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[INFO] Hybrid work with RTX - Printable Version +- Selur's Little Message Board (https://forum.selur.net) +-- Forum: Hybrid - Support (https://forum.selur.net/forum-1.html) +--- Forum: Problems & Questions (https://forum.selur.net/forum-3.html) +--- Thread: [INFO] Hybrid work with RTX (/thread-4163.html) Pages:
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Hybrid work with RTX - Smiggy - 22.07.2025 Hi Selur, I had a question. Will Hybrid work on a RTX Pro 6000 (Blackwell)? Particularly, the upscale model RealESRGAN realsr-anime model and the SCUNet tool? Thank you. RE: Hybrid work with RTX - Selur - 22.07.2025 It should. RE: Hybrid work with RTX - Smiggy - 22.07.2025 Hi, Thank you for your answer. Given the power of the GPU. Will the encoding be faster compared to a 4090 or 5090? RE: Hybrid work with RTX - Selur - 22.07.2025 Not owning a 5090, I can only say that from the specs it should. It has more VRAM (32 vs 24), more CUDA Cores (21760 vs 16384), newer Tensor Cores. RE: Hybrid work with RTX - Smiggy - 22.07.2025 Hello, We tried and got this error message. Please assist. 2025-07-22 14:52:58.031 C:\Users\user\Downloads\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\cuda\__init__.py:287: UserWarning: NVIDIA RTX PRO 6000 Blackwell Workstation Edition with CUDA capability sm_120 is not compatible with the current PyTorch installation. The current PyTorch install supports CUDA capabilities sm_50 sm_60 sm_61 sm_70 sm_75 sm_80 sm_86 sm_90. If you want to use the NVIDIA RTX PRO 6000 Blackwell Workstation Edition GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/ warnings.warn( C:\Users\user\Downloads\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\cuda\__init__.py:287: UserWarning: NVIDIA RTX PRO 6000 Blackwell Workstation Edition with CUDA capability sm_120 is not compatible with the current PyTorch installation. The current PyTorch install supports CUDA capabilities sm_50 sm_60 sm_61 sm_70 sm_75 sm_80 sm_86 sm_90. If you want to use the NVIDIA RTX PRO 6000 Blackwell Workstation Edition GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/ warnings.warn( 2025-07-22 14:52:58.056 Failed to evaluate the script: Python exception: CUDA error: invalid device ordinal CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. Traceback (most recent call last): File "src/cython/vapoursynth.pyx", line 3378, in vapoursynth._vpy_evaluate File "src/cython/vapoursynth.pyx", line 3379, in vapoursynth._vpy_evaluate File "C:\Users\user\AppData\Local\Temp\tempPreviewVapoursynthFile14_52_44_742.vpy", line 38, in clip = SCUNet(clip=clip, model=3, device_index=1) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "contextlib.py", line 81, in inner File "C:\Users\user\Downloads\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\utils\_contextlib.py", line 116, in decorate_context return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\user\Downloads\Hybrid\64bit\Vapoursynth\Lib\site-packages\vsscunet\__init__.py", line 134, in scunet inf_streams = [torch.cuda.Stream(device) for _ in range(num_streams)] ^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\user\Downloads\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\cuda\streams.py", line 39, in new with torch.cuda.device(device): ^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\user\Downloads\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\cuda\__init__.py", line 495, in enter self.prev_idx = torch.cuda._exchange_device(self.idx) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ RuntimeError: CUDA error: invalid device ordinal CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. RE: Hybrid work with RTX - Selur - 23.07.2025 Seems like the pytorch version used does not support the card. ![]() One would probably have to update pytorch which then requires updates of some of the other stuff. RE: Hybrid work with RTX - Smiggy - 23.07.2025 Hi, Where can we update pytorch? RE: Hybrid work with RTX - Selur - 23.07.2025 The portable python environment is under Hybrid/64bit/Vapoursynth, but be warned doing this is not easy. once could try calling: python -m pip install -U torch torchvision torch_tensorrt --index-url https://download.pytorch.org/whl/cu126 --extra-index-url https://pypi.nvidia.comBut no clue how much this could break, one could end up having to setup a custom torch add-on. I outlined the basic step here: Here's how I build Hybrids torch-addon:
# Base setup
* Create an empty Vapoursynth folder.
* Download portable Python (3.12)
https://www.python.org/ftp/python/3.12.9/python-3.12.9-embed-amd64.zip
* Download portable Vapoursynth
https://github.com/vapoursynth/vapoursynth/releases/download/R72/VapourSynth64-Portable-R72.zip
* Extract Python and then Vapoursynth into the Vapoursynth folder
* Change the content of the python312._pth to
```
Scripts
Lib\site-packages
python312.zip
.
# Uncomment to run site.main() automatically
#import site
```
* Install pip
* Download the pip installer
https://bootstrap.pypa.io/get-pip.py and move it into your Vapoursynth folder
* Open a terminal inside the Vapoursynth and call 'python get-pip.py'
* Integrate Vapoursynth to the environment by calling 'python -m pip install wheel/VapourSynth-72-cp312-abi3-win_amd64.whl'
* Install torch
```
python -m pip install -U packaging setuptools Wheel
python -m pip install -U torch torchvision torch_tensorrt --index-url https://download.pytorch.org/whl/cu126 --extra-index-url https://pypi.nvidia.com
```
* Install VSGAN
```
python -m pip install -U vsgan
```
* Install vs-rife by calling:
```
python -m pip install -U vsrife
```
* Install BasicVSR++
```
python -m pip install -U vsbasicvsrpp
```
* Install DPIR
```
python -m pip install -U vsdpir
```
* Install SCUNet
```
python -m pip install -U vsscunet
```
* Install REALEsrgan
```
python -m pip install -U vsrealesrgan
```
* Install HINet
```
python -m pip install -U vshinet
```
* Install AnimeSR
```
python -m pip install -U vsanimesr
```
* Install FeMaSR
```
python -m pip install -U vsfemasr
```
* Install CodeFormer
```
python -m pip install -U vscodeformer
python -m pip install https://github.com/eddiehe99/dlib-whl/releases/download/v20.0.0/dlib-20.0.0-cp312-cp312-win_amd64.whl
```
* Install GRLIR
```
python -m pip install -U vsgrlir
```
* Install MFDIN
```
python -m pip install -U vsmfdin
```
* Install ProPainter
```
python -m pip install https://github.com/dan64/vs-propainter/releases/download/v1.2.1/vspropainter-1.2.1-py3-none-any.whl
```
* Install VSDDColor
```
python -m pip install https://github.com/dan64/vs-deoldify/releases/download/v4.0.0/vsddcolor-1.0.1-py3-none-any.whl
```
* Install VSDDColor
```
python -m pip install -U vsswinir
```
* Install DeOdify
```
python -m pip install -U scikit-image
python -m pip install -U numba
python -m pip install https://github.com/dan64/vs-deoldify/releases/download/v5.0.4/vsdeoldify-5.0.4-py3-none-any.whl
```
* Download:
* https://colorizers.s3.us-east-2.amazonaws.com/colorization_release_v2-9b330a0b.pth
* https://colorizers.s3.us-east-2.amazonaws.com/siggraph17-df00044c.pth
* https://data.deepai.org/deoldify/ColorizeArtistic_gen.pth
* https://www.dropbox.com/s/axsd2g85uyixaho/ColorizeStable_gen.pth?dl=0
* https://data.deepai.org/deoldify/ColorizeVideo_gen.pth
and place them in the Vapoursynth/vsdeoldify/models-folder
* https://github.com/zhangmozhe/Deep-Exemplar-based-Video-Colorization/releases/download/v1.0/colorization_checkpoint.zip
and extract it into the Vapoursynth\Lib\site-packages\vsdeoldify\deepex
* https://download.pytorch.org/models/resnet101-63fe2227.pth
* https://download.pytorch.org/models/resnet50-19c8e357.pth
* https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_pretrain.pth
and place them in the Vapoursynth\Lib\site-packages\vsdeoldify\deepex\checkpoints-folder
* https://github.com/yyang181/colormnet/releases/download/v0.1/DINOv2FeatureV6_LocalAtten_s2_154000.pth
and place it into the Vapoursynth/Lib/site-packages/vsdeoldify/colormnet/weights-folder
* install spartial_correlation_sampler (needs new build, see: https://forum.selur.net/thread-3595-post-26036.html#pid26036)
extract content of spatial_correlation_sampler (open as an archive) into Vapoursynth\Lib\site-packages
* Download Models for the installed tools
```
python -m vsrife
python -m vsdpir
python -m vsscunet
python -m vsrealesrgan
python -m vshinet
python -m vsfemasr
python -m vscodeformer
python -m vsgrlir
python -m vsddcolor
python -m vsswinir
python -m vsbasicvsrpp
```
* Install DeepDeinterlace (**fails due to openmim dependency**)
Download https://github.com/pifroggi/vs_deepdeinterlace/archive/refs/heads/main.zip and place the vs_deepdeinterlace-folder (under vs_deepdeinterlace-main) inside your Vapoursynth/Lib/site-packages folder
```
python -m pip install positional_encodings
```
not supporting DfConvEkSA atm. due to openmin issues.I'll try finding some time, to create such a torch add-on in the next few days. (best remind me if I didn't get around to this on Friday) Cu Selur RE: Hybrid work with RTX - tailland - 23.07.2025 premier support, selur.
RE: Hybrid work with RTX - Selur - 23.07.2025 Won't probably get around to upload a new test torch, but I'm uploading a VapoursynthR72_torch_2025.06.06_torch2.7dev which I didn't build new, but which was a test setup I used in June. Should be up in ~1hour, please test and let me know whether that one works. Cu Selur Ps.: when I remember correctly the problem with torch dev2.7 was that it caused problems with HAVC due to excessive log output,... |