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Added extra steps to update the Cudnnn DLL found in the Torch packages (#4459)

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I added extra steps to update the Cudnnn DLL found in the Torch package
because it wasn't optimised or didn't use the lastest version. So
manually updating it can speed up iteration but the result might differ
from each card. Exemple i passed from 3 it/s to a steady 20 it/s.

## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] Community Node Submission


## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:

      
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No


## Description


## Related Tickets & Documents

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## QA Instructions, Screenshots, Recordings

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## Added/updated tests?

- [x] Yes
- [ ] No : _please replace this line with details on why tests
      have not been included_

## [optional] Are there any post deployment tasks we need to perform?
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Millun Atluri 2023-09-07 13:38:46 +10:00 committed by GitHub
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docs/installation/030_INSTALL_CUDA_AND_ROCM.md
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@ -57,6 +57,30 @@ familiar with containerization technologies such as Docker.
For downloads and instructions, visit the [NVIDIA CUDA Container
Runtime Site](https://developer.nvidia.com/nvidia-container-runtime)
### cuDNN Installation for 40/30 Series Optimization* (Optional)
1. Find the InvokeAI folder
2. Click on .venv folder - e.g., YourInvokeFolderHere\\.venv
3. Click on Lib folder - e.g., YourInvokeFolderHere\\.venv\Lib
4. Click on site-packages folder - e.g., YourInvokeFolderHere\\.venv\Lib\site-packages
5. Click on Torch directory - e.g., YourInvokeFolderHere\InvokeAI\\.venv\Lib\site-packages\torch
6. Click on the lib folder - e.g., YourInvokeFolderHere\\.venv\Lib\site-packages\torch\lib
7. Copy everything inside the folder and save it elsewhere as a backup.
8. Go to __https://developer.nvidia.com/cudnn__
9. Login or create an Account.
10. Choose the newer version of cuDNN. **Note:**
There are two versions, 11.x or 12.x for the differents architectures(Turing,Maxwell Etc...) of GPUs.
You can find which version you should download from [this link](https://docs.nvidia.com/deeplearning/cudnn/support-matrix/index.html).
13. Download the latest version and extract it from the download location
14. Find the bin folder E\cudnn-windows-x86_64-__Whatever Version__\bin
15. Copy and paste the .dll files into YourInvokeFolderHere\\.venv\Lib\site-packages\torch\lib **Make sure to copy, and not move the files**
16. If prompted, replace any existing files
**Notes:**
* 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.
* This optimization is intended for the newer version of graphics card (40/30 series) but results have been seen with older graphics card.
### Torch Installation
When installing torch and torchvision manually with `pip`, remember to provide