EEDI2 filter using CUDA
Ported from HolyWu's plugin.
eedi2cuda.EEDI2(clip clip, int field[, int mthresh=10, int lthresh=20, int vthresh=20, int estr=2, int dstr=4, int maxd=24, int map=0, int nt=50, int pp=1, int[] planes=[0, 1, 2], int num_streams=1, int device_id=-1])
Arguments have the exactly same meanings with the ones of Holy's EEDI2. See descriptions there.
Only pp=1
is implemented.
Additional arguments:
planes
: specify which planes to process. ForEEDI2
andEnlarge2
, unprocessed planes in the result clip contain garbage. ForAA2
, unprocessed planes are copied as-is.num_streams
: specify the number of CUDA streams. The value must less than or equal tocore.num_threads
. A larger value increases the concurrency and also increases the GPU memory usage. The default valuenum_streams=1
is already fast enough.device_id
: set the GPU device ID to use. You must specify this argument for each call if you have multiple GPUs.
eedi2cuda.Enlarge2(clip clip[, int mthresh=10, int lthresh=20, int vthresh=20, int estr=2, int dstr=4, int maxd=24, int map=0, int nt=50, int pp=1, int[] planes=[0, 1, 2], int num_streams=1, int device_id=-1])
Enlarge the clip by 2. Offsets caused by doubling are neutralized. This can be considered as a faster equivalent of the code below:
el = core.eedi2cuda.EEDI2(clip, 1)
el = core.fmtc.resample(el, sy=[-.5, -.5 * (1 << clip.format.subsampling_h)])
el = core.std.Transpose(el)
el = core.eedi2cuda.EEDI2(el, 1)
el = core.fmtc.resample(el, sy=[-.5, -.5 * (1 << clip.format.subsampling_w)])
el = core.std.Transpose(el)
eedi2cuda.AA2(clip clip[, int mthresh=10, int lthresh=20, int vthresh=20, int estr=2, int dstr=4, int maxd=24, int map=0, int nt=50, int pp=1, int[] planes=[0, 1, 2], int num_streams=1, int device_id=-1])
Double and then scale back to do anti-aliasing. Offsets caused by doubling are neutralized. This can be considered as a faster equivalent of the code below:
w = clip.width
h = clip.height
aa = core.eedi2cuda.EEDI2(clip, 1)
aa = core.fmtc.resample(aa, w, h, sy=[-.5, -.5 * (1 << clip.format.subsampling_h)])
aa = core.std.Transpose(aa)
aa = core.eedi2cuda.EEDI2(aa, 1)
aa = core.fmtc.resample(aa, h, w, sy=[-.5, -.5 * (1 << clip.format.subsampling_w)])
aa = core.std.Transpose(aa)
It uses spline36 with extended filter size for downscaling.
EEDI2_CUDA
EEDI2_CUDA_Enlarge2
EEDI2_CUDA_AA2
Arguments are the same. All bindings share a unified DLL.
Using in mpv
You can use eedi2cuda in mpv as the upscaler for realtime playback.
First ensure the VapourSynth video filter is available in mpv.
Then copy the script below and save it as eedi2enlarge.vpy
:
src16 = video_in.resize.Point(format=video_in.format.replace(bits_per_sample=16))
src16.eedi2cuda.Enlarge2().set_output()
In commandline option, load the script:
mpv --vf=vapoursynth=eedi2enlarge.vpy
Or you can specify it in mpv.conf
.
In most scenarios, it is not necessary to use an advanced upscaler for UV planes. The script below does per-plane upscaling:
import vapoursynth as vs
from vapoursynth import core
src16 = video_in.resize.Point(format=video_in.format.replace(bits_per_sample=16))
w = src16.width
h = src16.height
y = core.eedi2cuda.Enlarge2(src16, planes=0)
uv = core.resize.Point(src16, w * 2, h * 2,
format=vs.YUV444P16,
resample_filter_uv="spline36")
core.std.ShufflePlanes([y, uv], [0, 1, 2], vs.YUV).set_output()
- A CUDA-enabled GPU of compute capability 5.0 or higher (Maxwell+).
- GPU driver 461.33 or newer.
Please refer to build.yml.
EEDI2CUDA | Holy's EEDI2 | |
---|---|---|
EEDI2 | 175.33 fps | 18.44 fps |
Enlarge2 | 71.78 fps | 6.42 fps |
AA2 | 104.56 fps | 9.62 fps |
1080P YUV420P16 input. num_streams=16
.
Tested on i7-10875H and RTX 2070.