Progressive Compression with Universally Quantized Diffusion Models

• URL: http://arxiv.org/abs/2412.10935v2
• Date: Thu, 26 Dec 2024 21:12:31 GMT
• Title: Progressive Compression with Universally Quantized Diffusion Models
• Authors: Yibo Yang, Justus C. Will, Stephan Mandt,
• Abstract summary: We explore the potential of diffusion models for progressive coding, resulting in a sequence of bits that can be incrementally transmitted and decoded. Unlike prior work based on Gaussian diffusion or conditional diffusion models, we propose a new form of diffusion model with uniform noise in the forward process. We obtain promising first results on image compression, achieving competitive rate-distortion and rate-realism results on a wide range of bit-rates with a single model.
• Score: 35.199627388957566
• License:
• Abstract: Diffusion probabilistic models have achieved mainstream success in many generative modeling tasks, from image generation to inverse problem solving. A distinct feature of these models is that they correspond to deep hierarchical latent variable models optimizing a variational evidence lower bound (ELBO) on the data likelihood. Drawing on a basic connection between likelihood modeling and compression, we explore the potential of diffusion models for progressive coding, resulting in a sequence of bits that can be incrementally transmitted and decoded with progressively improving reconstruction quality. Unlike prior work based on Gaussian diffusion or conditional diffusion models, we propose a new form of diffusion model with uniform noise in the forward process, whose negative ELBO corresponds to the end-to-end compression cost using universal quantization. We obtain promising first results on image compression, achieving competitive rate-distortion and rate-realism results on a wide range of bit-rates with a single model, bringing neural codecs a step closer to practical deployment.

Related papers

• An Ordinary Differential Equation Sampler with Stochastic Start for Diffusion Bridge Models [13.00429687431982]
  Diffusion bridge models initialize the generative process from corrupted images instead of pure Gaussian noise. Existing diffusion bridge models often rely on Differential Equation samplers, which result in slower inference speed. We propose a high-order ODE sampler with a start for diffusion bridge models. Our method is fully compatible with pretrained diffusion bridge models and requires no additional training.
  arXiv  Detail & Related papers  (2024-12-28T03:32:26Z)
• Sequential Posterior Sampling with Diffusion Models [15.028061496012924]
  We propose a novel approach that models the transition dynamics to improve the efficiency of sequential diffusion posterior sampling in conditional image synthesis. We demonstrate the effectiveness of our approach on a real-world dataset of high frame rate cardiac ultrasound images. Our method opens up new possibilities for real-time applications of diffusion models in imaging and other domains requiring real-time inference.
  arXiv  Detail & Related papers  (2024-09-09T07:55:59Z)
• Lossy Image Compression with Foundation Diffusion Models [10.407650300093923]
  In this work we formulate the removal of quantization error as a denoising task, using diffusion to recover lost information in the transmitted image latent. Our approach allows us to perform less than 10% of the full diffusion generative process and requires no architectural changes to the diffusion model.
  arXiv  Detail & Related papers  (2024-04-12T16:23:42Z)
• Neural Diffusion Models [2.1779479916071067]
  We present a generalization of conventional diffusion models that enables defining and learning time-dependent non-linear transformations of data. NDMs outperform conventional diffusion models in terms of likelihood and produce high-quality samples.
  arXiv  Detail & Related papers  (2023-10-12T13:54:55Z)
• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• Reflected Diffusion Models [93.26107023470979]
  We present Reflected Diffusion Models, which reverse a reflected differential equation evolving on the support of the data. Our approach learns the score function through a generalized score matching loss and extends key components of standard diffusion models.
  arXiv  Detail & Related papers  (2023-04-10T17:54:38Z)
• Q-Diffusion: Quantizing Diffusion Models [52.978047249670276]
  Post-training quantization (PTQ) is considered a go-to compression method for other tasks. We propose a novel PTQ method specifically tailored towards the unique multi-timestep pipeline and model architecture. We show that our proposed method is able to quantize full-precision unconditional diffusion models into 4-bit while maintaining comparable performance.
  arXiv  Detail & Related papers  (2023-02-08T19:38:59Z)
• SinDiffusion: Learning a Diffusion Model from a Single Natural Image [159.4285444680301]
  We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image. It is based on two core designs. First, SinDiffusion is trained with a single model at a single scale instead of multiple models with progressive growing of scales. Second, we identify that a patch-level receptive field of the diffusion network is crucial and effective for capturing the image's patch statistics.
  arXiv  Detail & Related papers  (2022-11-22T18:00:03Z)
• Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models [54.1843419649895]
  A major challenge in using generative models to accomplish this task is the lack of paired data containing all modalities and corresponding outputs. We propose a solution based on a denoising diffusion probabilistic synthesis models to generate images under multi-model priors.
  arXiv  Detail & Related papers  (2022-06-10T12:23:05Z)
• Denoising Diffusion Probabilistic Models [91.94962645056896]
  We present high quality image synthesis results using diffusion probabilistic models. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics.
  arXiv  Detail & Related papers  (2020-06-19T17:24:44Z)

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.