mlsanityData to 7 Oct 2026

Bezier Distillation

arXiv 2503.16562 ↗First posted 20 Mar 2025cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

In Rectified Flow, by obtaining the rectified flow several times, the mapping relationship between distributions can be distilled into a neural network, and the target distribution can be directly predicted by the straight lines of the flow. However, during the pairing process of the mapping relationship, a large amount of error accumulation will occur, resulting in a decrease in performance after multiple rectifications. In the field of flow models, knowledge distillation of multi - teacher diffusion models is also a problem worthy of discussion in accelerating sampling. I intend to combine multi - teacher knowledge distillation with Bezier curves to solve the problem of error accumulation. Currently, the related paper is being written by myself.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 27 of its 38 references that are in the map. First posted on arXiv 20 Mar 2025.

Builds on

27 of 38 references.

References · 27

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  6. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  7. 2022Flow Matching for Generative Modelingcited by 5,426
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  9. 2018Neural Ordinary Differential Equationscited by 3,733
  10. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  11. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,550
  12. 2022Video Diffusion Modelscited by 2,280
15 more
  1. 2023Consistency Modelscited by 1,778
  2. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,339
  3. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  4. 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,036
  5. 2022Building Normalizing Flows with Stochastic Interpolantscited by 964
  6. 2017Deep Mutual Learningcited by 621
  7. 2021Diffusion Schr"odinger Bridge with Applications to Score-Based Generative Modelingcited by 594
  8. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 511
  9. 2015Optimal Transport for Domain Adaptationcited by 457
  10. 2017Snapshot Ensembles: Train 1, get M for freecited by 349
  11. 2021Solving Schr"odinger Bridges via Maximum Likelihoodcited by 140
  12. 2023TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillationcited by 115
  13. 2019Theoretical guarantees for sampling and inference in generative models with latent diffusionscited by 97
  14. 2020MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Trickscited by 39
  15. 2023Accelerating Diffusion-based Combinatorial Optimization Solvers by Progressive Distillationcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Parametric Trajectory Distillation for Few-Step Video Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Optimal Self-Distillation for Rectified Flow via Linear Probing
  2. 2024Relational Diffusion Distillation for Efficient Image Generation
  3. 2024Accelerating Diffusion Models with One-to-Many Knowledge Distillation
  4. 2024Improving the Training of Rectified Flows
  5. 2025Balanced conic rectified flow
  6. 2024Distribution Backtracking Builds A Faster Convergence Trajectory for Diffusion Distillation
  7. 2025How to build a consistency model: Learning flow maps via self-distillation
  8. 2026A Quantitative Approximation Framework for Flow Distillation in Diffusion Models