# Tiled Diffusion

arXiv: https://arxiv.org/abs/2412.15185 · HTML: https://mlsanity.com/paper/2412.15185/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 22 of 22)

- [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)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [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)
- [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)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation](https://mlsanity.com/paper/2302.08113/) (2023)
- [Blended Latent Diffusion](https://mlsanity.com/paper/2206.02779/) (2022)
- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [SyncDiffusion: Coherent Montage via Synchronized Joint Diffusions](https://mlsanity.com/paper/2306.05178/) (2023)
- [DiffCollage: Parallel Generation of Large Content with Diffusion Models](https://mlsanity.com/paper/2303.17076/) (2023)
- [Non-Stationary Texture Synthesis by Adversarial Expansion](https://mlsanity.com/paper/1805.04487/) (2018)
- [Differential Diffusion: Giving Each Pixel Its Strength](https://mlsanity.com/paper/2306.00950/) (2023)
- [Diffusing Colors: Image Colorization with Text Guided Diffusion](https://mlsanity.com/paper/2312.04145/) (2023)
- [SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps](https://mlsanity.com/paper/2201.05120/) (2022)
- [Generating Non-Stationary Textures using Self-Rectification](https://mlsanity.com/paper/2401.02847/) (2024)
- [TexTile: A Differentiable Metric for Texture Tileability](https://mlsanity.com/paper/2403.12961/) (2024)
- [Content-aware Tile Generation using Exterior Boundary Inpainting](https://mlsanity.com/paper/2409.14184/) (2024)

## Built on by (most cited first, 9 of 9)

- [JAMER: Project-Level Code Framework Dataset and Benchmark on Professional Game Engines](https://mlsanity.com/paper/2606.19830/) (2026)
- [Efficient and Training-Free Single-Image Diffusion Models](https://mlsanity.com/paper/2606.04299/) (2026)
- [LoopGen: Training-Free Loopable Music Generation](https://mlsanity.com/paper/2504.04466/) (2025)
- [ScrollScape: Unlocking 32K Image Generation With Video Diffusion Priors](https://mlsanity.com/paper/2603.24270/) (2026)
- [TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution](https://mlsanity.com/paper/2606.09608/) (2026)
- [SheafStain: Sheaf-Theoretic Schr"odinger Bridge for Spatially and Biologically Coherent Virtual Staining](https://mlsanity.com/paper/2606.11846/) (2026)
- [SeamEdit: A Black-Box VLM-Agnostic Pipeline for Large-Image Semantic Editing](https://mlsanity.com/paper/2606.13041/) (2026)
- [Text-based Tactile Graphics Generation for the Visually Impaired](https://mlsanity.com/paper/2607.22674/) (2026)
- [Moore, Escher, Penrose: A Conformal Golden Braid](https://mlsanity.com/paper/2610.02210/) (2026)

## Abstract (arXiv)

Image tiling -- the seamless connection of disparate images to create a coherent visual field -- is crucial for applications such as texture creation, video game asset development, and digital art. Traditionally, tiles have been constructed manually, a method that poses significant limitations in scalability and flexibility. Recent research has attempted to automate this process using generative models. However, current approaches primarily focus on tiling textures and manipulating models for single-image generation, without inherently supporting the creation of multiple interconnected tiles across diverse domains. This paper presents Tiled Diffusion, a novel approach that extends the capabilities of diffusion models to accommodate the generation of cohesive tiling patterns across various domains of image synthesis that require tiling. Our method supports a wide range of tiling scenarios, from self-tiling to complex many-to-many connections, enabling seamless integration of multiple images. Tiled Diffusion automates the tiling process, eliminating the need for manual intervention and enhancing creative possibilities in various applications, such as seamlessly tiling of existing images, tiled texture creation, and 360^circ synthesis.