流到模式：用于最新图像标记化的模式寻求扩散自编码器
Flow to the Mode: Mode-Seeking Diffusion Autoencoders for State-of-the-Art Image Tokenization
AI总结：
FlowMo是一种基于Transformer的扩散自编码器，通过模式匹配和模式寻求阶段实现图像标记化的新SOTA，无需卷积、对抗损失等。
摘要：
自从VQGAN和潜在扩散模型等流行视觉生成框架问世以来，最先进的图像生成系统通常都是两阶段系统，首先将视觉数据压缩或压缩成较低维度的潜在空间，然后再学习生成模型。标记器训练通常遵循标准配方，在压缩和重建图像时结合MSE、感知和对抗性损失。在先前工作中，扩散自编码器被提出作为一种学习端到端感知导向图像压缩的方法，但尚未在ImageNet-1K重建的竞赛任务中展示出最先进的性能。我们提出了FlowMo，一种基于Transformer的扩散自编码器，它在多种压缩率下实现了图像标记化的新的最先进的性能，而无需使用卷积、对抗性损失、空间对齐的二维潜在代码或从其他标记器中蒸馏。我们的关键见解是，FlowMo的训练应分为模式匹配预训练阶段和模式寻求后训练阶段。此外，我们进行了广泛的分析，并探索了在FlowMo标记器之上训练生成模型。我们的代码和模型将在http://kylesargent.github.io/flowmo上提供。
英文摘要：
Since the advent of popular visual generation frameworks like VQGAN and latent diffusion models, state-of-the-art image generation systems have generally been two-stage systems that first tokenize or compress visual data into a lower-dimensional latent space before learning a generative model. Tokenizer training typically follows a standard recipe in which images are compressed and reconstructed subject to a combination of MSE, perceptual, and adversarial losses. Diffusion autoencoders have been proposed in prior work as a way to learn end-to-end perceptually-oriented image compression, but have not yet shown state-of-the-art performance on the competitive task of ImageNet-1K reconstruction. We propose FlowMo, a transformer-based diffusion autoencoder that achieves a new state-of-the-art for image tokenization at multiple compression rates without using convolutions, adversarial losses, spatially-aligned two-dimensional latent codes, or distilling from other tokenizers. Our key insight is that FlowMo training should be broken into a mode-matching pre-training stage and a mode-seeking post-training stage. In addition, we conduct extensive analyses and explore the training of generative models atop the FlowMo tokenizer. Our code and models will be available at http://kylesargent.github.io/flowmo .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
