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Old 19th March 2021, 07:52   #1  |  Link
Selur
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Any ML based frame rate interpolation for Vapoursynth?

Just wondering is someone keeping an eye on 'newer' ML based motion interpolation stuff, like:I'm only aware that:(both have patreon pages)
and there are ncnn implemenation for:
https://github.com/nihui/cain-ncnn-vulkan
https://github.com/nihui/rife-ncnn-vulkan
sadly for none of those there are Vapoursynth filters, or is there and I'm simply not aware of it.

So I'm wondering:
a. Are there any ML based frame interpolation filters for Vapoursynth I'm not aware of?
b. Is someone keeping an eye on the development of those filters?

Cu Selur
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Old 2nd June 2021, 18:58   #2  |  Link
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Lots of thanks to HolyWu for implementing a RIFE Vapoursynth version (https://github.com/HomeOfVapourSynth...FE-ncnn-Vulkan) !
Thanks!

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Old 2nd June 2021, 19:55   #3  |  Link
poisondeathray
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Quote:
Originally Posted by Selur View Post
Lots of thanks to HolyWu for implementing a RIFE Vapoursynth version (https://github.com/HomeOfVapourSynth...FE-ncnn-Vulkan) !
Thanks!

Cu Selur

Yay!

Thanks
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Old 4th December 2021, 12:21   #4  |  Link
Selur
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Just as a small follow up: Nowadays there are two RIFE ports for Vapoursynth:both work fine here, but sadly there are no ports of DAIN, CAIN and FLAVR so far.

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Old 7th December 2021, 06:29   #5  |  Link
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Just wondering, for the people who have tried RIFE, how's the speed vs what we currently have like svpflow, interframe etc?
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Old 7th December 2021, 20:35   #6  |  Link
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RIFE is way slower and SVP and mvtools based interpolation and in no way usable for real time playback.
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Old 14th December 2021, 20:32   #7  |  Link
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Btw. did anyone do some testing with the new vs-rife which is now based on Practical-RIFE and would share some of his impressions?
(a. seems faster, b. allows higher (whole number) multipliers than 2)
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Old 2nd October 2022, 16:55   #8  |  Link
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https://github.com/styler00dollar/Va...FE-ncnn-Vulkan is a fork from HomeOfVapourSynthEvolution/VapourSynth-RIFE-ncnn-Vulkan which adds a bunch of new RIFE models.

Quote:
Nihui always just exports the fastest model (ensemble False, Fast True), while the other ones can result in a better image. Ensembling combines the flow of two different interpolations within the model and if fastmode is false it calls contextnet. You can see it here.
see: https://github.com/nihui/rife-ncnn-vulkan/issues/50
might also be interessting.

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