CCDM: Continuous Conditional Diffusion Models for Image Generation

• URL: http://arxiv.org/abs/2405.03546v1
• Date: Mon, 6 May 2024 15:10:19 GMT
• Title: CCDM: Continuous Conditional Diffusion Models for Image Generation
• Authors: Xin Ding, Yongwei Wang, Kao Zhang, Z. Jane Wang,
• Abstract summary: Continuous Conditional Generative Modeling (CCGM) aims to estimate the distribution of high-dimensional data, typically images, conditioned on scalar continuous variables. Existing Conditional Adversarial Networks (CcGANs) were initially designed for this task, their adversarial training mechanism remains vulnerable to extremely sparse or imbalanced data. To enhance the quality of generated images, a promising alternative is to replace CcGANs with Conditional Diffusion Models (CDMs)
• Score: 22.70942688582302
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Continuous Conditional Generative Modeling (CCGM) aims to estimate the distribution of high-dimensional data, typically images, conditioned on scalar continuous variables known as regression labels. While Continuous conditional Generative Adversarial Networks (CcGANs) were initially designed for this task, their adversarial training mechanism remains vulnerable to extremely sparse or imbalanced data, resulting in suboptimal outcomes. To enhance the quality of generated images, a promising alternative is to replace CcGANs with Conditional Diffusion Models (CDMs), renowned for their stable training process and ability to produce more realistic images. However, existing CDMs encounter challenges when applied to CCGM tasks due to several limitations such as inadequate U-Net architectures and deficient model fitting mechanisms for handling regression labels. In this paper, we introduce Continuous Conditional Diffusion Models (CCDMs), the first CDM designed specifically for the CCGM task. CCDMs address the limitations of existing CDMs by introducing specially designed conditional diffusion processes, a modified denoising U-Net with a custom-made conditioning mechanism, a novel hard vicinal loss for model fitting, and an efficient conditional sampling procedure. With comprehensive experiments on four datasets with varying resolutions ranging from 64x64 to 192x192, we demonstrate the superiority of the proposed CCDM over state-of-the-art CCGM models, establishing new benchmarks in CCGM. Extensive ablation studies validate the model design and implementation configuration of the proposed CCDM. Our code is publicly available at https://github.com/UBCDingXin/CCDM.

Related papers

• Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction [88.65168366064061]
  We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference. Our framework leads to a family of three novel objectives that are all simulation-free, and thus scalable. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.
  arXiv  Detail & Related papers  (2024-10-10T17:18:30Z)
• Towards Model-Agnostic Dataset Condensation by Heterogeneous Models [13.170099297210372]
  We develop a novel method to produce universally applicable condensed images through cross-model interactions. By balancing the contribution of each model and maintaining their semantic meaning closely, our approach overcomes the limitations associated with model-specific condensed images.
  arXiv  Detail & Related papers  (2024-09-22T17:13:07Z)
• Inverse design with conditional cascaded diffusion models [0.0]
  Adjoint-based design optimizations are usually computationally expensive and those costs scale with resolution. We extend the use of diffusion models over traditional generative models by proposing the conditional cascaded diffusion model (cCDM) Our study compares cCDM against a cGAN model with transfer learning. While both models show decreased performance with reduced high-resolution training data, the cCDM loses its superiority to the cGAN model with transfer learning when training data is limited.
  arXiv  Detail & Related papers  (2024-08-16T04:54:09Z)
• Model Inversion Attacks Through Target-Specific Conditional Diffusion Models [54.69008212790426]
  Model inversion attacks (MIAs) aim to reconstruct private images from a target classifier's training set, thereby raising privacy concerns in AI applications. Previous GAN-based MIAs tend to suffer from inferior generative fidelity due to GAN's inherent flaws and biased optimization within latent space. We propose Diffusion-based Model Inversion (Diff-MI) attacks to alleviate these issues.
  arXiv  Detail & Related papers  (2024-07-16T06:38:49Z)
• CriDiff: Criss-cross Injection Diffusion Framework via Generative Pre-train for Prostate Segmentation [60.61972883059688]
  CriDiff is a two-stage feature injecting framework with a Crisscross Injection Strategy (CIS) and a Generative Pre-train (GP) approach for prostate segmentation. To effectively learn multi-level of edge features and non-edge features, we proposed two parallel conditioners in the CIS. The GP approach eases the inconsistency between the images features and the diffusion model without adding additional parameters.
  arXiv  Detail & Related papers  (2024-06-20T10:46:50Z)
• Phased Consistency Models [80.3176677757006]
  We identify three key flaws in the current design of Latent Consistency Models (LCMs) We propose Phased Consistency Models (PCMs) which generalize the design space and address the identified limitations. Our evaluations demonstrate that PCMs outperform LCMs across 1--16 step generation settings.
  arXiv  Detail & Related papers  (2024-05-28T17:47:19Z)
• Generalized Consistency Trajectory Models for Image Manipulation [59.576781858809355]
  Diffusion models (DMs) excel in unconditional generation, as well as on applications such as image editing and restoration. This work aims to unlock the full potential of consistency trajectory models (CTMs) by proposing generalized CTMs (GCTMs) We discuss the design space of GCTMs and demonstrate their efficacy in various image manipulation tasks such as image-to-image translation, restoration, and editing.
  arXiv  Detail & Related papers  (2024-03-19T07:24:54Z)
• TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet method [2.626378252978696]
  We propose a novel diffusion model-based MRI reconstruction method, named TC-DiffRecon, which does not rely on a specific acceleration factor for training. We also suggest the incorporation of the MF-UNet module, designed to enhance the quality of MRI images generated by the model.
  arXiv  Detail & Related papers  (2024-02-17T13:09:00Z)
• How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data [57.97035325253996]
  We show how Constrained Deep Generative Models (C-DGMs) can be transformed into realistic synthetic data models. C-DGMs are able to exploit the background knowledge expressed by the constraints to outperform their standard counterparts.
  arXiv  Detail & Related papers  (2024-02-07T13:22:05Z)
• Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning [20.175586324567025]
  Mitigating catastrophic forgetting is a key hurdle in continual learning. A major issue is the deterioration in the quality of generated data compared to the original. We propose a GR-based approach for continual learning that enhances image quality in generators.
  arXiv  Detail & Related papers  (2023-12-10T17:39:42Z)
• Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference [60.32804641276217]
  We propose Latent Consistency Models (LCMs), enabling swift inference with minimal steps on any pre-trained LDMs. A high-quality 768 x 768 24-step LCM takes only 32 A100 GPU hours for training. We also introduce Latent Consistency Fine-tuning (LCF), a novel method that is tailored for fine-tuning LCMs on customized image datasets.
  arXiv  Detail & Related papers  (2023-10-06T17:11:58Z)
• Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation [18.956306942099097]
  Conditional diffusion models (CDM) is capable of generating images subject to specific distributions. We utilize category-aware semantic information underlied in CDM to get the prediction mask of the target object. Our method outperforms state-of-the-art CAM and diffusion model methods on two public medical image segmentation datasets.
  arXiv  Detail & Related papers  (2023-06-06T17:29:26Z)
• 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)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)
• Autoregressive Score Matching [113.4502004812927]
  We propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of the derivatives of univariable log-conditionals (scores) For AR-CSM models, this divergence between data and model distributions can be computed and optimized efficiently, requiring no expensive sampling or adversarial training. We show with extensive experimental results that it can be applied to density estimation on synthetic data, image generation, image denoising, and training latent variable models with implicit encoders.
  arXiv  Detail & Related papers  (2020-10-24T07:01:24Z)

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.