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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
xDiT: A Scalable Inference Engine for Diffusion Transformers (DiTs) with Massive Parallelism
| Date | Stars |
|---|---|
| 2026-07-31 | 2665 |
| 2026-08-02 | 2669 |
| 2026-08-03 | 2669 |
| 2026-08-06 | 2669 |
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<img alt="xDiT" src="https://raw.githubusercontent.com/xdit-project/xdit_assets/main/XDiTlogo.png" width="50%">
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<h3>A Scalable Inference Engine for Diffusion Transformers (DiTs) on Multiple Computing Devices</h3>
<a href="#cite-us">📝 Papers</a> | <a href="#QuickStart">🚀 Quick Start</a> | <a href="#support-dits">🎯 Supported DiTs</a> | <a href="#dev-guide">📚 Dev Guide </a> | <a href="https://github.com/xdit-project/xDiT/discussions">📈 Discussion </a> | <a href="https://medium.com/@xditproject">📝 Blogs</a></strong>
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[](https://discord.gg/YEWzWfCF9S)
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<h2 id="agenda">Table of Contents</h2>
- [🔥 Meet xDiT](#meet-xdit)
- [📢 Open-source Community](#updates)
- [🎯 Supported DiTs](#support-dits)
- [📈 Performance](#perf)
- [🚀 QuickStart](#QuickStart)
- [🖼️ ComfyUI with xDiT](#comfyui)
- [✨ xDiT's Arsenal](#secrets)
- [Parallel Methods](#parallel)
- [1. PipeFusion](#PipeFusion)
- [2. Unified Sequence Parallel](#USP)
- [3. Hybrid Parallel](#hybrid_parallel)
- [4. CFG Parallel](#cfg_parallel)
- [5. Parallel VAE](#parallel_vae)
- [Single GPU Acceleration](#1gpuacc)
- [Compilation Acceleration](#compilation)
- [Cache Acceleration](#cache_acceleration)
- [📚 Develop Guide](#dev-guide)
- [🚧 History and Looking for Contributions](#history)
- [📝 Cite Us](#cite-us)
<h2 id="meet-xdit">🔥 Meet xDiT</h2>
Diffusion Transformers (DiTs) are driving advancements in high-quality image and video generation.
With the escalating input context length in DiTs, the computational demand of the Attention mechanism grows **quadratically**!
Consequently, multi-GPU and multi-machine deployments are essential to meet the **real-time** requirements in online services.
<h3 id="meet-xdit-parallel">Parallel Inference</h3>
To meet real-time demand for DiTs applications, parallel inference is a must.
xDiT is an inference engine designed for the parallel deployment of DiTs on a large scale.
xDiT provides a suite of efficient parallel approaches for Diffusion Models, as well as computation accelerations.
The overview of xDiT is shown as follows.
<picture>
<img alt="xDiT" src="https://raw.githubusercontent.com/xdit-project/xdit_assets/main/methods/xdit_overview.png">
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1. Sequence Parallelism, [USP](https://arxiv.org/abs/2405.07719) is a unified sequence parallel approach proposed by us combining DeepSpeed-Ulysses, Ring-Attention.
2. [PipeFusion](https://arxiv.org/abs/2405.14430), a sequence-level pipeline parallelism, similar to [TeraPipe](https://arxiv.org/abs/2102.07988) but takes advantage of the input temporal redundancy characteristics of diffusion models.
3. Data Parallel: Processes multiple prompts or generates multiple images from a single prompt in parallel across images.
4. CFG Parallel, also known as Split Batch: Activates when using classifier-free guidance (CFG) with a constant parallelism of 2.
The four parallel methods in xDiT can be configured in a hybrid manner, optimizing communication patterns to best suit the underlying network hardware.
As shown in the following picture, xDiT offers a set of APIs to adapt DiT models in [huggingface/diffusers](https://github.com/huggingface/diffusers) to hybrid parallel implementation through simple wrappers.
If the model you require is not available in the model zoo, developing it by yourself is not so difficult; please refer to our [Dev Guide](#dev-guide).
We also have implemented the following parallel strategies for reference:
1. Tensor Parallelism
2. [DistriFusion](https://arxiv.org/abs/2402.19481)
<h3 id="meet-xdit-cache">Cache Acceleration</h3>
Cache method, including [TeaCache](https://github.com/ali-vilab/TeaCache.git), [First-Block-Cache](https://github.com/chengzeyi/ParaAttention.git) and [DiTFastAttn](https://github.com/thu-nics/DiTFastAttnExcerpt of 28,667 characters
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