Abstract
Diffusion models have emerged as a leading technique for generating images due to their ability to create high-resolution and realistic images. Despite their strong performance, diffusion models still struggle in managing image collections with significant feature differences. They often fail to capture complex features and produce conflicting results. Research has attempted to address this issue by learning different regions of an image through multiple diffusion paths and then combining them. However, this approach leads to inefficient coordination among multiple paths and high computational costs. To tackle these issues, this paper presents a Diffusion Fuzzy System (DFS), a latent-space multi-path diffusion model guided by fuzzy rules. DFS offers several advantages. First, unlike traditional multi-path diffusion methods, DFS uses multiple diffusion paths, each dedicated to learning a specific class of image features. By assigning each path to a different feature type, DFS overcomes the limitations of multi-path models in capturing heterogeneous image features. Second, DFS employs rule-chain-based reasoning to dynamically steer the diffusion process and enable efficient coordination among multiple paths. Finally, DFS introduces a fuzzy membership-based latent-space compression mechanism to reduce the computational costs of multi-path diffusion effectively. We tested our method on three public datasets: LSUN Bedroom, LSUN Church, and MS COCO. The results show that DFS achieves more stable training and faster convergence than existing single-path and multi-path diffusion models. Additionally, DFS surpasses baseline models in both image quality and alignment between text and images, and also shows improved accuracy when comparing generated images to target references.
Abstract (translated)
扩散模型由于能够生成高分辨率和逼真的图像而成为一种领先的图像生成技术。尽管它们表现出色，但扩散模型在处理具有显著特征差异的图像集合时仍存在挑战，经常无法捕捉复杂特性并产生矛盾的结果。为了解决这个问题，研究尝试通过学习图像的不同区域并通过多个扩散路径来组合它们的方法。然而，这种方法导致了多条路径之间协调效率低下和计算成本高昂的问题。 为了应对这些挑战，本文提出了一种基于模糊规则的扩散模糊系统（DFS），这是一种在潜在空间中由模糊规则引导的多路径扩散模型。DFS具有几个优点：首先，与传统的多路径扩散方法不同，DFS使用多个独立的扩散路径来专门学习某一类图像特征。通过将每个路径分配给不同的特征类型，DFS克服了多路径模型在捕捉异质图像特征方面的局限性。 其次，DFS采用基于规则链的推理机制，以动态引导扩散过程，并使多条路径之间的协调更加高效。最后，DFS引入了一种基于模糊隶属度的潜在空间压缩机制，有效降低了多路径扩散的计算成本。我们在三个公共数据集（LSUN Bedroom、LSUN Church和MS COCO）上测试了我们的方法，结果显示DFS在训练稳定性以及收敛速度方面都优于现有的单路径和多路径扩散模型。 此外，DFS在图像质量和文本与图像之间的对齐度方面超越了基线模型，并且在将生成的图像与目标参考进行比较时也显示出更高的准确性。
URL
https://arxiv.org/abs/2512.01533
