Effective and Efficient Masked Image Generation Models
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.
67cfa76cf36e4221c5009654
Zebin You
claimed_verified
2025-03-11T08:22:38.059Z
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/avatars/0aafdb1cbb492fda52a0303031cc6c14.svg
Zebin You
user
yyyou
67cfa76cf36e4221c5009655
Jingyang Ou
admin_assigned
2025-03-11T14:51:53.871Z
656949b71d7c2ca7b7aae5f2
/avatars/e7b23e260eb348cc26b849aaa601a503.svg
Jingyang Ou
user
JingyangOu
67cfa76cf36e4221c5009656
Xiaolu Zhang
admin_assigned
2025-03-11T14:52:08.690Z
67513d6d3b8586521cda5d76
/avatars/0f95cc5c23a0a1da289aa785bd33b616.svg
Xiaolu  Zhang
user
xiaolu0714
67cfa76cf36e4221c5009657
Jun Hu
67cfa76cf36e4221c5009658
Jun Zhou
67cfa76cf36e4221c5009659
Chongxuan Li
admin_assigned
2025-03-11T14:52:20.109Z
64c07b488e2612254361153b
/avatars/ade0f783cc4c2d3e73f402637f595471.svg
chongxuan li
user
zhenxuan00
Effective and Efficient Masked Image Generation Models
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.
A unified framework for masked image generation and diffusion models results in a model, eMIGM, that achieves strong performance on ImageNet generation with fewer function evaluations compared to state-of-the-art models.
masked image generation models masked diffusion models training sampling Fr\'echet Inception Distance (FID) VAR continuous diffusion models function evaluations (NFEs) model parameters