title: Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow
summary: Diffusion models have greatly improved visual generation but are hindered by
  slow generation speed due to the computationally intensive nature of solving
  generative ODEs. Rectified flow, a widely recognized solution, improves
  generation speed by straightening the ODE path. Its key components include: 1)
  using the diffusion form of flow-matching, 2) employing boldsymbol
  v-prediction, and 3) performing rectification (a.k.a. reflow). In this paper,
  we argue that the success of rectification primarily lies in using a pretrained
  diffusion model to obtain matched pairs of noise and samples, followed by
  retraining with these matched noise-sample pairs. Based on this, components 1)
  and 2) are unnecessary. Furthermore, we highlight that straightness is not an
  essential training target for rectification; rather, it is a specific case of
  flow-matching models. The more critical training target is to achieve a
  first-order approximate ODE path, which is inherently curved for models like
  DDPM and Sub-VP. Building on this insight, we propose Rectified Diffusion,
  which generalizes the design space and application scope of rectification to
  encompass the broader category of diffusion models, rather than being
  restricted to flow-matching models. We validate our method on Stable Diffusion
  v1-5 and Stable Diffusion XL. Our method not only greatly simplifies the
  training procedure of rectified flow-based previous works (e.g., InstaFlow) but
  also achieves superior performance with even lower training cost. Our code is
  available at https://github.com/G-U-N/Rectified-Diffusion.
paper_authors._id: 6708940226920e3023fb2ed5
paper_authors.name: Fu-Yun Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-11T07:43:36.040Z
paper_authors._id: 63e9e92f20c109718713f5eb
paper_authors.avatarUrl: /avatars/9ff312e854d803e1a2e9e685a21d12f8.svg
paper_authors.fullname: Fu-Yun Wang
paper_authors.type: user
paper_authors.user: wangfuyun
paper_authors._id: 6708940226920e3023fb2ed6
paper_authors.name: Ling Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-11T15:34:23.400Z
paper_authors._id: 64fde4e252e82dd432b74ce9
paper_authors.avatarUrl: /avatars/061a69d858b86d1600be916122cae7fc.svg
paper_authors.fullname: Ling Yang
paper_authors.type: user
paper_authors.user: Lingaaaaaaa
paper_authors._id: 6708940226920e3023fb2ed7
paper_authors.name: Zhaoyang Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-11T10:39:51.464Z
paper_authors._id: 64c26e0e400274d129a10d5d
paper_authors.avatarUrl: /avatars/c1debf3d7f61c2ea01d9a25d7e332712.svg
paper_authors.fullname: Zhaoyang Huang
paper_authors.type: user
paper_authors.user: DrinkingCoder
paper_authors._id: 6708940226920e3023fb2ed8
paper_authors.name: Mengdi Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-11T10:40:16.489Z
paper_authors._id: 6599415e8c8ac79295e0b5e3
paper_authors.avatarUrl: /avatars/85500bc8d2cd51444adcc19b1f8db313.svg
paper_authors.fullname: Mengdi Wang
paper_authors.type: user
paper_authors.user: Edify-Kd2024
paper_authors._id: 6708940226920e3023fb2ed9
paper_authors.name: Hongsheng Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-11T10:40:23.050Z
paper_authors._id: 65c04e9c27a5fdca81abcbd9
paper_authors.avatarUrl: /avatars/12a155683c824fa23da4a9e2bed4f64e.svg
paper_authors.fullname: Hongsheng LI
paper_authors.type: user
paper_authors.user: hsli-cuhk
paper_title: Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow
paper_summary: Diffusion models have greatly improved visual generation but are hindered by
  slow generation speed due to the computationally intensive nature of solving
  generative ODEs. Rectified flow, a widely recognized solution, improves
  generation speed by straightening the ODE path. Its key components include: 1)
  using the diffusion form of flow-matching, 2) employing boldsymbol
  v-prediction, and 3) performing rectification (a.k.a. reflow). In this paper,
  we argue that the success of rectification primarily lies in using a pretrained
  diffusion model to obtain matched pairs of noise and samples, followed by
  retraining with these matched noise-sample pairs. Based on this, components 1)
  and 2) are unnecessary. Furthermore, we highlight that straightness is not an
  essential training target for rectification; rather, it is a specific case of
  flow-matching models. The more critical training target is to achieve a
  first-order approximate ODE path, which is inherently curved for models like
  DDPM and Sub-VP. Building on this insight, we propose Rectified Diffusion,
  which generalizes the design space and application scope of rectification to
  encompass the broader category of diffusion models, rather than being
  restricted to flow-matching models. We validate our method on Stable Diffusion
  v1-5 and Stable Diffusion XL. Our method not only greatly simplifies the
  training procedure of rectified flow-based previous works (e.g., InstaFlow) but
  also achieves superior performance with even lower training cost. Our code is
  available at https://github.com/G-U-N/Rectified-Diffusion.
paper_ai_summary: Rectified Diffusion improves generation speed and performance by retraining with matched noise-sample pairs, simplifying the training procedure compared to previous methods.
paper_ai_keywords: diffusion models generative ODEs rectified flow flow-matching v-prediction reflow Stable Diffusion v1-5 Stable Diffusion XL InstaFlow