Constant Rate Schedule: Constant-Rate Distributional Change for Efficient Training and Sampling in Diffusion Models

• URL: http://arxiv.org/abs/2411.12188v2
• Date: Tue, 04 Feb 2025 06:56:07 GMT
• Title: Constant Rate Schedule: Constant-Rate Distributional Change for Efficient Training and Sampling in Diffusion Models
• Authors: Shuntaro Okada, Kenji Doi, Ryota Yoshihashi, Hirokatsu Kataoka, Tomohiro Tanaka,
• Abstract summary: Noise schedule ensures constant rate of change in probability distribution of diffused data.<n>Noise schedule is automatically determined and tailored to each dataset and type of diffusion model.
• Score: 16.863038973001483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a noise schedule that ensures a constant rate of change in the probability distribution of diffused data throughout the diffusion process. To obtain this schedule, we measure the probability-distributional change of diffused data by simulating the forward process and use it to determine the noise schedule before training diffusion models. The functional form of the noise schedule is automatically determined and tailored to each dataset and type of diffusion model, such as pixel space or latent space. We evaluate the effectiveness of our noise schedule on unconditional and class-conditional image generation tasks using the LSUN (Bedroom, Church, Cat, Horse), ImageNet, and FFHQ datasets. Through extensive experiments, we confirmed that our noise schedule broadly improves the performance of the pixel-space and latent-space diffusion models regardless of the dataset, sampler, and number of function evaluations.

Related papers

• ADiff4TPP: Asynchronous Diffusion Models for Temporal Point Processes [30.928368603673285]
  This work introduces a novel approach to modeling temporal point processes using diffusion models with an asynchronous noise schedule. We derive an objective to effectively train these models for a general family of noise schedules based on conditional flow matching. Our method achieves the joint distribution of the latent representations of events in a sequence and state-of-the-art results in predicting both the next inter-event time and event type on benchmark datasets.
  arXiv  Detail & Related papers  (2025-04-29T04:17:39Z)
• Score-Optimal Diffusion Schedules [29.062842062257918]
  An appropriate discretisation schedule is crucial to obtain high quality samples. This paper presents a novel algorithm for adaptively selecting an optimal discretisation schedule. We find that our learned schedule recovers performant schedules previously only discovered through manual search.
  arXiv  Detail & Related papers  (2024-12-10T19:26:51Z)
• Diffusion Priors for Variational Likelihood Estimation and Image Denoising [10.548018200066858]
  We propose adaptive likelihood estimation and MAP inference during the reverse diffusion process to tackle real-world noise. Experiments and analyses on diverse real-world datasets demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-10-23T02:52:53Z)
• Constrained Diffusion Models via Dual Training [80.03953599062365]
  Diffusion processes are prone to generating samples that reflect biases in a training dataset. We develop constrained diffusion models by imposing diffusion constraints based on desired distributions. We show that our constrained diffusion models generate new data from a mixture data distribution that achieves the optimal trade-off among objective and constraints.
  arXiv  Detail & Related papers  (2024-08-27T14:25:42Z)
• A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models [14.859580045688487]
  A practical bottleneck of diffusion models is their sampling speed. We propose a novel framework capable of adaptively allocating compute required for the score estimation. We show that our method could significantly improve the sampling throughput of the diffusion models without compromising image quality.
  arXiv  Detail & Related papers  (2024-08-12T05:33:45Z)
• Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment [56.609042046176555]
  suboptimal noise-data mapping leads to slow training of diffusion models. Drawing inspiration from the immiscibility phenomenon in physics, we propose Immiscible Diffusion. Our approach is remarkably simple, requiring only one line of code to restrict the diffuse-able area for each image.
  arXiv  Detail & Related papers  (2024-06-18T06:20:42Z)
• Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data [74.2507346810066]
  Ambient diffusion is a recently proposed framework for training diffusion models using corrupted data. We present the first framework for training diffusion models that provably sample from the uncorrupted distribution given only noisy training data.
  arXiv  Detail & Related papers  (2024-03-20T14:22:12Z)
• Blue noise for diffusion models [50.99852321110366]
  We introduce a novel and general class of diffusion models taking correlated noise within and across images into account. Our framework allows introducing correlation across images within a single mini-batch to improve gradient flow. We perform both qualitative and quantitative evaluations on a variety of datasets using our method.
  arXiv  Detail & Related papers  (2024-02-07T14:59:25Z)
• Diffusion Models With Learned Adaptive Noise [12.530583016267768]
  In this paper, we explore whether the diffusion process can be learned from data. A widely held assumption is that the ELBO is invariant to the noise process. We propose MULAN, a learned diffusion process that applies noise at different rates across an image.
  arXiv  Detail & Related papers  (2023-12-20T18:00:16Z)
• ShiftDDPMs: Exploring Conditional Diffusion Models by Shifting Diffusion Trajectories [144.03939123870416]
  We propose a novel conditional diffusion model by introducing conditions into the forward process. We use extra latent space to allocate an exclusive diffusion trajectory for each condition based on some shifting rules. We formulate our method, which we call textbfShiftDDPMs, and provide a unified point of view on existing related methods.
  arXiv  Detail & Related papers  (2023-02-05T12:48:21Z)
• Score-based Continuous-time Discrete Diffusion Models [102.65769839899315]
  We extend diffusion models to discrete variables by introducing a Markov jump process where the reverse process denoises via a continuous-time Markov chain. We show that an unbiased estimator can be obtained via simple matching the conditional marginal distributions. We demonstrate the effectiveness of the proposed method on a set of synthetic and real-world music and image benchmarks.
  arXiv  Detail & Related papers  (2022-11-30T05:33:29Z)
• Markup-to-Image Diffusion Models with Scheduled Sampling [111.30188533324954]
  Building on recent advances in image generation, we present a data-driven approach to rendering markup into images. The approach is based on diffusion models, which parameterize the distribution of data using a sequence of denoising operations. We conduct experiments on four markup datasets: mathematical formulas (La), table layouts (HTML), sheet music (LilyPond), and molecular images (SMILES)
  arXiv  Detail & Related papers  (2022-10-11T04:56:12Z)
• Diffusion-GAN: Training GANs with Diffusion [135.24433011977874]
  Generative adversarial networks (GANs) are challenging to train stably. We propose Diffusion-GAN, a novel GAN framework that leverages a forward diffusion chain to generate instance noise. We show that Diffusion-GAN can produce more realistic images with higher stability and data efficiency than state-of-the-art GANs.
  arXiv  Detail & Related papers  (2022-06-05T20:45:01Z)
• Non Gaussian Denoising Diffusion Models [91.22679787578438]
  We show that noise from Gamma distribution provides improved results for image and speech generation. We also show that using a mixture of Gaussian noise variables in the diffusion process improves the performance over a diffusion process that is based on a single distribution.
  arXiv  Detail & Related papers  (2021-06-14T16:42:43Z)

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.