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27th March 2023, 00:28 | #61 | Link | |
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Most models are posted here.
Interested on realesrgan-x4minus as well, but the link is broken on the model_data page, I found a link in a reddit post, searching again now. These are the Swin2SR models, the recommended for compressed jpeg photos. Haven't tested them, but as I could see Jpeg_dynamic seems the best, probably the others are also worth having a look: Code:
Swin2SR_Jpeg_dynamic.pth Swin2SR_ClassicalSR_X2_64.pth Swin2SR_ClassicalSR_X4_64.pth Swin2SR_CompressedSR_X4_48.pth Swin2SR_Lightweight_X2_64.pth About LDSR I found a post from here that says: Quote:
By the way, I wasn't aware either, but the wiki has also a page for "official" models which includes many of the ones listed above: https://upscale.wiki/wiki/Official_Research_Models
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i7-4790K@Stock::GTX 1070] AviSynth+ filters and mods on GitHub + Discussion thread Last edited by Dogway; 20th May 2023 at 13:11. |
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29th March 2023, 21:29 | #62 | Link |
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There is some promising update from neural-networks designers (authors of RIFE) about 'frame prediction' - https://github.com/megvii-research/CVPR2023-DMVFN . And recommended to check. It is expected to be better motion compensation engine in compare with current RIFE used in temporal denoising.
Can it be used in AVS via existing plugin or require plugin redesign ? |
29th March 2023, 21:51 | #63 | Link | ||
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Quote:
I posted some examples in this thread. https://forum.doom9.org/showthread.php?t=184387 Quote:
If someone makes a ncnn/vulkan compatible version then possibly avs version could materialize. None of the direct pytorch variants of any project can run directly in avs |
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30th March 2023, 06:19 | #64 | Link |
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"It uses 2 past frames to predict next frame"
It can be easily tested in tr=2 temporal denoising: n-2 and n-1 feed as t-1 and t frames from 2 past frames forward interpolation, n+2 and n+1 feed as pair of 2 next frames for backward interpolation to the past (engine should be time-axis symmetrical and do not know real time axis direction - sort of TENET movie idea) got 2 interpolated frames from 2 previous and 2 next - and pass it after interleaving with current n-frame to blending engine like vsTTempSmooth (sample-based, or mvtools blocks-based). Also developers promises finally move to multi-frames transforms analysis for better prediction and compensation for complex motion/transforms in case of non-constant speed motion and so on. But when it be released in some working demo may be still unknown. As I understand from paper https://arxiv.org/pdf/2303.09875.pdf there is still very active scientific research on image processing exist in some asian region (China ?) but the results still far enough for real testing and/or usage in AVS. But also from that paper it looks that the research group completely miss the main important task for interpolation engines of temporal denoising and MPEG compressability improving. So the currently engines in development can not be directly used for replacement denoise engines with any-tr like mvtools/MDegrainN. And 'large and very large' tr of about 10 or even 100+. Last edited by DTL; 30th March 2023 at 06:48. |
4th April 2023, 00:54 | #65 | Link | |
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Here's some other models compatible with avs-mlrt: https://github.com/the-database/mpv-...2x_animejanai/
Quote:
--- @dogway, I'll get to your requested models in a bit ... I've been away from my home PC. |
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19th May 2023, 13:44 | #67 | Link |
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Hi there!
I'm late to the party, but hey, never say never ehehehehe So, I tried with: Code:
ColorBars(848, 480, pixel_type="YV12") ConvertBits(32) ConvertToPlanarRGB() mlrt_ncnn(list_gpu=true) but when I tried with: Code:
ColorBars(848, 480, pixel_type="YV12") ConvertBits(32) ConvertToPlanarRGB() mlrt_ncnn(network_path="\\avs000\Ingest\MEDIA\temp\onnx-models\VHS-Sharpen-1x_46000_G.onnx", builtin=false, list_gpu=false) of course I have all the C++ Redistributable installed: and, despite the error, I can see the GPU VRAM being used: I was running Avisynth 3.7.3 x64 Beta 9 by Ferenc Pinter. I tried to switch to the IntelLLVM builds as suggested in the read-me on GitHub, but it didn't make any difference. On the other hand, when I tried a different model, it worked. For instance: Code:
ColorBars(848, 480, pixel_type="YV12") ConvertBits(32) ConvertToPlanarRGB() mlrt_ncnn(network_path="\\avs000\Ingest\MEDIA\temp\onnx-models\1x_BroadcastToStudioLite_485k.onnx", builtin=false, list_gpu=false) Before I fire up my Quadro P4000 and P5000, is it because the GTX 980Ti is too old for some models or is there some other reason behind it? In particular, it looks like the models whose .onnx files are 65MB don't work and the ones that are smaller do. For instance, 1x_ThePi7on-Solidd_Deborutify_UltraLite_260k_G.onnx also worked (and it's indeed just 4.6 MB). Same goes for realesr-general-wdn-x4v3.onnx, which worked just fine and again it's just 4.7MB. Last edited by FranceBB; 19th May 2023 at 13:57. |
19th May 2023, 15:05 | #68 | Link |
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My cheap GPU has trouble with the larger models. I can get the VHS-Sharpen-1x_46000_G model to work using the tilesize and overlap options and also fp16.
Code:
mlrt_ncnn(network_path=model, builtin=false, fp16=true, tilesize_w=width/4, tilesize_h=height/4, overlap_w=8, overlap_h=8) ----- While I'm here, I found some other models in the following pages.
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25th May 2023, 14:16 | #70 | Link | |
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I tested realesr-general-wdn-x4v3_opset16.onnx
Here are the results depicted in some nice previews, each with its SSIM score. Below you can find the script: Quote:
And here are the images stacked as: Original - PointResize NNEDI3 - ESRGAN I'm gonna pick one just to show why I'm sticking with NNEDI3: Images collection: Img1 - Img2 - Img3 - Img4 - Img5 - Img6 - Img7 - Img8 - Img9 - Img10 - Img11 - Img12 - Img13 - Img14 - Img15 - Img16 I guess I'm gonna stick with NNEDI3 for a while longer... |
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25th May 2023, 22:19 | #71 | Link |
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The result of that model looks very artificial. Have you tried any other models? I'd be worried about temporal consistency also. For real world video, the proprietary models from Topaz are good. Unfortunately those onnx models are housed in a password protected zip file .
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26th May 2023, 01:55 | #72 | Link |
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Delivering a good choice from 186GB trained models Topaz (with Proteus v3 until 3.0.6 at least)
still had the "ugly face syndrom" when guessing at maybe-face-content from less than 20 pixel size. For Proteus v4 this is promised to improve. Some more training on face guessing from such small pixel patches should do. But: Topaz VEAI 3 had Avisynth support killed, and users report many hassles. My paid updates ended with 3.0.6, for the time being I wasn't willing to spend again, so I stick to last Topaz 2.6.4. 23.10.2023 23:21 Just today I came across my post by chance and oops: Sorry for being completely OT. How did that get here ? Must have been tired a bit on 26.03.2023 03:01, will try to move that where it belongs.
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"To bypass shortcuts and find suffering...is called QUALity" (Die toten Augen von Friedrichshain) "Data reduction ? Yep, Sir. We're that issue working on. Synce invntoin uf lingöage..." Last edited by Emulgator; 23rd October 2023 at 22:22. |
22nd October 2023, 17:57 | #73 | Link |
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Code:
convertbits(32) converttoplanarrgb() mlrt_ncnn(network_path="C:\Program Files (x86)\AviSynth+\plugins64\ml_\models\1x-Film-Degrainer-1-000.onnx", fp16=true, builtin=false, tilesize_w=width/4, tilesize_h=height/4, overlap_w=8, overlap_h=8, list_gpu=true) Or is it something else Im missing? |
23rd October 2023, 15:44 | #79 | Link |
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Yeah, I tried delete it and the plugin will faster cause it will use more GPU memory. Try change fp16 to false too. If you meet error, you can change back to true
Last edited by kedautinh12; 24th October 2023 at 00:02. |
23rd October 2023, 21:05 | #80 | Link |
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Using tilesize just means that the image is divided into sections, and because of overlap you end up processing more pixels. I don't think setting fp16 to true will have any negative effects on speed, even on higher end GPUs.
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