mar
This article objectively describes a method for autoregressive image generation without vector quantization. It includes a PyTorch implementation and class-conditional MAR models trained on ImageNet 256x256, as well as the DiffLoss method. Resources and scripts provided in the repository aid in training and evaluation using PyTorch DDP, with detailed documentation and a Colab notebook demonstration. Ideal for developers and researchers in image generation, it offers tools, models, and visual demos to explore this advanced image synthesis technique.
