Any-resolution Training for High-resolution Image Synthesis
- URL: http://arxiv.org/abs/2204.07156v1
- Date: Thu, 14 Apr 2022 17:59:31 GMT
- Title: Any-resolution Training for High-resolution Image Synthesis
- Authors: Lucy Chai, Michael Gharbi, Eli Shechtman, Phillip Isola, Richard Zhang
- Abstract summary: Generative models operate at fixed resolution, even though natural images come in a variety of sizes.
We argue that every pixel matters and create datasets with variable-size images, collected at their native resolutions.
We introduce continuous-scale training, a process that samples patches at random scales to train a new generator with variable output resolutions.
- Score: 55.19874755679901
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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. Taking advantage of this data is
challenging; high-resolution processing is costly, and current architectures
can only process fixed-resolution 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 resolutions 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 our method takes advantage of the 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: https://chail.github.io/anyres-gan/.
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