---
title: "VAR"
type: "tool"
slug: "foundationvision-var"
canonical_url: "https://www.graphcanon.com/tools/foundationvision-var"
github_url: "https://github.com/FoundationVision/VAR"
homepage_url: null
stars: 8706
forks: 569
primary_language: "Jupyter Notebook"
license: "MIT"
categories: ["model-training", "inference-serving"]
tags: ["image-generation", "vision-transformer", "large-language-models", "diffusion-models", "generative-ai", "autoregressive-models", "transformers", "auto-regressive-model"]
updated_at: "2026-07-07T18:36:00.483578+00:00"
---

# VAR

> Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction

A state-of-the-art codebase for autoregressive image generation. VAR is a novel method that redefines autoregressive learning on images as coarse-to-fine 'next-scale prediction', diverging from the traditional raster-scan approach.

## Facts

- Repository: https://github.com/FoundationVision/VAR
- Stars: 8,706 · Forks: 569 · Open issues: 60 · Watchers: 100
- Primary language: Jupyter Notebook
- License: MIT
- Last pushed: 2025-11-10T21:42:29+00:00

## Categories

- [Model Training](/categories/model-training.md)
- [Inference & Serving](/categories/inference-serving.md)

## Tags

image-generation, vision-transformer, large language models, diffusion-models, generative-ai, autoregressive-models, transformers, auto-regressive-model

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## README (excerpt)

```text
# VAR: a new visual generation method elevates GPT-style models beyond diffusion🚀 & Scaling laws observed📈

<div align="center">

&nbsp;
&nbsp;
&nbsp;



</div>
<p align="center" style="font-size: larger;">
  <a href="https://arxiv.org/abs/2404.02905">Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction</a>
</p>

<div>
  <p align="center" style="font-size: larger;">
    <strong>NeurIPS 2024 Best Paper</strong>
  </p>
</div>

<p align="center">
<img src="https://github.com/FoundationVision/VAR/assets/39692511/9850df90-20b1-4f29-8592-e3526d16d755" width=95%>
<p>

<br>

## News
* **2025-11:** We Release our Text-to-Video generation model **InfinityStar** based on VAR & Infinity, please check [Infinity⭐️](https://github.com/FoundationVision/InfinityStar).
* **2025-11:** 🎉 InfinityStar is accepted as **NeurIPS 2025 Oral.**
* **2025-04:** 🎉 Infinity is accepted as **CVPR 2025 Oral.**
* **2024-12:** 🏆 VAR received **NeurIPS 2024 Best Paper Award**.
* **2024-12:** 🔥 We Release our Text-to-Image research based on VAR, please check [Infinity](https://github.com/FoundationVision/Infinity).
* **2024-09:** VAR is accepted as **NeurIPS 2024 Oral** Presentation.
* **2024-04:** [Visual AutoRegressive modeling](https://github.com/FoundationVision/VAR) is released.

## 🕹️ Try and Play with VAR!

~~We provide a [demo website](https://var.vision/demo) for you to play with VAR models and generate images interactively. Enjoy the fun of visual autoregressive modeling!~~

We provide a [demo website](https://opensource.bytedance.com/gmpt/t2i/invite) for you to play with VAR Text-to-Image and generate images interactively. Enjoy the fun of visual autoregressive modeling!

We also provide [demo_sample.ipynb](demo_sample.ipynb) for you to see more technical details about VAR.

[//]: # (<p align="center">)
[//]: # (<img src="https://user-images.githubusercontent.com/39692511/226376648-3f28a1a6-275d-4f88-8f3e-cd1219882488.png" width=50%)
[//]: # (<p>)


## What's New?

### 🔥 Introducing VAR: a new paradigm in autoregressive visual generation✨:

Visual Autoregressive Modeling (VAR) redefines the autoregressive learning on images as coarse-to-fine "next-scale prediction" or "next-resolution prediction", diverging from the standard raster-scan "next-token prediction".

<p align="center">
<img src="https://github.com/FoundationVision/VAR/assets/39692511/3e12655c-37dc-4528-b923-ec6c4cfef178" width=93%>
<p>

### 🔥 For the first time, GPT-style autoregressive models surpass diffusion models🚀:
<p align="center">
<img src="https://github.com/FoundationVision/VAR/assets/39692511/cc30b043-fa4e-4d01-a9b1-e50650d5675d" width=55%>
<p>


### 🔥 Discovering power-law Scaling Laws in VAR transformers📈:


<p align="center">
<img src="https://github.com/FoundationVision/VAR/assets/39692511/c35fb56e-896e-4e4b-9fb9-7a1c38513804" width=85%>
<p>
<p align="center">
<img src="https://github.com/FoundationVision/VAR/assets/39692511/91d7b92c-8fc3-44d9-8fb4-73d6cdb8ec1e" width=85%>
<p>


### 🔥 Zero-shot generalizability🛠️:

<p align="center">
<img src="https://github.com/FoundationVision/VAR/assets/39692511/a54a4e52-6793-4130-bae2-9e459a08e96a" width=70%>
<p>

#### For a deep dive into our analyses, discussions, and evaluations, check out our [paper](https://arxiv.org/abs/2404.02905).


## VAR zoo
We provide VAR models for you to play with, which are on <a href='https://huggingface.co/FoundationVision/var'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Huggingface-FoundationVision/var-yellow'></a> or can be downloaded from the following links:

|   model    | reso. |   FID    | rel. cost | #params | HF weights🤗                                                                        |
|:----------:|:-----:|:--------:|:---------:|:-------:|:------------------------------------------------------------------------------------|
|  VAR-d16   |  256  |   3.55   |    0.4    |  310M   | [var_d16.pth](https://huggingface.co/FoundationVision/va
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/foundationvision-var`](/api/graphcanon/tools/foundationvision-var)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_