Effective and Efficient Masked Image Generation Models
Authors: name: Zebin You, name: Jingyang Ou, name: Xiaolu Zhang, name: Jun Hu, name: Jun Zhou, name: Chongxuan Li

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 eMIGM.
Empirically, eMIGM demonstrates strong performance on ImageNet generation, as
measured by Fr\'echet Inception Distance (FID). In particular, on ImageNet
256x256, with similar number of function evaluations (NFEs) and model
parameters, eMIGM outperforms the seminal VAR. Moreover, as NFE and model
parameters increase, eMIGM achieves performance comparable to the
state-of-the-art continuous diffusion models while requiring less than 40% of
the NFE. Additionally, on ImageNet 512x512, with only about 60% of the NFE,
eMIGM outperforms the state-of-the-art continuous diffusion models.