3 citations · 11 across the 18 of their papers we have counts for
5 papers · 1 filter
Iterative Tilting for Diffusion Fine-Tuning
Jean Pachebat, Giovanni Conforti, Alain Durmus +1
We introduce iterative tilting, a gradient-free method for fine-tuning diffusion models toward reward-tilted distributions. The method decomposes a large reward tilt int…
Efficient Zero-Shot Inpainting with Decoupled Diffusion Guidance
Badr Moufad, Navid Bagheri Shouraki, Alain Oliviero Durmus +4
Diffusion models have emerged as powerful priors for image editing tasks such as inpainting and local modification, where the objective is to generate realistic content that remain…
Briding Diffusion Posterior Sampling and Monte Carlo methods: a survey
Yazid Janati, Alain Durmus, Jimmy Olsson +1
Diffusion models enable the synthesis of highly accurate samples from complex distributions and have become foundational in generative modeling. Recently, they have demonstrated si…
Conditional Diffusion Models with Classifier-Free Gibbs-like Guidance
Badr Moufad, Yazid Janati, Alain Durmus +3
Classifier-Free Guidance (CFG) is a widely used technique for improving conditional diffusion models by linearly combining the outputs of conditional and unconditional denoisers. W…
A Mixture-Based Framework for Guiding Diffusion Models
Yazid Janati, Badr Moufad, Mehdi Abou El Qassime +3
Denoising diffusion models have driven significant progress in the field of Bayesian inverse problems. Recent approaches use pre-trained diffusion models as priors to solve a wide…