Abstract
Diffusion models are highly regarded for their controllability and the diversity of images they generate. However, class-conditional generation methods based on diffusion models often focus on more common categories. In large-scale fine-grained image generation, issues of semantic information entanglement and insufficient detail in the generated images still persist. This paper attempts to introduce a concept of a tiered embedder in fine-grained image generation, which integrates semantic information from both super and child classes, allowing the diffusion model to better incorporate semantic information and address the issue of semantic entanglement. To address the issue of insufficient detail in fine-grained images, we introduce the concept of super-resolution during the perceptual information generation stage, enhancing the detailed features of fine-grained images through enhancement and degradation models. Furthermore, we propose an efficient ProAttention mechanism that can be effectively implemented in the diffusion model. We evaluate our method through extensive experiments on public benchmarks, demonstrating that our approach outperforms other state-of-the-art fine-tuning methods in terms of performance.
Abstract (translated)
扩散模型因其可控性和生成图像的多样性而备受推崇。然而，基于扩散模型的条件类别生成方法通常更关注常见类别，而在大规模细粒度图像生成中，语义信息纠缠和生成图像细节不足的问题仍然存在。本文尝试在细粒度图像生成中引入分层嵌入器的概念，该嵌入器结合了超类和子类的语义信息，使扩散模型能够更好地融合语义信息并解决语义纠缠问题。为了解决细粒度图像中细节不足的问题，我们在感知信息生成阶段引入了超分辨率概念，通过增强和退化模型来提升细粒度图像中的详细特征。此外，我们还提出了一种高效的ProAttention机制，该机制可以在扩散模型中有效实现。我们通过对公共基准进行广泛的实验评估，证明我们的方法在性能上优于其他最先进的微调方法。
URL
https://arxiv.org/abs/2512.05152
