Effective and Efficient Masked Image Generation Models

Understand

Although masked image generation models and masked diffusion models are designed with different motivations and objectives, we observe that they can be unified within a single framework.

• Building upon this insight, we carefully explore the design space of training and sampling, identifying key factors that contribute to both performance and efficiency.
• Based on the improvements observed during this exploration, we develop our model, referred to as \textbf{eMIGM}.
• Empirically, eMIGM demonstrates strong performance on ImageNet generation, as measured by Fr\'echet Inception Distance (FID).