffusion: Text-to-Image Synthesis with Training-Free Counting-Guidance Diffusion](https://mlsanity.com/paper/2505.04347/) (2025)
- [Data-Driven Loss Functions for Inference-Time Optimization in Text-to-Image](https://mlsanity.com/paper/2509.02295/) (2025)
- [Transferability Between Understanding and Generation in Unified Multimodal Models](https://mlsanity.com/paper/2607.04423/) (2026)
- [Why Settle for Mid: A Probabilistic Viewpoint to Spatial Relationship Alignment in Text-to-image Models](https://mlsanity.com/paper/2506.23418/) (2025)
- [CARINOX: Inference-time Scaling with Category-Aware Reward-based Initial Noise Optimization and Exploration](https://mlsanity.com/paper/2509.17458/) (2025)
- [Demystifying Numerosity in Diffusion Models -- Limitations and Remedies](https://mlsanity.com/paper/2510.11117/) (2025)
- [DeLeaker: Dynamic Inference-Time Reweighting For Semantic Leakage Mitigation in Text-to-Image Models](https://mlsanity.com/paper/2510.15015/) (2025)
- [JEDI: The Force of Jensen-Shannon Divergence in Disentangling Diffusion Models](https://mlsanity.com/paper/2505.19166/) (2025)
- [OSPO: Object-Centric Self-Improving Preference Optimization for Text-to-Image Generation](https://mlsanity.com/paper/2506.02015/) (2025)
- [CountCluster: Training-Free Object Quantity Guidance with Cross-Attention Map Clustering for Text-to-Image Generation](https://mlsanity.com/paper/2508.10710/) (2025)
- [D2D: Detector-to-Differentiable Critic for Improved Numeracy in Text-to-Image Generation](https://mlsanity.com/paper/2510.19278/) (2025)
- [When Do Diffusion Models learn to Generate Multiple Objects?](https://mlsanity.com/paper/2605.00273/) (2026)

## Abstract (arXiv)

Despite the unprecedented success of text-to-image diffusion models, controlling the number of depicted objects using text is surprisingly hard. This is important for various applications from technical documents, to children's books to illustrating cooking recipes. Generating object-correct counts is fundamentally challenging because the generative model needs to keep a sense of separate identity for every instance of the object, even if several objects look identical or overlap, and then carry out a global computation implicitly during generation. It is still unknown if such representations exist. To address count-correct generation, we first identify features within the diffusion model that can carry the object identity information. We then use them to separate and count instances of objects during the denoising process and detect over-generation and under-generation. We fix the latter by training a model that predicts both the shape and location of a missing object, based on the layout of existing ones, and show how it can be used to guide denoising with correct object count. Our approach, CountGen, does not depend on external source to determine object layout, but rather uses the prior from the diffusion model itself, creating prompt-dependent and seed-dependent layouts. Evaluated on two benchmark datasets, we find that CountGen strongly outperforms the count-accuracy of existing baselines.