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Scaling algorithms

Started by gameblabla, September 09, 2016, 02:05:54 am

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VMike

Quote from: gameblabla on April 03, 2017, 12:18:15 am
Quote from: VMike on April 02, 2017, 11:53:19 pm
What about speed? To get the result I have got in the videos I have made, each frame took almost 0.5 seconds to render (980ti). I don't know how well optimized waifu is of course.

This means that the emulator would only be able to render at 2 frames per second... Not much if you ask me
I doubt Waifu2x can be improved that much. Not with significant differences anyway

Yeah, that is sad to hear.

p2

im im not mistsken waifu2x only takes parts of the computing resources, not the maximum. So shouldnt it be possible ot use multiple instances of waifu2x at the same time, looping through them, each grabbing a frame?
Anyway war sucks. Just bring us your food instead of missiles  :P ~ DJ Omnimaga (11.10.2016 20:21:48)
if you cant get a jframe set up, draw stuff to it, and receive input, i can only imagine how horrible your game code is _._   ~ c4ooo (14.11.2016 22:44:07)
If they pull a Harambe on me tell my family I love them ~ u/Pwntear37d (AssangeWatch /r/)
make Walrii great again ~ DJ Omnimaga (28.11.2016 23:01:31)
God invented the pc, satan the smartphone I guess ~ p4nix (16.02.2017 22:51:49)

VMike

Quote from: p2 on April 03, 2017, 09:56:20 am
im im not mistsken waifu2x only takes parts of the computing resources, not the maximum. So shouldnt it be possible ot use multiple instances of waifu2x at the same time, looping through them, each grabbing a frame?

That is true. I did look at Afterburner and I THINK it was around 50%. I am not sure thou, it was not using it max at least like some games. I will check that later.
Now if someone did make this now it would be fun.
Waifu2x minimum requirements - GTX 1080Ti
Recomended - N/A

VMike

April 13, 2017, 05:46:55 pm #48 Last Edit: April 13, 2017, 05:48:43 pm by VMike
Apperently, a neural network called FSRCNN is suppose to be much faster than waifu2x, easily 30fps, and still almost look as good as it (if trained correct), or better in some cases.

http://mmlab.ie.cuhk.edu.hk/projects/FSRCNN.html

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