This repository provides the official PyTorch implementation of MAR (Masked Autoregressive with Diffusion Loss), a research method for class-conditional image generation on ImageNet 256x256. The core contribution is an autoregressive model that operates on continuous-valued latent tokens (via a pre-trained VAE) and uses a diffusion loss instead of vector quantization. The codebase includes model definitions (MAR encoder-decoder transformer, DiffLoss MLP), training and evaluation scripts with PyTorch DDP, a VAE latent caching pipeline, and pre-trained checkpoints for base/large/huge variants. While primarily a research prototype, it contains reusable components such as the DiffLoss module, diffusion utilities, and a clean data caching pattern. The Gradio app and Colab notebook make the model accessible for interactive use.