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Old 3rd February 2023, 07:55   #181  |  Link
PatchWorKs
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Quote:
Originally Posted by Selur View Post
Not that surprising, since there are not many threads that are image centric.
Usually it's about video. And image based filters can easily lead to temporal inconsistencies.
Very interesting reply by aydin's author @ my GitHub "issue":
Quote:
Originally Posted by Ahmet Can Solak
I want to clarify that Aydin works on spatio-temporal data(TZYX) when all four dimensions are available and can do TYX as well. Aydin doesn't read video file formats directly though, if you can save it as a file format that is supported then you can denoise movies too. With biological live imaging datasets, we use Aydin for denoising movies on daily basis.
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Old 14th February 2023, 08:43   #182  |  Link
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@Selur and anyone else interested in participating are called to collaborate by "make bug reports/feature requests here on GitHub" according to the aydin's author:
https://github.com/royerlab/aydin/is...ent-1428538108
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Old 11th March 2023, 11:21   #183  |  Link
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btw. has anyone tested if there are speed differences depending on the RGB color space VSGAN is fed?
Just noticed that for me (only testst a few models), RGBS seems to be ~10% faster than when I feed any other RGB color space.
-> Is it just me or can others confirm this?

Cu Selur
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Old 11th March 2023, 16:11   #184  |  Link
poisondeathray
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Quote:
Originally Posted by Selur View Post
btw. has anyone tested if there are speed differences depending on the RGB color space VSGAN is fed?
Just noticed that for me (only testst a few models), RGBS seems to be ~10% faster than when I feed any other RGB color space.
-> Is it just me or can others confirm this?

Cu Selur
Similar for me ; RGBS was about 7-10% faster than RGB24 (source was RGB24)
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Old 11th March 2023, 17:41   #185  |  Link
Selur
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Thanks for testing
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Old 11th March 2023, 22:30   #186  |  Link
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Argh, I got a strange problem when using vsdpir and vsgan inside the same script:

using both with RGH:
Code:
from vsdpir import dpir as DPIR
# adjusting color space from YUV420P8 to RGBH for vsDPIRDenoise
clip = core.resize.Bicubic(clip=clip, format=vs.RGBH, matrix_in_s="470bg", range_s="limited")
# denoising using DPIRDenoise
clip = DPIR(clip=clip, strength=5.000, task="denoise", device_index=0, trt=True, trt_cache_path="J:/tmp")
# changing range from limited to full range
clip = core.resize.Bicubic(clip, range_in_s="limited", range_s="full")
# Setting color range to PC (full) range.
clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=0)
# resizing using VSGAN
from vsgan import ESRGAN
vsgan = ESRGAN(clip=clip,device="cuda")
model = "C:/Users/Selur/Desktop/testing/1x_Dotzilla_Compact_80k_net_g.pth"
vsgan.load(model)
vsgan.apply() # 640x352
clip = vsgan.clip
works.

using both with RGBS:
Code:
from vsdpir import dpir as DPIR
# adjusting color space from YUV420P8 to RGBS for vsDPIRDenoise
clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, matrix_in_s="470bg", range_s="limited")
# denoising using DPIRDenoise
clip = DPIR(clip=clip, strength=5.000, task="denoise", device_index=0, trt=True, trt_cache_path="J:/tmp")
# changing range from limited to full range
clip = core.resize.Bicubic(clip, range_in_s="limited", range_s="full")
# Setting color range to PC (full) range.
clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=0)
# resizing using VSGAN
from vsgan import ESRGAN
vsgan = ESRGAN(clip=clip,device="cuda")
model = "C:/Users/Selur/Desktop/testing/1x_Dotzilla_Compact_80k_net_g.pth"
vsgan.load(model)
vsgan.apply() # 640x352
clip = vsgan.clip
works.

Using VSDIR with RGBS and VSGAN with RGBH:
Code:
from vsdpir import dpir as DPIR
# adjusting color space from YUV420P8 to RGBS for vsDPIRDenoise
clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, matrix_in_s="470bg", range_s="limited")
# denoising using DPIRDenoise
clip = DPIR(clip=clip, strength=5.000, task="denoise", device_index=0, trt=True, trt_cache_path="J:/tmp")
# changing range from limited to full range
clip = core.resize.Bicubic(clip, range_in_s="limited", range_s="full")
# Setting color range to PC (full) range.
clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=0)
# adjusting color space from RGBS to RGBH for vsVSGAN
clip = core.resize.Bicubic(clip=clip, format=vs.RGBH, range_s="full", dither_type="error_diffusion")
# resizing using VSGAN
from vsgan import ESRGAN
vsgan = ESRGAN(clip=clip,device="cuda")
model = "C:/Users/Selur/Desktop/testing/1x_Dotzilla_Compact_80k_net_g.pth"
vsgan.load(model)
vsgan.apply() # 640x352
clip = vsgan.clip
works.

Using VSDIR with RGBH and VSGAN with RGBS:
Code:
from vsdpir import dpir as DPIR
# adjusting color space from YUV420P8 to RGBH for vsDPIRDenoise
clip = core.resize.Bicubic(clip=clip, format=vs.RGBH, matrix_in_s="470bg", range_s="limited")
# denoising using DPIRDenoise
clip = DPIR(clip=clip, strength=5.000, task="denoise", device_index=0, trt=True, trt_cache_path="J:/tmp")
# changing range from limited to full range
clip = core.resize.Bicubic(clip, range_in_s="limited", range_s="full")
# Setting color range to PC (full) range.
clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=0)
# adjusting color space from RGBH to RGBS for vsVSGAN
clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, range_s="full")
# resizing using VSGAN
from vsgan import ESRGAN
vsgan = ESRGAN(clip=clip,device="cuda")
model = "C:/Users/Selur/Desktop/testing/1x_Dotzilla_Compact_80k_net_g.pth"
vsgan.load(model)
vsgan.apply() # 640x352
clip = vsgan.clip
fails with:
Code:
Input type (float) and bias type (struct c10::Half) should be the same

Using VSDIR with RGBS and VSGAN with RGB48:
Code:
from vsdpir import dpir as DPIR
# adjusting color space from YUV420P8 to RGBS for vsDPIRDenoise
clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, matrix_in_s="470bg", range_s="limited")
# denoising using DPIRDenoise
clip = DPIR(clip=clip, strength=5.000, task="denoise", device_index=0, trt=True, trt_cache_path="J:/tmp")
# changing range from limited to full range
clip = core.resize.Bicubic(clip, range_in_s="limited", range_s="full")
# Setting color range to PC (full) range.
clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=0)
# adjusting color space from RGBS to RGB48 for vsVSGAN
clip = core.resize.Bicubic(clip=clip, format=vs.RGB48, range_s="full", dither_type="error_diffusion")
# resizing using VSGAN
from vsgan import ESRGAN
vsgan = ESRGAN(clip=clip,device="cuda")
model = "C:/Users/Selur/Desktop/testing/1x_Dotzilla_Compact_80k_net_g.pth"
vsgan.load(model)
vsgan.apply() # 640x352
clip = vsgan.clip
produces graphic glitches here https://ibb.co/jTCbghX

Cu Selur

Ps.: I also reported this to https://github.com/rlaphoenix/VSGAN/issues/31, but thought it might be interesting to others in case they run into the same issue.
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Old 12th March 2023, 07:53   #187  |  Link
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Okay, issue seems to be that VSDPIR overwrites the default tensor type. (so not a bug in VSGAN, but an unexpected behavior of VSDPIR)
Using:
Code:
import torch
torch.set_default_tensor_type(torch.FloatTensor)
after VSDPIR, should fix this it.

Behavior is fixed with https://github.com/HolyWu/vs-dpir/releases/tag/v3.0.1

Cu Selur
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Last edited by Selur; 12th March 2023 at 08:49.
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Old 19th October 2023, 04:14   #188  |  Link
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New 2x upscaling model for anime AniScale2
https://github.com/Sirosky/Upscale-H.../tag/AniScale2

It only works for digital created animes. It came with a base upscale model and a refiner model for sharpening. The model has depth of field awareness so the intended blur effect will be kept intact in the result.

I tested it a little on BLEACH and it looks pretty convincing.

Last edited by lansing; 19th October 2023 at 04:24.
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Old 19th October 2023, 17:11   #189  |  Link
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Interesting, thanks for the info.
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