# Scaling Diffusion Mamba with Bidirectional SSMs for Efficient Image and Video Generation

arXiv: https://arxiv.org/abs/2405.15881 · HTML: https://mlsanity.com/paper/2405.15881/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 32 of its 35 references that are in the map.
- First posted on arXiv 24 May 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)
- State-space sequence models (S4, Mamba) / [Video, motion and generative Mamba models](https://mlsanity.com/ssm/video-motion-and-generative-mamba-models/)

## Builds on (most cited first, 30 of 32)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [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)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [Improved Precision and Recall Metric for Assessing Generative Models](https://mlsanity.com/paper/1904.06991/) (2019)
- [VideoGPT: Video Generation using VQ-VAE and Transformers](https://mlsanity.com/paper/2104.10157/) (2021)
- [Latte: Latent Diffusion Transformer for Video Generation](https://mlsanity.com/paper/2401.03048/) (2024)
- [MoCoGAN: Decomposing Motion and Content for Video Generation](https://mlsanity.com/paper/1707.04993/) (2017)
- [All are Worth Words: A ViT Backbone for Diffusion Models](https://mlsanity.com/paper/2209.12152/) (2022)
- [Hungry Hungry Hippos: Towards Language Modeling with State Space Models](https://mlsanity.com/paper/2212.14052/) (2022)
- [Latent Video Diffusion Models for High-Fidelity Long Video Generation](https://mlsanity.com/paper/2211.13221/) (2022)
- [StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2](https://mlsanity.com/paper/2112.14683/) (2021)
- [Generating Images with Sparse Representations](https://mlsanity.com/paper/2103.03841/) (2021)
- [One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale](https://mlsanity.com/paper/2303.06555/) (2023)
- [Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks](https://mlsanity.com/paper/2202.10571/) (2022)
- [Video Probabilistic Diffusion Models in Projected Latent Space](https://mlsanity.com/paper/2302.07685/) (2023)
- [A Good Image Generator Is What You Need for High-Resolution Video Synthesis](https://mlsanity.com/paper/2104.15069/) (2021)
- [DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation](https://mlsanity.com/paper/2307.01831/) (2023)
- [DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2304.06648/) (2023)
- [Diffusion Models Without Attention](https://mlsanity.com/paper/2311.18257/) (2023)

## Built on by (most cited first, 21 of 21)

- [One-Minute Video Generation with Test-Time Training](https://mlsanity.com/paper/2504.05298/) (2025)
- [Autoregressive Adversarial Post-Training for Real-Time Interactive Video Generation](https://mlsanity.com/paper/2506.09350/) (2025)
- [LinGen: Towards High-Resolution Minute-Length Text-to-Video Generation with Linear Computational Complexity](https://mlsanity.com/paper/2412.09856/) (2024)
- [Spatial-Mamba: Effective Visual State Space Models via Structure-aware State Fusion](https://mlsanity.com/paper/2410.15091/) (2024)
- [Taming Teacher Forcing for Masked Autoregressive Video Generation](https://mlsanity.com/paper/2501.12389/) (2025)
- [Error Analyses of Auto-Regressive Video Diffusion Models: A Unified Framework](https://mlsanity.com/paper/2503.10704/) (2025)
- [Mamba in Vision: A Comprehensive Survey of Techniques and Applications](https://mlsanity.com/paper/2410.03105/) (2024)
- [MaskMamba: A Hybrid Mamba-Transformer Model for Masked Image Generation](https://mlsanity.com/paper/2409.19937/) (2024)
- [Optical Flow Representation Alignment Mamba Diffusion Model for Medical Video Generation](https://mlsanity.com/paper/2411.01647/) (2024)
- [Efficient 3D Shape Generation via Diffusion Mamba with Bidirectional SSMs](https://mlsanity.com/paper/2406.05038/) (2024)
- [Mixture-of-Mamba: Enhancing Multi-Modal State-Space Models with Modality-Aware Sparsity](https://mlsanity.com/paper/2501.16295/) (2025)
- [DiffGAP: A Lightweight Diffusion Module in Contrastive Space for Bridging Cross-Model Gap](https://mlsanity.com/paper/2503.12131/) (2025)
- [Evolution of Video Generative Foundations](https://mlsanity.com/paper/2604.06339/) (2026)
- [Efficient Video Diffusion Models: Advancements and Challenges](https://mlsanity.com/paper/2604.15911/) (2026)
- [A Mechanistic View on Video Generation as World Models: State and Dynamics](https://mlsanity.com/paper/2601.17067/) (2026)
- [MANTA: Diffusion Mamba for Efficient and Effective Stochastic Long-Term Dense Anticipation](https://mlsanity.com/paper/2501.08837/) (2025)
- [Diffusion Transformer-to-Mamba Distillation for High-Resolution Image Generation](https://mlsanity.com/paper/2506.18999/) (2025)
- [Technologies on Effectiveness and Efficiency: A Survey of State Spaces Models](https://mlsanity.com/paper/2503.11224/) (2025)
- [Venturing into Uncharted Waters: The Navigation Compass from Transformer to Mamba](https://mlsanity.com/paper/2406.16722/) (2024)
- [MixANT: Observation-dependent Memory Propagation for Stochastic Dense Action Anticipation](https://mlsanity.com/paper/2509.11394/) (2025)
- [Foley-Flow: Coordinated Video-to-Audio Generation with Masked Audio-Visual Alignment and Dynamic Conditional Flows](https://mlsanity.com/paper/2603.08126/) (2026)

## Abstract (arXiv)

In recent developments, the Mamba architecture, known for its selective state space approach, has shown potential in the efficient modeling of long sequences. However, its application in image generation remains underexplored. Traditional diffusion transformers (DiT), which utilize self-attention blocks, are effective but their computational complexity scales quadratically with the input length, limiting their use for high-resolution images. To address this challenge, we introduce a novel diffusion architecture, Diffusion Mamba (DiM), which foregoes traditional attention mechanisms in favor of a scalable alternative. By harnessing the inherent efficiency of the Mamba architecture, DiM achieves rapid inference times and reduced computational load, maintaining linear complexity with respect to sequence length. Our architecture not only scales effectively but also outperforms existing diffusion transformers in both image and video generation tasks. The results affirm the scalability and efficiency of DiM, establishing a new benchmark for image and video generation techniques. This work advances the field of generative models and paves the way for further applications of scalable architectures.