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Regularized Distribution Matching Distillation for One-step Unpaired Image-to-Image Translation

arXiv 2406.14762 ↗First posted 20 Jun 2024cs.CV cs.LGcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Diffusion distillation methods aim to compress the diffusion models into efficient one-step generators while trying to preserve quality. Among them, Distribution Matching Distillation (DMD) offers a suitable framework for training general-form one-step generators, applicable beyond unconditional generation. In this work, we introduce its modification, called Regularized Distribution Matching Distillation, applicable to unpaired image-to-image (I2I) problems. We demonstrate its empirical performance in application to several translation tasks, including 2D examples and I2I between different image datasets, where it performs on par or better than multi-step diffusion baselines.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 41 of its 69 references that are in the map. First posted on arXiv 20 Jun 2024.

Builds on

41 of 69 references.

Closest ancestors · 13

Same direction, newest first

  1. 2023One-step Diffusion with Distribution Matching Distillation30 Nov
  2. 2023Adversarial Diffusion Distillation28 Nov
  3. 2023Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion1 Oct
  4. 2023Unpaired Image-to-Image Translation via Neural Schr"odinger Bridge24 May
  5. 2023Consistency Models2 Mar
  6. 2022Classifier-Free Diffusion Guidance26 Jul
  7. 2022EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations14 Jul
  8. 2022Elucidating the Design Space of Diffusion-Based Generative Models1 Jun
  9. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANs15 Dec
  10. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models6 Aug
  11. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  12. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
1 more
  1. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 28

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  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2022Flow Matching for Generative Modelingcited by 5,426
  4. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  5. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  6. 2013Auto-Encoding Variational Bayescited by 3,980
  7. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,550
  8. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,006
  9. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,277
  10. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,903
  11. 2023ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillationcited by 1,207
  12. 2019StarGAN v2: Diverse Image Synthesis for Multiple Domainscited by 1,095
16 more
  1. 2022Building Normalizing Flows with Stochastic Interpolantscited by 964
  2. 2023Improving and generalizing flow-based generative models with minibatch optimal transportcited by 835
  3. 2017Unsupervised Image-to-Image Translation Networkscited by 602
  4. 2023InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generationcited by 387
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  16. 2018Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networkscited by 10

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  4. 2025Adversarial Distribution Matching for Diffusion Distillation Towards Efficient Image and Video Synthesis
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  6. 2025Decoupled DMD: CFG Augmentation as the Spear, Distribution Matching as the Shield
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