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Funny, I can't actually find any ncnn vs dml benchmarks for rife. I'm inferring from the relative performance compared to cuda and trt.
https://github.com/n00mkrad/flowframes/ … chmarks.md
Benchmark 2 https://github.com/AmusementClub/vs-mlr … /v14.test4

I might just do some benchmarks myself in VSEditor.

Reading through the thread it looks like the current consensus for worthwhile models from increasing quality/decreasing performance are 4.4v2, 4.6v2, 4.9v2/4.12_litev2/4.13_litev2, and placebo tier would be something like 4.13_ensemblev2 I guess? 4.12/13 are apparently better for animation, but otherwise slower than 4.9.

Problem is if you disable it then the generalized models which are too large for my vram size are generated instead.

22:33:32.546 [i]: AVSF: found new player instance
22:33:32.572 [i]: AVSF: filters in use: LAV Splitter Source -> LAV Video Decoder -> * -> madVR
22:33:32.573 [i]: AVSF: new video in mpc-hc64.exe (64-bit) [MPC-HC 2.0.0.0] on screen 0
22:33:32.614 [i]: Media: video 1920x1440 [PAR 1.000] at 23.976 fps
22:33:32.614 [i]: Media: codec type is AV1, YUV/4:2:0/10 bits
22:33:32.615 [i]: Playback: starting up...
22:33:32.619 [i]: Playback [73c0e3c8]: Frame server (64-bit) C:\Users\Sahil\AppData\Local\Programs\VapourSynth\core\vapoursynth.dll
22:33:32.620 [i]: Playback [73c0e3c8]: resulting video frame 1920x1440
22:33:32.621 [i]: Playback [73c0e3c8]: 4 acceptible profiles, best is 'RIFE AI engine' [5000]
22:33:32.623 [i]: Playback [73c0e3c8]: enabled while video is playing
22:33:32.626 [i]: Playback [73c0e3c8]: playing at 59.94 [23.976 *5/2] /10 bit
22:33:32.939 [E]: Playback [73c0e3c8]: VS - Python exception: [WinError 740] The requested operation requires elevation
22:33:32.939 [E]: Playback [73c0e3c8]: VS - Traceback (most recent call last):
22:33:32.939 [E]: Playback [73c0e3c8]: VS - File \src\cython\vapoursynth.pyx\, line 2866, in vapoursynth._vpy_evaluate
22:33:32.939 [E]: Playback [73c0e3c8]: VS - File \src\cython\vapoursynth.pyx\, line 2867, in vapoursynth._vpy_evaluate
22:33:32.939 [E]: Playback [73c0e3c8]: VS - File \C:\Users\Sahil\AppData\Roaming\SVP4\scripts\73c0e3c8.py\, line 77, in <module>
22:33:32.940 [E]: Playback [73c0e3c8]: VS - smooth = interpolate(clip)
22:33:32.940 [E]: Playback [73c0e3c8]: VS - File \C:\Users\Sahil\AppData\Roaming\SVP4\scripts\73c0e3c8.py\, line 56, in interpolate
22:33:32.940 [E]: Playback [73c0e3c8]: VS - smooth = RIFE(input_m,multi=rife_num,model=rife_mnum,backend=trt_backend)
22:33:32.940 [E]: Playback [73c0e3c8]: VS - File \C:\Program Files (x86)\SVP 4\rife\vsmlrt.py\, line 936, in RIFE
22:33:32.940 [E]: Playback [73c0e3c8]: VS - output0 = RIFEMerge(
22:33:32.940 [E]: Playback [73c0e3c8]: VS - File \C:\Program Files (x86)\SVP 4\rife\vsmlrt.py\, line 821, in RIFEMerge
22:33:32.940 [E]: Playback [73c0e3c8]: VS - return inference_with_fallback(
22:33:32.940 [E]: Playback [73c0e3c8]: VS - File \C:\Program Files (x86)\SVP 4\rife\vsmlrt.py\, line 1420, in inference_with_fallback
22:33:32.940 [E]: Playback [73c0e3c8]: VS - raise e
22:33:32.940 [E]: Playback [73c0e3c8]: VS - File \C:\Program Files (x86)\SVP 4\rife\vsmlrt.py\, line 1399, in inference_with_fallback
22:33:32.940 [E]: Playback [73c0e3c8]: VS - return _inference(
22:33:32.940 [E]: Playback [73c0e3c8]: VS - File \C:\Program Files (x86)\SVP 4\rife\vsmlrt.py\, line 1339, in _inference
22:33:32.940 [E]: Playback [73c0e3c8]: VS - engine_path = trtexec(
22:33:32.940 [E]: Playback [73c0e3c8]: VS - File \C:\Program Files (x86)\SVP 4\rife\vsmlrt.py\, line 1172, in trtexec
22:33:32.940 [E]: Playback [73c0e3c8]: VS - completed_process = subprocess.run(args, env=env, check=False, stdout=sys.stderr)
22:33:32.940 [E]: Playback [73c0e3c8]: VS - File \C:\Users\Sahil\AppData\Local\Programs\Python\Python38\Lib\subprocess.py\, line 493, in run
22:33:32.940 [E]: Playback [73c0e3c8]: VS - with Popen(*popenargs, **kwargs) as process:
22:33:32.940 [E]: Playback [73c0e3c8]: VS - File \C:\Users\Sahil\AppData\Local\Programs\Python\Python38\Lib\subprocess.py\, line 858, in __init__
22:33:32.940 [E]: Playback [73c0e3c8]: VS - self._execute_child(args, executable, preexec_fn, close_fds,
22:33:32.940 [E]: Playback [73c0e3c8]: VS - File \C:\Users\Sahil\AppData\Local\Programs\Python\Python38\Lib\subprocess.py\, line 1311, in _execute_child
22:33:32.940 [E]: Playback [73c0e3c8]: VS - hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
22:33:32.940 [E]: Playback [73c0e3c8]: VS - OSError: [WinError 740] The requested operation requires elevation

Issue resolved by running mpc-hc in admin oddly.

Another issue. Trt inference model has to be built or cached for every new resolution. This caused me a lot of confusion for real time playback as it would hang and there's no indication how long that will take to finish building. Once that's done realtime playback works. It might help new users if the TensorRT version came with a benchmark tool that contained some reference footage at different resolutions and gave a notification when all the refence models for common resolutions were saved. Also going over the vsmlrt documentation, it's extremely important to set fp16, num_parameters, and previously workspace before the inference model is made. How is svp handling fp16 vs fp32 and does the gpu thread setting refer to the num_stream parameter in vsmlrt?

According to this, HDR is treated like a regular color format.
https://forum.doom9.org/showthread.php?t=175205
http://avisynth.nl/index.php/Avisynthplus_color_formats
https://github.com/FFMS/ffms2/blob/mast … visynth.md

Worth looking into.

Good to know, maybe this fork has worked it out?
http://ffdshow-tryout.sourceforge.net/changelog.php

Seems like everyone's suspicions that Ryzen's core count and love for high frequency memory would mean it performs well here. What I'm really wondering is if Threadripper with its 4 memory channels will do even better. Would love to see a comparison between a 1700 and 1900X.

https://imouto.my/tutorials/madvr/
https://wiki.mikejung.biz/MadVR#madVR_Overview
http://www.svp-team.com/forum/viewtopic.php?id=2317
https://forum.doom9.org/showthread.php?t=171787

It's a little outdated but all still relevant. I switched to NGU Anti-Aliasing from NNEDI3 because it has better render times and is tuned for animation. I would also recommend getting rid of profile switching based on filenames/formats because it makes no sense, just go by resolution. Which MadVR settings you end up using depends entirely on the power of your GPU. Since it will need to render each extra frame from SVP, the GPU gets pushed considerably more. Still the biggest limiting factor in my experience has been ram speed. Any ram OC dramatically improves SVP performance provided your CPU meets the minimum requirments.

MadVR still has some high end upscalers that haven't been implemented in mpv like NGU which in my experience is sharper than NNEDI3 in some scenarios but significantly faster which is vital for interpolation. For my mostly anime usecase, NGU w/AA had the best results though if I had a 10 bit monitor, I'd probably make the switch to mpv. Still I've had great results with madVR, able to get 90 fps across the board with no dropped frames. I think the biggest bottleneck is ram speed actually since neither my GPU or CPU are getting near full utilization, but I can't get up to 120 fps. That was only possible after overclocking my ram. Couldn't get over 75 fps beforehand, and on my old DDR3 PC had dropped frames at 60 fps.

QT is just a gui for mpv which of course has vapoursynth integration which works much better than avisynth+ and ffdshow raw video filter which is used for MPC-HC. Unfortunately ffdshow doesn't allow 10 bit. Madshi mentioned in the past that he was planning vapoursynth integration eventually but it's been over a year since then with no further mention. He seems to be focused on tuning NGU and HDR support for now.

With good profile configurations you can fine tune upscaling based on the native resolution of the video with either program I believe. I know you can for madVR at least.