DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models

• URL: http://arxiv.org/abs/2211.01095v2
• Date: Sat, 6 May 2023 17:15:37 GMT
• Title: DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models
• Authors: Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu
• Abstract summary: 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.
• Score: 45.612477740555406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion probabilistic models (DPMs) have achieved impressive success in high-resolution image synthesis, especially in recent large-scale text-to-image generation applications. An essential technique for improving the sample quality of DPMs is guided sampling, which usually needs a large guidance scale to obtain the best sample quality. The commonly-used fast sampler for guided sampling is DDIM, a first-order diffusion ODE solver that generally needs 100 to 250 steps for high-quality samples. Although recent works propose dedicated high-order solvers and achieve a further speedup for sampling without guidance, their effectiveness for guided sampling has not been well-tested before. In this work, we demonstrate that previous high-order fast samplers suffer from instability issues, and they even become slower than DDIM when the guidance scale grows large. To further speed up guided sampling, we propose DPM-Solver++, a high-order solver for the guided sampling of DPMs. DPM-Solver++ solves the diffusion ODE with the data prediction model and adopts thresholding methods to keep the solution matches training data distribution. We further propose a multistep variant of DPM-Solver++ to address the instability issue by reducing the effective step size. Experiments show that DPM-Solver++ can generate high-quality samples within only 15 to 20 steps for guided sampling by pixel-space and latent-space DPMs.

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)
• 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)
• Sampler Scheduler for Diffusion Models [0.0]
  Diffusion modeling (DM) has high-quality generative performance. Currently, there is a contradiction in samplers for diffusion-based generative models. We propose the feasibility of using different samplers (ODE/SDE) on different sampling steps of the same sampling process.
  arXiv  Detail & Related papers  (2023-11-12T13:35:25Z)
• Boosting Diffusion Models with an Adaptive Momentum Sampler [21.88226514633627]
  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.
  arXiv  Detail & Related papers  (2023-08-23T06:22:02Z)
• Alleviating Exposure Bias in Diffusion Models through Sampling with Shifted Time Steps [23.144083737873263]
  Diffusion Probabilistic Models (DPM) have shown remarkable efficacy in the synthesis of high-quality images. Previous work has attempted to mitigate this issue by perturbing inputs during training. We propose a novel sampling method that we propose, without retraining the model.
  arXiv  Detail & Related papers  (2023-05-24T21:39:27Z)
• Fast Diffusion Probabilistic Model Sampling through the lens of Backward Error Analysis [26.907301901503835]
  Denoising diffusion probabilistic models (DDPMs) are a class of powerful generative models. DDPMs generally need hundreds or thousands of sequential function evaluations (steps) of neural networks to generate a sample. This paper aims to develop a fast sampling method for DDPMs requiring much fewer steps while retaining high sample quality.
  arXiv  Detail & Related papers  (2023-04-22T16:58:47Z)
• UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models [92.43617471204963]
  Diffusion probabilistic models (DPMs) have demonstrated a very promising ability in high-resolution image synthesis. We develop a unified corrector (UniC) that can be applied after any existing DPM sampler to increase the order of accuracy. We propose a unified predictor-corrector framework called UniPC for the fast sampling of DPMs.
  arXiv  Detail & Related papers  (2023-02-09T18:59:48Z)
• 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)
• Pseudo Numerical Methods for Diffusion Models on Manifolds [77.40343577960712]
  Denoising Diffusion Probabilistic Models (DDPMs) can generate high-quality samples such as image and audio samples. DDPMs require hundreds to thousands of iterations to produce final samples. We propose pseudo numerical methods for diffusion models (PNDMs) PNDMs can generate higher quality synthetic images with only 50 steps compared with 1000-step DDIMs (20x speedup)
  arXiv  Detail & Related papers  (2022-02-20T10:37:52Z)
• 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.