title: Illustrious: an Open Advanced Illustration Model
summary: In this work, we share the insights for achieving state-of-the-art quality in
  our text-to-image anime image generative model, called Illustrious. To achieve
  high resolution, dynamic color range images, and high restoration ability, we
  focus on three critical approaches for model improvement. First, we delve into
  the significance of the batch size and dropout control, which enables faster
  learning of controllable token based concept activations. Second, we increase
  the training resolution of images, affecting the accurate depiction of
  character anatomy in much higher resolution, extending its generation
  capability over 20MP with proper methods. Finally, we propose the refined
  multi-level captions, covering all tags and various natural language captions
  as a critical factor for model development. Through extensive analysis and
  experiments, Illustrious demonstrates state-of-the-art performance in terms of
  animation style, outperforming widely-used models in illustration domains,
  propelling easier customization and personalization with nature of open source.
  We plan to publicly release updated Illustrious model series sequentially as
  well as sustainable plans for improvements.
paper_authors._id: 66fb911b028e0d22bc061206
paper_authors.name: Sang Hyun Park
paper_authors.status: admin_assigned
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paper_authors.fullname: Sang Hyun Park
paper_authors.type: user
paper_authors.user: andyp-nvidia
paper_authors._id: 66fb911b028e0d22bc061207
paper_authors.name: Jun Young Koh
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paper_authors.fullname: JUNYOUNG KOH
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paper_authors.user: solbon1212
paper_authors._id: 66fb911b028e0d22bc061208
paper_authors.name: Junha Lee
paper_authors._id: 66fb911b028e0d22bc061209
paper_authors.name: Joy Song
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paper_authors.fullname: Joy Song
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paper_authors.user: fashiongirl9
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paper_authors.name: Dongha Kim
paper_authors._id: 66fb911b028e0d22bc06120b
paper_authors.name: Hoyeon Moon
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-02T08:55:06.114Z
paper_authors._id: 651d27e3a00c49c5e50c0653
paper_authors.avatarUrl: /avatars/2c62a11c0502e6887f1cba97f9e259a6.svg
paper_authors.fullname: Hoyeon Moon
paper_authors.type: user
paper_authors.user: mhy9910
paper_authors._id: 66fb911b028e0d22bc06120c
paper_authors.name: Hyunju Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-02T08:55:50.662Z
paper_authors._id: 66e2bc2f0d7950a94e8d390f
paper_authors.avatarUrl: /avatars/fae051c59518af1f121de468eab910a0.svg
paper_authors.fullname: Hyunju Lee
paper_authors.type: user
paper_authors.user: leehg57
paper_authors._id: 66fb911b028e0d22bc06120d
paper_authors.name: Min Song
paper_title: Illustrious: an Open Advanced Illustration Model
paper_summary: In this work, we share the insights for achieving state-of-the-art quality in
  our text-to-image anime image generative model, called Illustrious. To achieve
  high resolution, dynamic color range images, and high restoration ability, we
  focus on three critical approaches for model improvement. First, we delve into
  the significance of the batch size and dropout control, which enables faster
  learning of controllable token based concept activations. Second, we increase
  the training resolution of images, affecting the accurate depiction of
  character anatomy in much higher resolution, extending its generation
  capability over 20MP with proper methods. Finally, we propose the refined
  multi-level captions, covering all tags and various natural language captions
  as a critical factor for model development. Through extensive analysis and
  experiments, Illustrious demonstrates state-of-the-art performance in terms of
  animation style, outperforming widely-used models in illustration domains,
  propelling easier customization and personalization with nature of open source.
  We plan to publicly release updated Illustrious model series sequentially as
  well as sustainable plans for improvements.
paper_ai_keywords: batch size dropout control token based concept activations training resolution character anatomy multi-level captions state-of-the-art performance animation style illustration domains open source