title: Learning Few-Step Diffusion Models by Trajectory Distribution Matching
summary: Accelerating diffusion model sampling is crucial for efficient AIGC
  deployment. While diffusion distillation methods -- based on distribution
  matching and trajectory matching -- reduce sampling to as few as one step, they
  fall short on complex tasks like text-to-image generation. Few-step generation
  offers a better balance between speed and quality, but existing approaches face
  a persistent trade-off: distribution matching lacks flexibility for multi-step
  sampling, while trajectory matching often yields suboptimal image quality. To
  bridge this gap, we propose learning few-step diffusion models by Trajectory
  Distribution Matching (TDM), a unified distillation paradigm that combines the
  strengths of distribution and trajectory matching. Our method introduces a
  data-free score distillation objective, aligning the student's trajectory with
  the teacher's at the distribution level. Further, we develop a
  sampling-steps-aware objective that decouples learning targets across different
  steps, enabling more adjustable sampling. This approach supports both
  deterministic sampling for superior image quality and flexible multi-step
  adaptation, achieving state-of-the-art performance with remarkable efficiency.
  Our model, TDM, outperforms existing methods on various backbones, such as SDXL
  and PixArt-alpha, delivering superior quality and significantly reduced
  training costs. In particular, our method distills PixArt-alpha into a
  4-step generator that outperforms its teacher on real user preference at 1024
  resolution. This is accomplished with 500 iterations and 2 A800 hours -- a mere
  0.01% of the teacher's training cost. In addition, our proposed TDM can be
  extended to accelerate text-to-video diffusion. Notably, TDM can outperform its
  teacher model (CogVideoX-2B) by using only 4 NFE on VBench, improving the total
  score from 80.91 to 81.65. Project page: https://tdm-t2x.github.io/
paper_authors._id: 67d6881cf997964e21f90598
paper_authors.name: Yihong Luo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-17T08:44:52.452Z
paper_authors._id: 65f7e6856bd4bac5b6a4ecc3
paper_authors.avatarUrl: /avatars/9c0c48310fb5e99c19700316a0ab531e.svg
paper_authors.fullname: Yihong Luo
paper_authors.type: user
paper_authors.user: Luo-Yihong
paper_authors._id: 67d6881cf997964e21f90599
paper_authors.name: Tianyang Hu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T08:56:48.283Z
paper_authors._id: 636a40faa6f948c4f0c62ae5
paper_authors.avatarUrl: /avatars/30c35b194ba84d6e274df30e91a8cc45.svg
paper_authors.fullname: Tianyang Hu
paper_authors.type: user
paper_authors.user: whatlegequ
paper_authors._id: 67d6881cf997964e21f9059a
paper_authors.name: Jiacheng Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T08:56:55.114Z
paper_authors._id: 67b91a3c186bc4f8d83c94cf
paper_authors.avatarUrl: /avatars/a79538be4b5ed02cd54556458375e4af.svg
paper_authors.fullname: Jiacheng Sun
paper_authors.type: user
paper_authors.user: JIACSUN96
paper_authors._id: 67d6881cf997964e21f9059b
paper_authors.name: Yujun Cai
paper_authors._id: 67d6881cf997964e21f9059c
paper_authors.name: Jing Tang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T08:57:09.362Z
paper_authors._id: 636d660056c0762cfd9dc8d5
paper_authors.avatarUrl: /avatars/50ea2100e00b67ef10adc57556477184.svg
paper_authors.fullname: jing tang
paper_authors.type: user
paper_authors.user: jingtang
paper_title: Learning Few-Step Diffusion Models by Trajectory Distribution Matching
paper_summary: Accelerating diffusion model sampling is crucial for efficient AIGC
  deployment. While diffusion distillation methods -- based on distribution
  matching and trajectory matching -- reduce sampling to as few as one step, they
  fall short on complex tasks like text-to-image generation. Few-step generation
  offers a better balance between speed and quality, but existing approaches face
  a persistent trade-off: distribution matching lacks flexibility for multi-step
  sampling, while trajectory matching often yields suboptimal image quality. To
  bridge this gap, we propose learning few-step diffusion models by Trajectory
  Distribution Matching (TDM), a unified distillation paradigm that combines the
  strengths of distribution and trajectory matching. Our method introduces a
  data-free score distillation objective, aligning the student's trajectory with
  the teacher's at the distribution level. Further, we develop a
  sampling-steps-aware objective that decouples learning targets across different
  steps, enabling more adjustable sampling. This approach supports both
  deterministic sampling for superior image quality and flexible multi-step
  adaptation, achieving state-of-the-art performance with remarkable efficiency.
  Our model, TDM, outperforms existing methods on various backbones, such as SDXL
  and PixArt-alpha, delivering superior quality and significantly reduced
  training costs. In particular, our method distills PixArt-alpha into a
  4-step generator that outperforms its teacher on real user preference at 1024
  resolution. This is accomplished with 500 iterations and 2 A800 hours -- a mere
  0.01% of the teacher's training cost. In addition, our proposed TDM can be
  extended to accelerate text-to-video diffusion. Notably, TDM can outperform its
  teacher model (CogVideoX-2B) by using only 4 NFE on VBench, improving the total
  score from 80.91 to 81.65. Project page: https://tdm-t2x.github.io/
paper_ai_summary: TDM, a Trajectory Distribution Matching method, accelerates diffusion model sampling by combining distribution and trajectory matching, achieving superior quality and efficiency in text-to-image and text-to-video generation.
paper_ai_keywords: diffusion model sampling diffusion distillation distribution matching trajectory matching few-step generation data-free score distillation sampling-steps-aware objective deterministic sampling SDXL PixArt-$\alpha$ text-to-video diffusion CogVideoX-2B VBench TDM