长久以来, 人们认为视觉Encoder重建质量越高,生成效果会越好, 该论文引入iFID:插值FID——一种与扩散模型生成质量强相关的新指标, 揭示了连通潜在空间对生成的意义
UniTok: A Unified Tokenizer for Visual Generation and Understanding
[RAE: Diffusion Transformers with Representation Autoencoders](https://aboard-hurricane-478.notion.site/RAE-Diffusion-Transformers-with-Representation-Autoencoders-3401baf34fa5812b87eed4e03068b60a)
Beyond Language Modeling:An Exploration of Multimodal Pretraining
SVG:Latent Diffusion Model without Variational Autoencoder
The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding
Making Reconstruction FID Predictive of Diffusion Generation FID: