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

• URL: http://arxiv.org/abs/2606.26016v2
• Date: Mon, 29 Jun 2026 18:18:10 GMT
• Title: MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation
• Abstract summary: Masked Image Modeling (MIM) has excelled in representation learning, but its integration into generative pipelines has remained largely modular and disjointed.<n>We propose MIMFlow, a unified end-to-end framework that jointly optimize latent semantics, pixel reconstruction, and generative flow.
• Score: 29.57000468788334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Normalizing Flows (NFs) are powerful generative models capable of exact density estimation and sampling. However, their strict invertibility often forces the model to exhaust its capacity on low-level pixel details, hindering the capture of high-level semantic structures. While Masked Image Modeling (MIM) has excelled in representation learning, its integration into generative pipelines has remained largely modular and disjointed. In this paper, we propose MIMFlow, a unified end-to-end framework that jointly optimizes latent semantics, pixel reconstruction, and generative flow. By employing a VAE encoder to infer semantic latent from masked images, MIMFlow achieves a principled decoupling of the generative task: the Normalizing Flow focuses on modeling a simplified, low-frequency semantic manifold, while a specialized decoder handles high-frequency synthesis. This design effectively resolves the inherent capacity bottleneck of NFs, allowing the model to prioritize global structural coherence over redundant noise. Empirical results on ImageNet 256$\times$256 show that MIMFlow-L reaches 71.3\% linear probing accuracy and an FID of 2.50. Despite using only 128 tokens (50\% fewer than standard models), it yields a 32.8\% performance gain over similar-scale NF baselines. Our code is available at https://github.com/MCG-NJU/MIMFlow.

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