Nice, small, and fast realtime CNN-based upscaler. Trained on visual novel screenshots/CG.
Currently very new and immature 😭.
Supports exporting to an mpv memeshader!
And now a Magpie effect!
The order of quality is 8x32 > 4x32 > 4x24 > 4x16 > 4x12 > 3x12 > 2x12 > fast > faster > veryfast. Conversely the order of speed would be the reverse.
The CuNNy-fast shader is the recommended shader if you're on a slow machine.
Variants:
- (No-suffix): Trained to be neutral and do no denoising or sharpening.
SOFT: Trained to anti-alias & produce a soft output. Is probably the most 'artifact-free' variant if you don't count blur.DS: Trained to denoise & sharpen.NVL: Trained to upscale visual novel games & high-quality illustrations.
mpv shaders are found inside the mpv/ directory.
Metric-focused variants are found inside the results/ directory.
To activate, add glsl-shader="path/to/shader.glsl" to your
mpv.conf.
There are versions of the mpv shaders use 8-bit dp4a instructions. They can be
many times faster than the standard upscaling shader depending on if your
hardware supports accelerated dp4a instructions. Requires vo=gpu-next with
gpu-api=vulkan. They can be found inside the dp4a/ subdirectories.
Magpie effects are found inside the magpie/ directory.
To install, drag the files into the effects subfolder inside the root of your
Magpie installation. Once installed you will be able to add the effect in the
Scaling modes tab.
As CuNNy only upscales by a factor of 2x, if you are scaling a ratio between 1 and 2 (i.e. 720 -> 1080 [1.5x]) you will need to downscale after upscaling.
The choice of downscaler has a large effect on the resulting image quality and
sharpness. A high-quality downscale effect is provided in this repository
located inside the same magpie/ directory. Apply the downscale effect after
the upscale effect.
Tested training with PyTorch nightly on Linux with Python 3.11. If any errors arise try using nightly.
Create folders by running sh scripts/mkfolders.sh.
Prepare data by running sh scripts/build.sh, then sh scripts/split.sh <input-folder> <output-128-grids>, then py scripts/proc.py <128-grids> <out>.
To train py train.py <data> <N> <D> where N is the number of internal
convolutions and D is the number of feature layers.
Convert the resulting model to an mpv shader by running
py mpv.py <models/model.pickle>.
Convert the resulting model to a Magpie effect by running
py magpie.py <models/model.pickle>.
Trains very fast on my machine.
See results/.
LGPL v3