标题： QuantFace：一步扩散人脸修复的低比特后训练量化 显示英文标题

标题： QuantFace: Low-Bit Post-Training Quantization for One-Step Diffusion Face Restoration

摘要： 扩散模型在人脸修复方面取得了显著的性能。然而，扩散模型的繁重计算使其在智能手机等设备上的部署变得困难。在这项工作中，我们提出了 QuantFace，一种针对一步式扩散人脸修复模型的新颖低比特量化方法，其中全精度（\ie , 32位）权重和激活被量化为 4$\sim$6位。 我们首先分析了激活中的数据分布，发现它们高度可变。为了保留原始数据信息，我们采用了旋转-缩放通道平衡。此外，我们提出了量化蒸馏低秩适应（QD-LoRA），该方法联合优化量化和蒸馏性能。最后，我们提出了一种自适应比特宽度分配策略。我们将这种策略形式化为一个整数规划问题，结合量化误差和感知度量来找到令人满意的资源分配。 在合成和真实世界数据集上的大量实验证明了 QuantFace 在 6 位和 4 位下的有效性。QuantFace 在最近领先的低比特量化方法中实现了显著优势。代码可在 https://github.com/jiatongli2024/QuantFace 获取。 显示英文摘要

摘要： Diffusion models have been achieving remarkable performance in face restoration. However, the heavy computations of diffusion models make it difficult to deploy them on devices like smartphones. In this work, we propose QuantFace, a novel low-bit quantization for one-step diffusion face restoration models, where the full-precision (\ie, 32-bit) weights and activations are quantized to 4$\sim$6-bit. We first analyze the data distribution within activations and find that they are highly variant. To preserve the original data information, we employ rotation-scaling channel balancing. Furthermore, we propose Quantization-Distillation Low-Rank Adaptation (QD-LoRA) that jointly optimizes for quantization and distillation performance. Finally, we propose an adaptive bit-width allocation strategy. We formulate such a strategy as an integer programming problem, which combines quantization error and perceptual metrics to find a satisfactory resource allocation. Extensive experiments on the synthetic and real-world datasets demonstrate the effectiveness of QuantFace under 6-bit and 4-bit. QuantFace achieves significant advantages over recent leading low-bit quantization methods for face restoration. The code is available at https://github.com/jiatongli2024/QuantFace.