MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation

Normalizing Flows (NFs) are powerful generative models ca-pable of exact density estimation and sampling. However, their strictinvertibility often forces the model to exhaust its capacity on low-levelpixel details, hindering the capture of high-level semantic structures.While Masked Image Modeling (MIM) has excelled in representationlearning, its integration into generative pipelines has remained largelymodular and disjointed. In this paper, we propose MIMFlow, a unifiedend-to-end framework that jointly optimizes latent semantics, pixel re-construction, and generative flow. By employing a VAE encoder to infersemantic latent from masked images, MIMFlow achieves a principleddecoupling of the generative task: the Normalizing Flow focuses on mod-eling a simplified, low-frequency semantic manifold, while a specializeddecoder handles high-frequency synthesis. This design effectively resolvesthe inherent capacity bottleneck of NFs, allowing the model to prioritizeglobal structural coherence over redundant noise. Empirical results onImageNet 256×256 show that MIMFlow-L reaches 71.3% linear prob-ing accuracy and an FID of 2.50. Despite using only 128 tokens (50%fewer than standard models), it yields a 32.8% performance gain oversimilar-scale NF baselines.