# MegaFusion: Extend Diffusion Models towards Higher-resolution Image Generation without Further Tuning

arXiv: https://arxiv.org/abs/2408.11001 · HTML: https://mlsanity.com/paper/2408.11001/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 50 of its 58 references that are in the map.
- First posted on arXiv 20 Aug 2024.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 30 of 50)

- [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)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [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)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (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)
- [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)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [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)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)

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

- [Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion](https://mlsanity.com/paper/2410.19324/) (2024)
- [SceneGen: Single-Image 3D Scene Generation in One Feedforward Pass](https://mlsanity.com/paper/2508.15769/) (2025)
- [CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up](https://mlsanity.com/paper/2412.16112/) (2024)
- [HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance](https://mlsanity.com/paper/2504.06232/) (2025)
- [Ultra-Resolution Adaptation with Ease](https://mlsanity.com/paper/2503.16322/) (2025)
- [Agentic Retoucher for Text-To-Image Generation](https://mlsanity.com/paper/2601.02046/) (2026)
- [SuperGen: An Efficient Ultra-high-resolution Video Generation System with Sketching and Tiling](https://mlsanity.com/paper/2508.17756/) (2025)
- [HiWave: Training-Free High-Resolution Image Generation via Wavelet-Based Diffusion Sampling](https://mlsanity.com/paper/2506.20452/) (2025)
- [Extend3D: Town-Scale 3D Generation](https://mlsanity.com/paper/2603.29387/) (2026)
- [TP-Blend: Textual-Prompt Attention Pairing for Precise Object-Style Blending in Diffusion Models](https://mlsanity.com/paper/2601.08011/) (2026)
- [MRGen: Segmentation Data Engine for Underrepresented MRI Modalities](https://mlsanity.com/paper/2412.04106/) (2024)
- [Exploring Position Encoding in Diffusion U-Net for Training-free High-resolution Image Generation](https://mlsanity.com/paper/2503.09830/) (2025)
- [Serial Low-rank Adaptation of Vision Transformer](https://mlsanity.com/paper/2503.17750/) (2025)
- [UltraImageGen: Efficient Ultra-High-Resolution Image Generation with Hierarchical Local Attention](https://mlsanity.com/paper/2510.16325/) (2025)
- [SEGA: Spectral-Energy Guided Attention for Resolution Extrapolation in Diffusion Transformers](https://mlsanity.com/paper/2605.22668/) (2026)
- [ResDiT: Evoking the Intrinsic Resolution Scalability in Diffusion Transformers](https://mlsanity.com/paper/2512.01426/) (2025)
- [Multi-Resolution Flow Matching: Training-Free Diffusion Acceleration via Staged Sampling](https://mlsanity.com/paper/2607.01642/) (2026)
- [Improvements to SDXL in NovelAI Diffusion V3](https://mlsanity.com/paper/2409.15997/) (2024)
- [RectifiedHR: Enable Efficient High-Resolution Synthesis via Energy Rectification](https://mlsanity.com/paper/2503.02537/) (2025)
- [InfoScale: Unleashing Training-free Variable-scaled Image Generation via Effective Utilization of Information](https://mlsanity.com/paper/2509.01421/) (2025)
- [TEASR: Training-Efficient Any-Step Diffusion Transformer for Real-World Image Super-Resolution](https://mlsanity.com/paper/2606.16188/) (2026)
- [A Sanity Check on Composed Image Retrieval](https://mlsanity.com/paper/2604.12904/) (2026)
- [HierEdit: Region-Aware Hierarchical Diffusion for Efficient High-Resolution Editing](https://mlsanity.com/paper/2605.17294/) (2026)
- [Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing](https://mlsanity.com/paper/2607.06136/) (2026)
- [HRDiT: Training-Free High-Resolution Image Generation with Off-the-Shelf Diffusion Transformer Models](https://mlsanity.com/paper/2608.07003/) (2026)

## Abstract (arXiv)

Diffusion models have emerged as frontrunners in text-to-image generation, but their fixed image resolution during training often leads to challenges in high-resolution image generation, such as semantic deviations and object replication. This paper introduces MegaFusion, a novel approach that extends existing diffusion-based text-to-image models towards efficient higher-resolution generation without additional fine-tuning or adaptation. Specifically, we employ an innovative truncate and relay strategy to bridge the denoising processes across different resolutions, allowing for high-resolution image generation in a coarse-to-fine manner. Moreover, by integrating dilated convolutions and noise re-scheduling, we further adapt the model's priors for higher resolution. The versatility and efficacy of MegaFusion make it universally applicable to both latent-space and pixel-space diffusion models, along with other derivative models. Extensive experiments confirm that MegaFusion significantly boosts the capability of existing models to produce images of megapixels and various aspect ratios, while only requiring about 40% of the original computational cost.