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Install problems - solved #26
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@Samhayne Can you also tell me which version of Pyton you used, because all of the things I have applied, downloaded the manual zip and whatever, but still not working. I use pyton 3.11.4 |
To be honest tried two hours to install, absolutly not able to install this. |
Grüß Dich, Jens. I believe the reason was that there was only a wheel for 3.10 (...cp310...) in the releases zip. |
Grüß dich auch Sam, I think, if I work more on this, I could even bring this to work, but I do not know, if this is worth it. I actually use FlowFrames 1.40 to interpolate videos, but there is a strange bug, where it slows down after a while. The same did not happen on NCNN-VULKAN Vapoursynth. So i wanted to check, if pytorch is responsible for the slowness. Also NCNN-VULKAN in Flowframes is even 10 Frames faster and seems to scale with greater GPU cards, where the pytorch version does not. To find out, why this is happening, I was researching and trying to find where actually the error happens and if this is happening on holywu vs-rife and what the performance is. Can you give me a perhaps a number of how much fps you get on a 720p video with 2x upscaling and if there is a slowdown, if you let it run for about 20 mins or more? |
Hey Jens, I can tell you that I won‘t get a slowdown with 4K videos. |
Hello, It seems that it is round about as fast as the flowframes pytorch rife method. 1280x720 = 921.600 I get around 39 frames with pytorch/cuda on a NVIDIA RTX 3060 Laptop with 720p So HolyWU/Vs-rife seems to be not faster than flowframes. However I need to still find out why the error happens. Lets research more. |
Ok. Here are some numbers for you, @jensdraht1999, for my GTX 1070 @ 720p: clip = RIFE(clip, model='4.6', scale=1.0, num_streams=2, ensemble=True) clip = RIFE(clip, model='4.6', scale=1.0, num_streams=2) |
Hello @Samhayne, Thank you indeed for the number. My numbers with Flowframes: @ 720p with CUDA/PYTORCH (NVIDIA ONLY) FP32 : 48 FPS @ 720p with VULKAN/NCNN/VAPOURSYNTH FP32 :53 FPS I do not even know, if those halfprecision settings apply, it seems not. The odd thing is, that VULKAN/NCNN implemenation from Vapoursynth is much faster and it does not get slower with time, it takes like 30 min to do the job, cuda/pytorch 2 hours. And there is not even waiting for the images to extract and encode the images to video later, because it's done on the fly. VULKAN/NCNN/VAPOURSYNTH has been used with 8 threads. |
@Samhayne I wanted to use this to test out, but I could not make it work. 1.) Install Python 3.10.11 Traceback (most recent call last): I do not understand what I am suppossed to do? Do you have example script, which is working? |
@jensdraht1999 Hm. Did you install PyTorch? You shouldn't need to run any of the scripts manually to install vsrife. From the main page:
|
@Samhayne you mean after step 5 right? I mean for the scripts to execute... |
Yes, I “just” installed the mentioned dependencies as described on their project pages and then invoked the two lines to install vsrife and fetch the models. |
Ok perfect, then I will try again with pytorch installed. I had installed pytorch 2.0.1, now will try 1.13. Thank you very much!!! |
@Samhayne I think I have solved all installation problems. However, how you are using this exactly? No experience with VS in general. I have a script called interpolate.py with following content: import vapoursynth as vs clip = video_in Then I have start it like this? vspipe -c y4m "C:\Users{UserName}\Desktop\vsss\interpolate.py" - | "C:\Users\King\Desktop\vsss\ffmpeg.exe" -i - "C:\Users\King\Desktop\vsss\test.mp4" |
Hey @jensdraht1999, I'm using StaxRip for my encodings and rewired the dependency paths, so I could use vs-rife, This is the (partly generated) script I'm using for 4k videos - you just would need to adapt the paths:
...and (grabbed from the log outputs) under the hood is invoked (depending on the chosen piping) by...
-or-
-or- requires DJATOM/x265-aMod or Patman86/x265-Mod-by-Patman: I hope this was still helpful. |
@Samhayne Thanks. I will have a look and then look what can be done. Thanks again! |
I have followed your instructions and it also works. For example: This here: with following modified script: sys.path.append(r"C:\Users{UserName}\Desktop\vss\StaxRip-v2.29.0-x64\Apps\Plugins\VS\Scripts") This will be 112 Frames. So it will take around 15 Minutes. The reason why I have set num_streams=12 is, because every stream is taking around 0.42gb, so it will take 5.1 VRAM and it is getting a significant boost at least on 720p video. If the number of streams is set lower, it's slower, around 80 fps. So yeah, TRT really gives this a boost of 40fps, which in percent is 40% boost. (79 FPS x 1,40 = 110,6 FPS) |
BTW RIFE 4.7 is somehow a lot of slower. I am getting around 80FPS with it. The difference is quite big, perhaps a bug? @HolyWu |
@jensdraht1999 I didn’t use Rife 4.7 much as it gives me more artifacts than 4.6 (or 4.0 which is even more artifact resistant, but unfortunately no trt support). I don’t encode Animes either. |
@Samhayne Yeah, you are probably right, this might be the reason, it's totally bad performing. BTW, here is the full script for interpolating a video with audio: cd "C:\Users{UserName}\Desktop\vss" Script.vpy: sys.path.append(r"C:\Users{UserName}\Desktop\vss\StaxRip-v2.29.0-x64\Apps\Plugins\VS\Scripts") |
Ran into 2 problems, installing the current vs-rife version (on Windows) today, that cost me some time to resolve and here are the solutions:
Problem 1:
Fixed by installing psutil:
pip install psutil
Problem 2:
Looks like at PyPI there's no Windows version of TensorRT available at the moment. (see: NVIDIA/TensorRT#2933 (comment))
(Couldn't get an older version either from there)
Solution: Install manually from zip as described here:
https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html#installing-zip
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