Generative models operate at fixed resolution, even though natural images
come in a variety of sizes. As high-resolution details are downsampled away and
low-resolution images are discarded altogether, precious supervision is lost.
We argue that every pixel matters and create datasets with variable-size
images, collected at their native resolutions. To take advantage of varied-size
data, we introduce continuous-scale training, a process that samples patches at
random scales to train a new generator with variable output resolutions. First,
conditioning the generator on a target scale allows us to generate higher
resolution images than previously possible, without adding layers to the model.
Second, by conditioning on continuous coordinates, we can sample patches that
still obey a consistent global layout, which also allows for scalable training
at higher resolutions. Controlled FFHQ experiments show that our method can
take advantage of multi-resolution training data better than discrete
multi-scale approaches, achieving better FID scores and cleaner high-frequency
details. We also train on other natural image domains including churches,
mountains, and birds, and demonstrate arbitrary scale synthesis with both
coherent global layouts and realistic local details, going beyond 2K resolution
in our experiments. Our project page is available at:
this https URL

研究表明，变化尺寸的数据可以更好地利用多分辨率训练，新的基于连续比例尺的训练方法可使用原始分辨率大小的图像数据和连续坐标取样生成高分辨率图像，并在自然图像数据集上获得更好的实验结果。