Abstract
AutoRegressive (AR) models have made notable progress in image generation, with Masked AutoRegressive (MAR) models gaining attention for their efficient parallel decoding. However, MAR models have traditionally underperformed when compared to standard AR models. This study refines the MAR architecture to improve image generation quality. We begin by evaluating various image tokenizers to identify the most effective one. Subsequently, we introduce an improved Bidirectional LLaMA architecture by replacing causal attention with bidirectional attention and incorporating 2D RoPE, which together form our advanced model, MaskGIL. Scaled from 111M to 1.4B parameters, MaskGIL achieves a FID score of 3.71, matching state-of-the-art AR models in the ImageNet 256x256 benchmark, while requiring only 8 inference steps compared to the 256 steps of AR models. Furthermore, we develop a text-driven MaskGIL model with 775M parameters for generating images from text at various resolutions. Beyond image generation, MaskGIL extends to accelerate AR-based generation and enable real-time speech-to-image conversion. Our codes and models are available at this https URL.
Abstract (translated)
自回归（AutoRegressive，AR）模型在图像生成领域取得了显著进展，而遮蔽自回归（Masked AutoRegressive，MAR）模型由于其高效的并行解码能力受到了广泛关注。然而，与标准的AR模型相比，传统的MAR模型性能较差。本研究对MAR架构进行了改进，以提高图像生成质量。我们首先评估了各种图像标记器，以确定最有效的标记器。随后，我们通过将因果注意力替换为双向注意力，并引入2D RoPE来改进Bidirectional LLaMA架构，从而形成我们的先进模型MaskGIL。从1.11亿参数扩展到14亿参数规模的MaskGIL，在ImageNet 256x256基准测试中达到了3.71的FID评分，与最先进的AR模型相匹配，同时仅需8个推理步骤，而相比之下AR模型需要256个步骤。 此外，我们开发了一个具有7.75亿参数、基于文本驱动的MaskGIL模型，用于在各种分辨率下从文本生成图像。除了图像生成之外，MaskGIL还扩展了基于自回归的生成加速，并实现了实时语音转图像转换。我们的代码和模型可在以下网址获取：[此处插入链接]（请将实际URL填入）。
URL
https://arxiv.org/abs/2507.13032
