arXiv Computer Vision By Suhyeon Lee, Kwanyoung Kim, Jong Chul Ye

Single-Step Bidirectional Unpaired Image Translation Using Implicit Bridge Consistency Distillation

Read the original on arXiv Computer Vision →

The paper introduces Implicit Bridge Consistency Distillation (IBCD), a method that enables single-step, bidirectional unpaired image translation without adversarial loss. IBCD uses a diffusion implicit bridge model that connects PF-ODE trajectories between distributions, parametrized on a signed time axis to select the target domain, allowing one network to translate in both directions in a single step. The authors also propose distribution matching for consistency distillation and an adaptive weighting scheme based on distillation difficulty, achieving state‑of‑the‑art performance on benchmark datasets and extending to brain MRI contrast translation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv AI
Aug 25

GAN-Diff : Coupling Pretrained WGAN-GP Features with Conditional Diffusion U-Nets

arXiv:2608.22272v1 Announce Type: cross Abstract: Generative adversarial networks (GANs) can provide efficient image generation, while diffusion models offer high-quality image restoration but requir...

By Saif Ahmed, Ashadulla Hil Galib, S. M. Riaz Rahman Antu, Ahmed Faizul Haque Dhrubo, Souvik Pramanik, Mohammad Abdul Qayum, Mohsin Sajjad, Mohammad Ashrafuzzaman Khan
arXiv AI
Jul 21

A${}^2$BM: Alignment-Aware Bridge Matching for Image-to-Image Translation

arXiv:2607. 16294v1 Announce Type: cross Abstract: Paired image-to-image translation underpins a wide range of computer vision tasks, including image editing, sensor translation, and domain adaptation.

By Aimi Okabayashi (UBS Vannes), Georges Le Bellier (LIP, CEDRIC - VERTIGO), Nicolas Audebert (LaSTIG, IGN, CEDRIC - VERTIGO), Charlotte Pelletier (OBELIX), Thomas Corpetti (LETG - Rennes), Nicolas Courty (OBELIX)
arXiv Computer Vision
Oct 23

Noise-Started One-Step Real-World Super-Resolution via LR-Conditioned SplitMeanFlow and GAN Refinement

The paper introduces SMFSR, a one‑step real‑world super‑resolution framework that retains the noise‑started generation of diffusion models while conditioning on low‑resolution images. It uses LR‑conditioned SplitMeanFlow with Interval Splitting Consistency to collapse a multi‑step trajectory into a single average‑velocity prediction, and adds a GAN refinement stage with a DINOv3 discriminator and variational score distillation to enhance texture realism. Experiments show SMFSR achieves state‑of‑the‑art perceptual quality among one‑step diffusion‑based methods while maintaining fast inference.

By Wei Zhu, Kai Zhang, Yu Zheng, Lei Luo, Yong Guo, Jian Yang
Hugging Face Trending Papers
4d ago

Latent Diffusion-GAN: Adversarial Learning in the Autoencoded Latent Space

Latent Diffusion-GAN (LD‑GAN) integrates adversarial learning into diffusion models by using the pretrained VAE as an energy‑based discriminator, avoiding extra parameters and preserving the latent priors. It introduces a structural consistency energy to align encoder and decoder features, improving perceptual quality. Experiments show LD‑GAN enhances sample fidelity, sharpness, and diversity across generation tasks while maintaining efficient training dynamics.