Existing methods for restoring degraded human-centric images often struggle with insufficient fidelity, particularly in human body restoration (HBR). Recent diffusion-based restoration methods commonly adapt pre-trained text-to-image diffusion models, where the variational autoencoder (VAE) can significantly bottleneck restoration fidelity. We propose LCUDiff, a stable one-step framework that upgrades a pre-trained latent diffusion model from the 4-channel latent space to the 16-channel latent space. For VAE fine-tuning, channel splitting distillation (CSD) is used to keep the first four channels aligned with pre-trained priors while allocating the additional channels to effectively encode high-frequency details. We further design prior-preserving adaptation (PPA) to smoothly bridge the mismatch between 4-channel diffusion backbones and the higher-dimensional 16-channel latent. In addition, we propose a decoder router (DeR) for per-sample decoder routing using restoration-quality score annotations, which improves visual quality across diverse conditions. Experiments on synthetic and real-world datasets show competitive results with higher fidelity and fewer artifacts under mild degradations, while preserving one-step efficiency. The code and model will be at https://github.com/gobunu/LCUDiff.

翻译：现有针对以人为中心的退化图像修复方法往往面临保真度不足的问题，这在人体修复任务中尤为突出。近期基于扩散模型的修复方法通常适配预训练的文本到图像扩散模型，其中变分自编码器往往成为限制修复保真度的关键瓶颈。本文提出LCUDiff，一种稳定的单步框架，可将预训练的潜在扩散模型从4通道潜在空间升级至16通道潜在空间。在VAE微调过程中，采用通道分割蒸馏技术保持前四个通道与预训练先验对齐，同时分配额外通道以有效编码高频细节。我们进一步设计了先验保持适配方法，以平滑桥接4通道扩散主干与高维16通道潜在空间之间的不匹配。此外，我们提出基于修复质量评分标注的逐样本解码器路由机制，该机制能提升不同条件下的视觉质量。在合成数据集和真实数据集上的实验表明，该方法在轻度退化条件下能以更高保真度和更少伪影取得具有竞争力的结果，同时保持单步推理效率。代码与模型将在https://github.com/gobunu/LCUDiff发布。