title: SenseFlow: Scaling Distribution Matching for Flow-based Text-to-Image
    Distillation
summary: The Distribution Matching Distillation (DMD) has been successfully applied to
  text-to-image diffusion models such as Stable Diffusion (SD) 1.5. However,
  vanilla DMD suffers from convergence difficulties on large-scale flow-based
  text-to-image models, such as SD 3.5 and FLUX. In this paper, we first analyze
  the issues when applying vanilla DMD on large-scale models. Then, to overcome
  the scalability challenge, we propose implicit distribution alignment (IDA) to
  regularize the distance between the generator and fake distribution.
  Furthermore, we propose intra-segment guidance (ISG) to relocate the timestep
  importance distribution from the teacher model. With IDA alone, DMD converges
  for SD 3.5; employing both IDA and ISG, DMD converges for SD 3.5 and FLUX.1
  dev. Along with other improvements such as scaled up discriminator models, our
  final model, dubbed SenseFlow, achieves superior performance in
  distillation for both diffusion based text-to-image models such as SDXL, and
  flow-matching models such as SD 3.5 Large and FLUX. The source code will be
  avaliable at https://github.com/XingtongGe/SenseFlow.
paper_authors._id: 683efe2880e41655390ab9f5
paper_authors.name: Xingtong Ge
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T09:02:22.769Z
paper_authors._id: 65a94d2bbe0b2002351b6a8e
paper_authors.avatarUrl: /avatars/c2a7a4bff254c643c1158a20dd74dc0f.svg
paper_authors.fullname: Xingtong Ge
paper_authors.type: user
paper_authors.user: domiso
paper_authors._id: 683efe2880e41655390ab9f6
paper_authors.name: Xin Zhang
paper_authors._id: 683efe2880e41655390ab9f7
paper_authors.name: Tongda Xu
paper_authors._id: 683efe2880e41655390ab9f8
paper_authors.name: Yi Zhang
paper_authors._id: 683efe2880e41655390ab9f9
paper_authors.name: Xinjie Zhang
paper_authors._id: 683efe2880e41655390ab9fa
paper_authors.name: Yan Wang
paper_authors._id: 683efe2880e41655390ab9fb
paper_authors.name: Jun Zhang
paper_title: SenseFlow: Scaling Distribution Matching for Flow-based Text-to-Image
    Distillation
paper_summary: The Distribution Matching Distillation (DMD) has been successfully applied to
  text-to-image diffusion models such as Stable Diffusion (SD) 1.5. However,
  vanilla DMD suffers from convergence difficulties on large-scale flow-based
  text-to-image models, such as SD 3.5 and FLUX. In this paper, we first analyze
  the issues when applying vanilla DMD on large-scale models. Then, to overcome
  the scalability challenge, we propose implicit distribution alignment (IDA) to
  regularize the distance between the generator and fake distribution.
  Furthermore, we propose intra-segment guidance (ISG) to relocate the timestep
  importance distribution from the teacher model. With IDA alone, DMD converges
  for SD 3.5; employing both IDA and ISG, DMD converges for SD 3.5 and FLUX.1
  dev. Along with other improvements such as scaled up discriminator models, our
  final model, dubbed SenseFlow, achieves superior performance in
  distillation for both diffusion based text-to-image models such as SDXL, and
  flow-matching models such as SD 3.5 Large and FLUX. The source code will be
  avaliable at https://github.com/XingtongGe/SenseFlow.
paper_ai_summary: Implicit distribution alignment and intra-segment guidance enhance distribution matching distillation for large-scale text-to-image and flow-based models, improving convergence and performance.
paper_ai_keywords: implicit distribution alignment intra-segment guidance distribution matching distillation text-to-image diffusion models flow-based models generator fake distribution timestep importance distribution SenseFlow discriminator models