# NoiseCollage: A Layout-Aware Text-to-Image Diffusion Model Based on Noise Cropping and Merging

arXiv: https://arxiv.org/abs/2403.03485 · HTML: https://mlsanity.com/paper/2403.03485/

## Key facts

- Cited by 25 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 43 of its 43 references that are in the map.
- First posted on arXiv 6 Mar 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, 30 of 43)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers](https://mlsanity.com/paper/2211.01324/) (2022)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [DiffEdit: Diffusion-based semantic image editing with mask guidance](https://mlsanity.com/paper/2210.11427/) (2022)
- [Zero-shot Image-to-Image Translation](https://mlsanity.com/paper/2302.03027/) (2023)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors](https://mlsanity.com/paper/2203.13131/) (2022)
- [UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models](https://mlsanity.com/paper/2302.04867/) (2023)
- [Blended Latent Diffusion](https://mlsanity.com/paper/2206.02779/) (2022)
- [BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion](https://mlsanity.com/paper/2307.10816/) (2023)
- [SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model](https://mlsanity.com/paper/2212.05034/) (2022)
- [SpaText: Spatio-Textual Representation for Controllable Image Generation](https://mlsanity.com/paper/2211.14305/) (2022)

## Built on by (most cited first, 25 of 25)

- [Training-free and Adaptive Sparse Attention for Efficient Long Video Generation](https://mlsanity.com/paper/2502.21079/) (2025)
- [CreatiLayout: Siamese Multimodal Diffusion Transformer for Creative Layout-to-Image Generation](https://mlsanity.com/paper/2412.03859/) (2024)
- [CreatiDesign: A Unified Multi-Conditional Diffusion Transformer for Creative Graphic Design](https://mlsanity.com/paper/2505.19114/) (2025)
- [Conditional Image Synthesis with Diffusion Models: A Survey](https://mlsanity.com/paper/2409.19365/) (2024)
- [GrounDiT: Grounding Diffusion Transformers via Noisy Patch Transplantation](https://mlsanity.com/paper/2410.20474/) (2024)
- [TKG-DM: Training-free Chroma Key Content Generation Diffusion Model](https://mlsanity.com/paper/2411.15580/) (2024)
- [Seg2Any: Open-set Segmentation-Mask-to-Image Generation with Precise Shape and Semantic Control](https://mlsanity.com/paper/2506.00596/) (2025)
- [InstanceAssemble: Layout-Aware Image Generation via Instance Assembling Attention](https://mlsanity.com/paper/2509.16691/) (2025)
- [Improving Editability in Image Generation with Layer-wise Memory](https://mlsanity.com/paper/2505.01079/) (2025)
- [MUSE: Multi-Subject Unified Synthesis via Explicit Layout Semantic Expansion](https://mlsanity.com/paper/2508.14440/) (2025)
- [Generative Photomontage](https://mlsanity.com/paper/2408.07116/) (2024)
- [NoiseController: Towards Consistent Multi-view Video Generation via Noise Decomposition and Collaboration](https://mlsanity.com/paper/2504.18448/) (2025)
- [LORE: Latent Optimization for Precise Semantic Control in Rectified Flow-based Image Editing](https://mlsanity.com/paper/2508.03144/) (2025)
- [From Image Generation to Infrastructure Design: a Multi-agent Pipeline for Street Design Generation](https://mlsanity.com/paper/2509.05469/) (2025)
- [CanvasComposer: Personalized Group Photo Generation via a Multi-Reference Canvas](https://mlsanity.com/paper/2510.20820/) (2025)
- [AGSwap: Overcoming Category Boundaries in Object Fusion via Adaptive Group Swapping](https://mlsanity.com/paper/2509.18699/) (2025)
- [TAUE: Training-free Noise Transplant and Cultivation Diffusion Model](https://mlsanity.com/paper/2511.02580/) (2025)
- [P3S-Diffusion:A Selective Subject-driven Generation Framework via Point Supervision](https://mlsanity.com/paper/2412.19533/) (2024)
- [AnimatePainter: A Self-Supervised Rendering Framework for Reconstructing Painting Process](https://mlsanity.com/paper/2503.17029/) (2025)
- [PRIMEdit: Probability Redistribution for Instance-Aware Multi-Object Video Editing With a New Benchmark Dataset](https://mlsanity.com/paper/2412.12877/) (2024)
- [Guidance-base Diffusion Models for Improving Photoacoustic Image Quality](https://mlsanity.com/paper/2502.06354/) (2025)
- [ISAC: Training-Free Instance-to-Semantic Attention Control for Multi-Instance Generation](https://mlsanity.com/paper/2505.20935/) (2025)
- [COLLAR: Cascaded Object-Level Latent Refinement for High-Fidelity Conditional Generation](https://mlsanity.com/paper/2606.00954/) (2026)
- [Appearance Pointers -- Multimodal Region Control of Diffusion Transformers](https://mlsanity.com/paper/2607.19344/) (2026)
- [Spatially-Grounded Text-to-Video Generation via Inference-Time Gradient-Free Optimization](https://mlsanity.com/paper/2608.13037/) (2026)

## Abstract (arXiv)

Layout-aware text-to-image generation is a task to generate multi-object images that reflect layout conditions in addition to text conditions. The current layout-aware text-to-image diffusion models still have several issues, including mismatches between the text and layout conditions and quality degradation of generated images. This paper proposes a novel layout-aware text-to-image diffusion model called NoiseCollage to tackle these issues. During the denoising process, NoiseCollage independently estimates noises for individual objects and then crops and merges them into a single noise. This operation helps avoid condition mismatches; in other words, it can put the right objects in the right places. Qualitative and quantitative evaluations show that NoiseCollage outperforms several state-of-the-art models. These successful results indicate that the crop-and-merge operation of noises is a reasonable strategy to control image generation. We also show that NoiseCollage can be integrated with ControlNet to use edges, sketches, and pose skeletons as additional conditions. Experimental results show that this integration boosts the layout accuracy of ControlNet. The code is available at https://github.com/univ-esuty/noisecollage.