# distilled_decoding

**Repository Path**: mirrors_microsoft/distilled_decoding

## Basic Information

- **Project Name**: distilled_decoding
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-04-22
- **Last Updated**: 2026-02-28

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow Matching

**[[paper (arXiv)](https://arxiv.org/abs/2412.17153)]**
**[[paper (ICLR25)](https://openreview.net/forum?id=zKlFXV87Pp&noteId=TXjgUIaXYP)]**
**[[code](https://github.com/imagination-research/distilled-decoding)]**
**[[website](https://imagination-research.github.io/distilled-decoding)]**


**Authors:** [Enshu Liu (Tsinghua)](https://scholar.google.com/citations?user=0LUhWzoAAAAJ&hl=en)\*, [Xuefei Ning (Tsinghua)](https://nics-effalg.com/ningxuefei/), [Yu Wang (Tsinghua)](https://scholar.google.com/citations?user=j8JGVvoAAAAJ&hl=en), [Zinan Lin (Microsoft Research)](https://zinanlin.me/)†

* \* Work mostly done during Enshu Liu's internship at Microsoft Research

* † Project advisor: Zinan Lin

This is the official repository of paper [Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow Matching](https://arxiv.org/abs/2412.17153). We propose Distilled Decoding (DD) to distill a pre-trained image auto-regressive model to few steps for fast sampling.

We provide examples of applying DD to SOTA image AR models like [VAR](https://arxiv.org/abs/2404.02905) and [LlamaGen](https://arxiv.org/abs/2406.06525). Please refer to the contents in the directories [`./VAR`](VAR) and [`./LlamaGen`](LlamaGen) for more details. Currently, we only support sampling with our released DD models on ImageNet-256. We will release the training code and text-to-image models later.

## News
* `4/21/2025`: **The DD models and inference code for ImageNet-256 have been released!** The DD training code and text-to-image models are undergoing an additional Microsoft's internal review process and will be released at a later date.
  * Code: https://github.com/microsoft/distilled_decoding 
  * Models: https://huggingface.co/microsoft/distilled_decoding
* `12/24/2024`: The paper is released [here](https://arxiv.org/abs/2412.17153).
* `12/22/2024`: The project website is released [here](https://imagination-research.github.io/distilled-decoding).
* `12/22/2024`: The code and the pre-trained `DD` models are currently under Microsoft's internal review. We will release them here once the review is done. Please star/watch the repo to get the latest update.

## Contributing

This project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

## Trademarks

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trademarks or logos is subject to and must follow 
[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.