LTH14/mar

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1949 stars · 124 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of MAR (Masked Autoregressive) image generation with DiffLoss, from the NeurIPS 2024 paper 'Autoregressive Image Generation without Vector Quantization'. It includes pre-trained class-conditional models on ImageNet 256x256, training/evaluation scripts, and a Colab demo.

Use cases

• generate images autoregressively without vector quantization
• run pre-trained MAR models on ImageNet class-conditional generation
• train MAR with DiffLoss using PyTorch DDP
• reproduce results from the MAR paper
• evaluate FID and Inception Score for image generation models
• try image generation in a Colab notebook

When to choose

• you want to experiment with or reproduce state-of-the-art autoregressive image generation without VQ tokenizers
• you need pre-trained ImageNet 256x256 class-conditional generation models
• you want a simple PyTorch reference implementation of MAR and DiffLoss

When to avoid

• you need text-to-image or general-purpose image generation rather than class-conditional ImageNet generation
• you lack GPU resources for training or sampling
• you need a production-ready image generation service rather than research code

Facets

library · maturity stable

machine-learning deep-learning image-processing deep-learning computer-vision image-processing artificial-intelligence python pytorch image-generation autoregressive-model diffusion-loss diffloss imagenet research-code pretrained-models gpu

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