SenseFlow: Scaling Distribution Matching for Flow-based Text-to-Image
  Distillation
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.
683efe2880e41655390ab9f5
Xingtong Ge
claimed_verified
2025-06-04T09:02:22.769Z
65a94d2bbe0b2002351b6a8e
/avatars/c2a7a4bff254c643c1158a20dd74dc0f.svg
Xingtong Ge
user
domiso
683efe2880e41655390ab9f6
Xin Zhang
683efe2880e41655390ab9f7
Tongda Xu
683efe2880e41655390ab9f8
Yi Zhang
683efe2880e41655390ab9f9
Xinjie Zhang
683efe2880e41655390ab9fa
Yan Wang
683efe2880e41655390ab9fb
Jun Zhang
SenseFlow: Scaling Distribution Matching for Flow-based Text-to-Image
  Distillation
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.
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.
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