VAR
Official implementation of Visual Autoregressive Modeling for scalable image generation
GraphCanon updated 4d · GitHub synced 4d · 33 views this month
Decision brief
VAR is an ultra-simple user-friendly state-of-the-art codebase for autoregressive image generation
Good fit when
- When you prefer a straightforward implementation with minimal configuration effort
- If fast attention computation acceleration from flash-attn or xformers is beneficial
Avoid when
- Avoid if your project requires complex customization beyond basic VAR parameters
- Not ideal when you do not have access to or willingness to prepare the ImageNet dataset in a specific structure
Observed Jul 15, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Slowing (279d since push)
- As of 4d
- Provenance
- Not a fork · Organization account
- As of 4d
- Security (OSV)
- No criticals
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/FoundationVision/VARHow it fits your stack(4)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Integrates
Relationship graph
Optional deeper exploration of typed edges and category neighbours.
Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Ultra-simple and user-friendly state-of-the-art codebase for autoregressive image generation
Capability facts
- Languages
- jupyter notebook
Source: github.language · Aug 17, 2026
Categories
Tags
README
Installation
-
Install
torch>=2.0.0. -
Install other pip packages via
pip3 install -r requirements.txt. -
Prepare the ImageNet dataset
assume the ImageNet is in `/path/to/imagenet`. It should be like this:
/path/to/imagenet/: train/: n01440764: many_images.JPEG ... n01443537: many_images.JPEG ... val/: n01440764: ILSVRC2012_val_00000293.JPEG ... n01443537: ILSVRC2012_val_00000236.JPEG ...NOTE: The arg
--data_path=/path/to/imagenetshould be passed to the training script. -
(Optional) install and compile
flash-attnandxformersfor faster attention computation. Our code will automatically use them if installed. See models/basic_var.py#L15-L30.
License
This project is licensed under the MIT License - see the LICENSE file for details.
For agents
This page has a .md twin and JSON over the API.