title: Effective and Efficient Masked Image Generation Models
summary: 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.
paper_authors._id: 67cfa76cf36e4221c5009654
paper_authors.name: Zebin You
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-11T08:22:38.059Z
paper_authors._id: 624f909eac5dd186b01ac3f5
paper_authors.avatarUrl: /avatars/71a5c93c491064ef9e1eda80fda90665.svg
paper_authors.fullname: Zebin You
paper_authors.type: user
paper_authors.user: yyyou
paper_authors._id: 67cfa76cf36e4221c5009655
paper_authors.name: Jingyang Ou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-11T14:51:53.871Z
paper_authors._id: 656949b71d7c2ca7b7aae5f2
paper_authors.avatarUrl: /avatars/e7b23e260eb348cc26b849aaa601a503.svg
paper_authors.fullname: Jingyang Ou
paper_authors.type: user
paper_authors.user: JingyangOu
paper_authors._id: 67cfa76cf36e4221c5009656
paper_authors.name: Xiaolu Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-11T14:52:08.690Z
paper_authors._id: 67513d6d3b8586521cda5d76
paper_authors.avatarUrl: /avatars/0f95cc5c23a0a1da289aa785bd33b616.svg
paper_authors.fullname: Xiaolu  Zhang
paper_authors.type: user
paper_authors.user: xiaolu0714
paper_authors._id: 67cfa76cf36e4221c5009657
paper_authors.name: Jun Hu
paper_authors._id: 67cfa76cf36e4221c5009658
paper_authors.name: Jun Zhou
paper_authors._id: 67cfa76cf36e4221c5009659
paper_authors.name: Chongxuan Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-11T14:52:20.109Z
paper_authors._id: 64c07b488e2612254361153b
paper_authors.avatarUrl: /avatars/ade0f783cc4c2d3e73f402637f595471.svg
paper_authors.fullname: chongxuan li
paper_authors.type: user
paper_authors.user: zhenxuan00
paper_title: Effective and Efficient Masked Image Generation Models
paper_summary: 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.
paper_ai_keywords: masked image generation models masked diffusion models training and sampling Fr\'echet Inception Distance (FID) function evaluations (NFEs) VAR continuous diffusion models