# Diffusion Models

## Diffusion Model Serving

* PatchedServe: A Patch Management Framework for SLO-Optimized Hybrid Resolution Diffusion Serving (arXiv:2501.09253) \[[arXiv](https://arxiv.org/abs/2501.09253)]
  * UWaterloo & CMU & Rice
  * Serve requests with hybrid resolutions.
* FlexCache: Flexible Approximate Cache System for Video Diffusion (arXiv:2501.04012) \[[arXiv](https://arxiv.org/abs/2501.04012)]
  * UWaterloo
  * Cache for text-to-video diffusion models.
* xDiT: an Inference Engine for Diffusion Transformers (DiTs) with Massive Parallelism (arXiv:2411.01738) \[[arXiv](https://arxiv.org/abs/2411.01738)] \[[Code](https://github.com/xdit-project/xDiT)]
  * Tencent
  * Several parallel approaches for DiTs.
* SwiftDiffusion: Efficient Diffusion Model Serving with Add-on Modules (arXiv:2407.02031) \[[arXiv](https://arxiv.org/abs/2407.02031)]
  * HKUST & Alibaba
* PipeFusion: Displaced Patch Pipeline Parallelism for Inference of Diffusion Transformer Models (arXiv:2405.14430) \[[arXiv](https://arxiv.org/abs/2405.14430)] \[[Code](https://github.com/xdit-project/xDiT)]
  * Tencent & HKU
* Cache Me if You Can: Accelerating Diffusion Models through Block Caching ([CVPR 2024](/reading-notes/conference/cvpr-2024.md)) \[[Paper](https://openaccess.thecvf.com/content/CVPR2024/html/Wimbauer_Cache_Me_if_You_Can_Accelerating_Diffusion_Models_through_Block_CVPR_2024_paper.html)] \[[Homepage](https://fwmb.github.io/blockcaching/)]
  * Meta & TUM & MCML & Oxford
* CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion Model ([CVPR 2024](/reading-notes/conference/cvpr-2024.md)) \[[Paper](https://openaccess.thecvf.com/content/CVPR2024/html/Zeng_CAT-DM_Controllable_Accelerated_Virtual_Try-on_with_Diffusion_Model_CVPR_2024_paper.html)] \[[Code](https://github.com/zengjianhao/CAT-DM)]
  * TJU & Tencent
  * **CAT-DM**: **C**ontrollable **A**ccelerated virtual **T**ry-on with **D**iffusion **M**odel
* DeepCache: Accelerating Diffusion Models for Free ([CVPR 2024](/reading-notes/conference/cvpr-2024.md)) \[[Paper](https://openaccess.thecvf.com/content/CVPR2024/html/Ma_DeepCache_Accelerating_Diffusion_Models_for_Free_CVPR_2024_paper.html)] \[[Code](https://github.com/horseee/DeepCache)]
  * NUS
* DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models ([CVPR 2024](/reading-notes/conference/cvpr-2024.md)) \[[Paper](https://openaccess.thecvf.com/content/CVPR2024/html/Li_DistriFusion_Distributed_Parallel_Inference_for_High-Resolution_Diffusion_Models_CVPR_2024_paper.html)] \[[Code](https://github.com/mit-han-lab/distrifuser)]
  * MIT & Princeton & Lepton AI & NVIDIA
  * Split the model input into multiple patches and assign each patch to a GPU.
* Approximate Caching for Efficiently Serving Text-to-Image Diffusion Models ([NSDI 2024](/reading-notes/conference/nsdi-2024.md)) \[[Paper](https://www.usenix.org/conference/nsdi24/presentation/agarwal-shubham)] \[[Slides](https://www.usenix.org/system/files/nsdi24_slides-agarwal_shubham.pdf)]
  * Adobe Research & UIUC
  * Skip a certain number of denoising steps.

## Diffusion Model Training

* DiffusionPipe: Training Large Diffusion Models with Efficient Pipelines ([MLSys 2024](/reading-notes/conference/mlsys-2024.md)) \[[Paper](https://proceedings.mlsys.org/paper_files/paper/2024/file/45c1f6a8cbf2da59ebf2c802b4f742cd-Paper-Conference.pdf)] \[[Slides](https://mlsys.org/media/mlsys-2024/Slides/2636_X1xLHC2.pdf)]
  * HKU & AWS & OSU
  * Fill the computation of non-trainable model parts into idle periods of the pipeline training of the backbones.

## Supporting Add-on Modules

* X-Adapter: Adding Universal Compatibility of Plugins for Upgraded Diffusion Model ([CVPR 2024](/reading-notes/conference/cvpr-2024.md)) \[[Paper](https://openaccess.thecvf.com/content/CVPR2024/html/Ran_X-Adapter_Adding_Universal_Compatibility_of_Plugins_for_Upgraded_Diffusion_Model_CVPR_2024_paper.html)] \[[Homepage](https://showlab.github.io/X-Adapter/)] \[[Code](https://github.com/showlab/X-Adapter)]
  * NUS & Tencent & FDU

## Domain-Specific Accelerator (DSA)

* Cambricon-D: Full-Network Differential Acceleration for Diffusion Models ([ISCA 2024](/reading-notes/conference/isca-2024.md)) \[[Paper](https://ieeexplore.ieee.org/document/10609724)]
  * ICT, CAS

## Acronyms

* DiT: Diffusion Transformer


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://paper.lingyunyang.com/paper-list/systems-for-ml/diffusion-models.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.