This paper develops a unified framework for image-to-image translation based
on conditional diffusion models and evaluates this framework on four
challenging image-to-image translation tasks, namely colorization, inpainting,
uncropping, and JPEG restoration. Our simple implementation of image-to-image
diffusion models outperforms strong GAN and regression baselines on all tasks,
without task-specific hyper-parameter tuning, architecture customization, or
any auxiliary loss or sophisticated new techniques needed. We uncover the
impact of an L2 vs. L1 loss in the denoising diffusion objective on sample
diversity, and demonstrate the importance of self-attention in the neural
architecture through empirical studies. Importantly, we advocate a unified
evaluation protocol based on ImageNet, with human evaluation and sample quality
scores (FID, Inception Score, Classification Accuracy of a pre-trained
ResNet-50, and Perceptual Distance against original images). We expect this
standardized evaluation protocol to play a role in advancing image-to-image
translation research. Finally, we show that a generalist, multi-task diffusion
model performs as well or better than task-specific specialist counterparts.
Check out this https URL for an overview of the results.

本文提出了一种基于条件扩散模型的统一框架，用于图像到图像的转换，并在四个具有挑战性的图像到图像任务中测试了这个框架，即上色，修复，裁剪和 JPEG 恢复。我们的简单实现超越了所有任务上的强 GAN 和回归基线，无需任务特定的超参数调整，架构定制或需要任何辅助丢失或先进的新技术。我们揭示了扩散目标中 L2 与 L1 损失的影响，并通过实证研究证明了自我关注在神经结构中的重要性。重要的是，我们提倡一个基于 ImageNet 的统一评估协议，具有人工评估和样本质量得分（FID，Inception Score，预训练 ResNet-50 的分类准确度和与原始图像的感知距离），我们期望这个标准化的评估协议在推进图像到图像翻译研究方面起到作用。最后，我们展示了一个通用的，多任务扩散模型的执行效果与任务特定的专家模型相当或更好。