title: Open-MAGVIT2: An Open-Source Project Toward Democratizing
    Auto-regressive Visual Generation
summary: We present Open-MAGVIT2, a family of auto-regressive image generation models
  ranging from 300M to 1.5B. The Open-MAGVIT2 project produces an open-source
  replication of Google's MAGVIT-v2 tokenizer, a tokenizer with a super-large
  codebook (i.e., 2^{18} codes), and achieves the state-of-the-art
  reconstruction performance (1.17 rFID) on ImageNet 256 times 256.
  Furthermore, we explore its application in plain auto-regressive models and
  validate scalability properties. To assist auto-regressive models in predicting
  with a super-large vocabulary, we factorize it into two sub-vocabulary of
  different sizes by asymmetric token factorization, and further introduce "next
  sub-token prediction" to enhance sub-token interaction for better generation
  quality. We release all models and codes to foster innovation and creativity in
  the field of auto-regressive visual generation.
paper_authors._id: 66de6c935af9428df34917b4
paper_authors.name: Zhuoyan Luo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-09T08:23:00.548Z
paper_authors._id: 657187de7644d1128571495e
paper_authors.avatarUrl: /avatars/89412c94fd6136f6680055551de3ddc4.svg
paper_authors.fullname: Zhuoyan Luo
paper_authors.type: user
paper_authors.user: RobertLuo1
paper_authors._id: 66de6c935af9428df34917b5
paper_authors.name: Fengyuan Shi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-09T08:26:16.830Z
paper_authors._id: 666c1232a7ca4800af0545d7
paper_authors.avatarUrl: /avatars/b4713c86ee57f30aae478262634692fe.svg
paper_authors.fullname: Fengyuan Shi
paper_authors.type: user
paper_authors.user: shifengyuan
paper_authors._id: 66de6c935af9428df34917b6
paper_authors.name: Yixiao Ge
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-09T08:26:23.973Z
paper_authors._id: 640e9762b03f4cd29f58d982
paper_authors.avatarUrl: /avatars/81da37d628163fe3e094b247c7c3a3b5.svg
paper_authors.fullname: Yixiao Ge
paper_authors.type: user
paper_authors.user: yxgeee
paper_authors._id: 66de6c935af9428df34917b7
paper_authors.name: Yujiu Yang
paper_authors._id: 66de6c935af9428df34917b8
paper_authors.name: Limin Wang
paper_authors._id: 66de6c935af9428df34917b9
paper_authors.name: Ying Shan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-09T08:27:34.082Z
paper_authors._id: 63ca3ddc04c979828310bfcb
paper_authors.avatarUrl: /avatars/615e0d8622950b4408b40d550f02a894.svg
paper_authors.fullname: Ying Shan
paper_authors.type: user
paper_authors.user: yshan2u
paper_title: Open-MAGVIT2: An Open-Source Project Toward Democratizing
    Auto-regressive Visual Generation
paper_summary: We present Open-MAGVIT2, a family of auto-regressive image generation models
  ranging from 300M to 1.5B. The Open-MAGVIT2 project produces an open-source
  replication of Google's MAGVIT-v2 tokenizer, a tokenizer with a super-large
  codebook (i.e., 2^{18} codes), and achieves the state-of-the-art
  reconstruction performance (1.17 rFID) on ImageNet 256 times 256.
  Furthermore, we explore its application in plain auto-regressive models and
  validate scalability properties. To assist auto-regressive models in predicting
  with a super-large vocabulary, we factorize it into two sub-vocabulary of
  different sizes by asymmetric token factorization, and further introduce "next
  sub-token prediction" to enhance sub-token interaction for better generation
  quality. We release all models and codes to foster innovation and creativity in
  the field of auto-regressive visual generation.
paper_ai_summary: Open-MAGVIT2 models, ranging from 300M to 1.5B parameters, achieve state-of-the-art image reconstruction and exploration in auto-regressive models with super-large token vocabularies.
paper_ai_keywords: auto-regressive image generation Open-MAGVIT2 MAGVIT-v2 tokenizer super-large codebook reconstruction performance asymmetric token factorization next sub-token prediction