Optimizing Few-Step Sampler for Diffusion Probabilistic Model

• URL: http://arxiv.org/abs/2412.10786v1
• Date: Sat, 14 Dec 2024 10:47:52 GMT
• Title: Optimizing Few-Step Sampler for Diffusion Probabilistic Model
• Authors: Jen-Yuan Huang,
• Abstract summary: Diffusion Probabilistic Models (DPMs) have demonstrated exceptional capability of generating high-quality and diverse images. But their practical application is hindered by the intensive computational cost during inference.
• Score: 0.0
• License:
• Abstract: Diffusion Probabilistic Models (DPMs) have demonstrated exceptional capability of generating high-quality and diverse images, but their practical application is hindered by the intensive computational cost during inference. The DPM generation process requires solving a Probability-Flow Ordinary Differential Equation (PF-ODE), which involves discretizing the integration domain into intervals for numerical approximation. This corresponds to the sampling schedule of a diffusion ODE solver, and we notice the solution from a first-order solver can be expressed as a convex combination of model outputs at all scheduled time-steps. We derive an upper bound for the discretization error of the sampling schedule, which can be efficiently optimized with Monte-Carlo estimation. Building on these theoretical results, we purpose a two-phase alternating optimization algorithm. In Phase-1, the sampling schedule is optimized for the pre-trained DPM; in Phase-2, the DPM further tuned on the selected time-steps. Experiments on a pre-trained DPM for ImageNet64 dataset demonstrate the purposed method consistently improves the baseline across various number of sampling steps.

Related papers

• Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling [3.866047645663101]
  In computer vision, for example, tasks such as inpainting, deblurring, and super resolution can be effectively modeled as inverse problems. DDPMs are shown to provide a promising solution to noisy linear inverse problems without the need for additional task specific training.
  arXiv  Detail & Related papers  (2024-12-28T06:17:44Z)
• On the Trajectory Regularity of ODE-based Diffusion Sampling [79.17334230868693]
  Diffusion-based generative models use differential equations to establish a smooth connection between a complex data distribution and a tractable prior distribution. In this paper, we identify several intriguing trajectory properties in the ODE-based sampling process of diffusion models.
  arXiv  Detail & Related papers  (2024-05-18T15:59:41Z)
• Align Your Steps: Optimizing Sampling Schedules in Diffusion Models [63.927438959502226]
  Diffusion models (DMs) have established themselves as the state-of-the-art generative modeling approach in the visual domain and beyond. A crucial drawback of DMs is their slow sampling speed, relying on many sequential function evaluations through large neural networks. We propose a general and principled approach to optimizing the sampling schedules of DMs for high-quality outputs.
  arXiv  Detail & Related papers  (2024-04-22T18:18:41Z)
• Accelerating Diffusion Sampling with Optimized Time Steps [69.21208434350567]
  Diffusion probabilistic models (DPMs) have shown remarkable performance in high-resolution image synthesis. Their sampling efficiency is still to be desired due to the typically large number of sampling steps. Recent advancements in high-order numerical ODE solvers for DPMs have enabled the generation of high-quality images with much fewer sampling steps.
  arXiv  Detail & Related papers  (2024-02-27T10:13:30Z)
• SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models [66.67616086310662]
  Diffusion Probabilistic Models (DPMs) have achieved considerable success in generation tasks. As sampling from DPMs is equivalent to solving diffusion SDE or ODE which is time-consuming, numerous fast sampling methods built upon improved differential equation solvers are proposed. We propose SA-of-r, which is an improved efficient Adams method for solving diffusion SDE to generate data with high quality.
  arXiv  Detail & Related papers  (2023-09-10T12:44:54Z)
• AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models [103.41269503488546]
  Existing customization methods require access to multiple reference examples to align pre-trained diffusion probabilistic models with user-provided concepts. This paper aims to address the challenge of DPM customization when the only available supervision is a differentiable metric defined on the generated contents. We propose a novel method AdjointDPM, which first generates new samples from diffusion models by solving the corresponding probability-flow ODEs. It then uses the adjoint sensitivity method to backpropagate the gradients of the loss to the models' parameters.
  arXiv  Detail & Related papers  (2023-07-20T09:06:21Z)
• Learning Unnormalized Statistical Models via Compositional Optimization [73.30514599338407]
  Noise-contrastive estimation(NCE) has been proposed by formulating the objective as the logistic loss of the real data and the artificial noise. In this paper, we study it a direct approach for optimizing the negative log-likelihood of unnormalized models.
  arXiv  Detail & Related papers  (2023-06-13T01:18:16Z)
• On Accelerating Diffusion-Based Sampling Process via Improved Integration Approximation [12.882586878998579]
  A popular approach to sample a diffusion-based generative model is to solve an ordinary differential equation (ODE) We consider accelerating several popular ODE-based sampling processes by optimizing certain coefficients via improved integration approximation (IIA) We show that considerably better FID scores can be achieved by using IIA-EDM, IIA-DDIM, and IIA-DPM-r than the original counterparts.
  arXiv  Detail & Related papers  (2023-04-22T06:06:28Z)
• DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps [45.612477740555406]
  Diffusion probabilistic models (DPMs) are emerging powerful generative models. DPM-r is suitable for both discrete-time and continuous-time DPMs without any further training. We achieve 4.70 FID in 10 function evaluations and 2.87 FID in 20 function evaluations on the CIFAR10 dataset.
  arXiv  Detail & Related papers  (2022-06-02T08:43:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.