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 this https URL

连续条件生成建模（CCGM）是一种基于标量连续变量（称为回归标签）的高维数据（通常为图像）的分布估计方法。现有的条件扩散模型（CDMs）在应用于 CCGM 任务时面临着多种挑战，本文介绍了第一种专为 CCGM 任务设计的连续条件扩散模型（CCDMs），通过引入特别设计的条件扩散过程、修改的去噪 U-Net 与自定义的条件机制、新型的硬邻近损失以及高效的条件采样过程，CCDMs 克服了现有 CDMs 的限制，基于四个不同分辨率（从 64x64 到 192x192）的数据集进行的全面实验证明了 CCDMs 相对于最先进的 CCGM 模型的优越性，建立了 CCGM 领域的新基准。广泛的消融研究验证了提出的 CCDM 的模型设计和实施配置。我们的代码公开在此 https URL。