title: FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing
summary: Though Rectified Flows (ReFlows) with distillation offers a promising way for
  fast sampling, its fast inversion transforms images back to structured noise
  for recovery and following editing remains unsolved. This paper introduces
  FireFlow, a simple yet effective zero-shot approach that inherits the startling
  capacity of ReFlow-based models (such as FLUX) in generation while extending
  its capabilities to accurate inversion and editing in 8 steps. We first
  demonstrate that a carefully designed numerical solver is pivotal for ReFlow
  inversion, enabling accurate inversion and reconstruction with the precision of
  a second-order solver while maintaining the practical efficiency of a
  first-order Euler method. This solver achieves a 3times runtime speedup
  compared to state-of-the-art ReFlow inversion and editing techniques, while
  delivering smaller reconstruction errors and superior editing results in a
  training-free mode. The code is available at
  https://github.com/HolmesShuan/FireFlow{this URL}.
paper_authors._id: 675b9172aeae790a46d985b1
paper_authors.name: Yingying Deng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T10:20:24.256Z
paper_authors._id: 66be27e39284c8209f5e82fe
paper_authors.avatarUrl: /avatars/5c59b74a364107654f343c3904bcfae4.svg
paper_authors.fullname: dengyingying
paper_authors.type: user
paper_authors.user: yingying87
paper_authors._id: 675b9172aeae790a46d985b2
paper_authors.name: Xiangyu He
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-13T15:22:40.083Z
paper_authors._id: 6422430fe61513ec1c687165
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paper_authors.fullname: Xiangyu He
paper_authors.type: user
paper_authors.user: MagicBag
paper_authors._id: 675b9172aeae790a46d985b3
paper_authors.name: Changwang Mei
paper_authors._id: 675b9172aeae790a46d985b4
paper_authors.name: Peisong Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T10:19:47.517Z
paper_authors._id: 626f98528a894872cfbf620c
paper_authors.avatarUrl: /avatars/fe31d20313e6ca85e96bc249424c5383.svg
paper_authors.fullname: Peisong Wang
paper_authors.type: user
paper_authors.user: duke1852022
paper_authors._id: 675b9172aeae790a46d985b5
paper_authors.name: Fan Tang
paper_title: FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing
paper_summary: Though Rectified Flows (ReFlows) with distillation offers a promising way for
  fast sampling, its fast inversion transforms images back to structured noise
  for recovery and following editing remains unsolved. This paper introduces
  FireFlow, a simple yet effective zero-shot approach that inherits the startling
  capacity of ReFlow-based models (such as FLUX) in generation while extending
  its capabilities to accurate inversion and editing in 8 steps. We first
  demonstrate that a carefully designed numerical solver is pivotal for ReFlow
  inversion, enabling accurate inversion and reconstruction with the precision of
  a second-order solver while maintaining the practical efficiency of a
  first-order Euler method. This solver achieves a 3times runtime speedup
  compared to state-of-the-art ReFlow inversion and editing techniques, while
  delivering smaller reconstruction errors and superior editing results in a
  training-free mode. The code is available at
  https://github.com/HolmesShuan/FireFlow{this URL}.
paper_ai_summary: FireFlow, a ReFlow-based method, achieves fast and accurate image inversion and editing in under eight steps using an efficient numerical solver.
paper_ai_keywords: Rectified Flows ReFlows distillation fast sampling structured noise zero-shot approach FLUX numerical solver second-order solver first-order Euler method runtime speedup reconstruction errors editing results training-free mode