Boosting Diffusion Models with an Adaptive Momentum Sampler

• URL: http://arxiv.org/abs/2308.11941v1
• Date: Wed, 23 Aug 2023 06:22:02 GMT
• Title: Boosting Diffusion Models with an Adaptive Momentum Sampler
• Authors: Xiyu Wang, Anh-Dung Dinh, Daochang Liu, Chang Xu
• Abstract summary: We present a novel reverse sampler for DPMs inspired by the widely-used Adam sampler. Our proposed sampler can be readily applied to a pre-trained diffusion model. By implicitly reusing update directions from early steps, our proposed sampler achieves a better balance between high-level semantics and low-level details.
• Score: 21.88226514633627
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion probabilistic models (DPMs) have been shown to generate high-quality images without the need for delicate adversarial training. However, the current sampling process in DPMs is prone to violent shaking. In this paper, we present a novel reverse sampler for DPMs inspired by the widely-used Adam optimizer. Our proposed sampler can be readily applied to a pre-trained diffusion model, utilizing momentum mechanisms and adaptive updating to smooth the reverse sampling process and ensure stable generation, resulting in outputs of enhanced quality. By implicitly reusing update directions from early steps, our proposed sampler achieves a better balance between high-level semantics and low-level details. Additionally, this sampler is flexible and can be easily integrated into pre-trained DPMs regardless of the sampler used during training. Our experimental results on multiple benchmarks demonstrate that our proposed reverse sampler yields remarkable improvements over different baselines. We will make the source code available.

Related papers

• DC-Solver: Improving Predictor-Corrector Diffusion Sampler via Dynamic Compensation [68.55191764622525]
  Diffusion models (DPMs) have shown remarkable performance in visual synthesis but are computationally expensive due to the need for multiple evaluations during the sampling. Recent predictor synthesis-or diffusion samplers have significantly reduced the required number of evaluations, but inherently suffer from a misalignment issue. We introduce a new fast DPM sampler called DC-CPRr, which leverages dynamic compensation to mitigate the misalignment.
  arXiv  Detail & Related papers  (2024-09-05T17:59:46Z)
• Self-Guided Generation of Minority Samples Using Diffusion Models [57.319845580050924]
  We present a novel approach for generating minority samples that live on low-density regions of a data manifold. Our framework is built upon diffusion models, leveraging the principle of guided sampling. Experiments on benchmark real datasets demonstrate that our approach can greatly improve the capability of creating realistic low-likelihood minority instances.
  arXiv  Detail & Related papers  (2024-07-16T10:03:29Z)
• Boosting Diffusion Models with Moving Average Sampling in Frequency Domain [101.43824674873508]
  Diffusion models rely on the current sample to denoise the next one, possibly resulting in denoising instability. In this paper, we reinterpret the iterative denoising process as model optimization and leverage a moving average mechanism to ensemble all the prior samples. We name the complete approach "Moving Average Sampling in Frequency domain (MASF)"
  arXiv  Detail & Related papers  (2024-03-26T16:57:55Z)
• 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)
• On Calibrating Diffusion Probabilistic Models [78.75538484265292]
  diffusion probabilistic models (DPMs) have achieved promising results in diverse generative tasks. We propose a simple way for calibrating an arbitrary pretrained DPM, with which the score matching loss can be reduced and the lower bounds of model likelihood can be increased. Our calibration method is performed only once and the resulting models can be used repeatedly for sampling.
  arXiv  Detail & Related papers  (2023-02-21T14:14:40Z)
• Optimizing DDPM Sampling with Shortcut Fine-Tuning [16.137936204766692]
  Shortcut Fine-Tuning (SFT) is a new approach for addressing the challenge of fast sampling of pretrained Denoising Diffusion Probabilistic Models (DDPMs) SFT advocates for the fine-tuning of DDPM samplers through the direct minimization of Integral Probability Metrics (IPM) Inspired by a control perspective, we propose a new algorithm SFT-PG: Shortcut Fine-Tuning with Policy Gradient.
  arXiv  Detail & Related papers  (2023-01-31T01:37:48Z)
• DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models [45.612477740555406]
  We propose DPM-r++, a high-order solver for guided sampling of DPMs. We show that DPM-r++ can generate high-quality samples within only 15 to 20 steps for guided sampling by pixel-space and latent-space DPMs.
  arXiv  Detail & Related papers  (2022-11-02T13:14:30Z)
• Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality [44.37533757879762]
  We introduce Differentiable Diffusion Sampler Search (DDSS), a method that optimize fast samplers for any pre-trained diffusion model. We also present Generalized Gaussian Diffusion Models (GGDM), a family of flexible non-Markovian samplers for diffusion models. Our method is compatible with any pre-trained diffusion model without fine-tuning or re-training required.
  arXiv  Detail & Related papers  (2022-02-11T18:53:18Z)
• Improved Denoising Diffusion Probabilistic Models [4.919647298882951]
  We show that DDPMs can achieve competitive log-likelihoods while maintaining high sample quality. We also find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes. We show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable.
  arXiv  Detail & Related papers  (2021-02-18T23:44:17Z)
• Denoising Diffusion Implicit Models [117.03720513930335]
  We present denoising diffusion implicit models (DDIMs) for iterative implicit probabilistic models with the same training procedure as DDPMs. DDIMs can produce high quality samples $10 times$ to $50 times$ faster in terms of wall-clock time compared to DDPMs.
  arXiv  Detail & Related papers  (2020-10-06T06:15:51Z)

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.