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### cuDNN Installation for 40/30 Series Optimization* (Optional)
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1. Find the InvokeAI folder
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2. Click on .venv folder - e.g., YourInvokeFolderHere\\.venv
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3. Click on Lib folder - e.g., YourInvokeFolderHere\\.venv\Lib
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4. Click on site-packages folder - e.g., YourInvokeFolderHere\\.venv\Lib\site-packages
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5. Click on Torch directory - e.g., YourInvokeFolderHere\InvokeAI\\.venv\Lib\site-packages\torch
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6. Click on the lib folder - e.g., YourInvokeFolderHere\\.venv\Lib\site-packages\torch\lib
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7. Copy everything inside the folder and save it elsewhere as a backup.
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8. Go to __https://developer.nvidia.com/cudnn__
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9. Login or create an Account.
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10. Choose the newer version of cuDNN. **Note:**
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There are two versions, 11.x or 12.x for the differents architectures(Turing,Maxwell Etc...) of GPUs.
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You can find which version you should download from [this link](https://docs.nvidia.com/deeplearning/cudnn/support-matrix/index.html).
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13. Download the latest version and extract it from the download location
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14. Find the bin folder E\cudnn-windows-x86_64-__Whatever Version__\bin
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15. Copy and paste the .dll files into YourInvokeFolderHere\\.venv\Lib\site-packages\torch\lib **Make sure to copy, and not move the files**
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16. If prompted, replace any existing files
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**Notes:**
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* If no change is seen or any issues are encountered, follow the same steps as above and paste the torch/lib backup folder you made earlier and replace it. If you didn't make a backup, you can also uninstall and reinstall torch through the command line to repair this folder.
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* This optimization is intended for the newer version of graphics card (40/30 series) but results have been seen with older graphics card.
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### Torch Installation
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When installing torch and torchvision manually with `pip`, remember to provide
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