title: FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality
    Samples with Less Compute
summary: Despite their remarkable performance, modern Diffusion Transformers are
  hindered by substantial resource requirements during inference, stemming from
  the fixed and large amount of compute needed for each denoising step. In this
  work, we revisit the conventional static paradigm that allocates a fixed
  compute budget per denoising iteration and propose a dynamic strategy instead.
  Our simple and sample-efficient framework enables pre-trained DiT models to be
  converted into flexible ones -- dubbed FlexiDiT -- allowing them to
  process inputs at varying compute budgets. We demonstrate how a single
  flexible model can generate images without any drop in quality, while
  reducing the required FLOPs by more than 40\% compared to their static
  counterparts, for both class-conditioned and text-conditioned image generation.
  Our method is general and agnostic to input and conditioning modalities. We
  show how our approach can be readily extended for video generation, where
  FlexiDiT models generate samples with up to 75\% less compute without
  compromising performance.
paper_authors._id: 67c14524af5eaa8dd062a216
paper_authors.name: Sotiris Anagnostidis
paper_authors.status: admin_assigned
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paper_authors.fullname: Sotiris Anagnostidis
paper_authors.type: user
paper_authors.user: sanagnos
paper_authors._id: 67c14524af5eaa8dd062a217
paper_authors.name: Gregor Bachmann
paper_authors.status: admin_assigned
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paper_authors.fullname: Gregor Bachmann
paper_authors.type: user
paper_authors.user: gregorbachmann
paper_authors._id: 67c14524af5eaa8dd062a218
paper_authors.name: Yeongmin Kim
paper_authors.status: admin_assigned
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paper_authors._id: 65d6a010c57d1c140e395e31
paper_authors.avatarUrl: /avatars/ddc9cfc98da36b639bd9205ee65b6967.svg
paper_authors.fullname: Yeongmin Kim
paper_authors.type: user
paper_authors.user: YeongminKim
paper_authors._id: 67c14524af5eaa8dd062a219
paper_authors.name: Jonas Kohler
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-28T12:32:36.925Z
paper_authors._id: 6408a3a19e9f790c905281c2
paper_authors.avatarUrl: /avatars/3517962e54bda141018e13f7e21fb1ae.svg
paper_authors.fullname: jonas köhler
paper_authors.type: user
paper_authors.user: Jonaskohler
paper_authors._id: 67c14524af5eaa8dd062a21a
paper_authors.name: Markos Georgopoulos
paper_authors._id: 67c14524af5eaa8dd062a21b
paper_authors.name: Artsiom Sanakoyeu
paper_authors._id: 67c14524af5eaa8dd062a21c
paper_authors.name: Yuming Du
paper_authors._id: 67c14524af5eaa8dd062a21d
paper_authors.name: Albert Pumarola
paper_authors._id: 67c14524af5eaa8dd062a21e
paper_authors.name: Ali Thabet
paper_authors._id: 67c14524af5eaa8dd062a21f
paper_authors.name: Edgar Schönfeld
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-02T20:18:03.571Z
paper_authors._id: 6571c662d5c6a6d3b0bdae88
paper_authors.avatarUrl: /avatars/8080bcdf8b331f62383b724050189660.svg
paper_authors.fullname: Edgar Schoenfeld
paper_authors.type: user
paper_authors.user: edgarschoenfeld
paper_title: FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality
    Samples with Less Compute
paper_summary: Despite their remarkable performance, modern Diffusion Transformers are
  hindered by substantial resource requirements during inference, stemming from
  the fixed and large amount of compute needed for each denoising step. In this
  work, we revisit the conventional static paradigm that allocates a fixed
  compute budget per denoising iteration and propose a dynamic strategy instead.
  Our simple and sample-efficient framework enables pre-trained DiT models to be
  converted into flexible ones -- dubbed FlexiDiT -- allowing them to
  process inputs at varying compute budgets. We demonstrate how a single
  flexible model can generate images without any drop in quality, while
  reducing the required FLOPs by more than 40\% compared to their static
  counterparts, for both class-conditioned and text-conditioned image generation.
  Our method is general and agnostic to input and conditioning modalities. We
  show how our approach can be readily extended for video generation, where
  FlexiDiT models generate samples with up to 75\% less compute without
  compromising performance.
paper_ai_keywords: Diffusion Transformers denoising step compute budget denoising iteration flexible FlexiDiT pre-trained DiT models framework sample-efficient compute FLOPs class-conditioned text-conditioned video generation downstream tasks performance