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.github/ISSUE_TEMPLATE/FEATURE_REQUEST.yml

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name: Feature Request
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description: Commit a idea or Request a new feature
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description: Contribute a idea or request a new feature
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title: '[enhancement]: '
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labels: ['enhancement']
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# assignees:
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- type: markdown
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attributes:
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value: |
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Thanks for taking the time to fill out this Feature request!
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Thanks for taking the time to fill out this feature request!
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- type: checkboxes
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attributes:
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label: Is there an existing issue for this?
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description: |
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Please make use of the [search function](https://github.com/invoke-ai/InvokeAI/labels/enhancement)
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to see if a simmilar issue already exists for the feature you want to request
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to see if a similar issue already exists for the feature you want to request
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options:
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- label: I have searched the existing issues
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required: true
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label: What should this feature add?
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description: Please try to explain the functionality this feature should add
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placeholder: |
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Instead of one huge textfield, it would be nice to have forms for bug-reports, feature-requests, ...
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Instead of one huge text field, it would be nice to have forms for bug-reports, feature-requests, ...
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Great benefits with automatic labeling, assigning and other functionalitys not available in that form
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via old-fashioned markdown-templates. I would also love to see the use of a moderator bot 🤖 like
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https://github.com/marketplace/actions/issue-moderator-with-commands to auto close old issues and other things
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- type: textarea
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attributes:
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label: Aditional Content
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label: Additional Content
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description: Add any other context or screenshots about the feature request here.
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placeholder: This is a Mockup of the design how I imagine it <screenshot>
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placeholder: This is a mockup of the design how I imagine it <screenshot>

docs/installation/030_INSTALL_CUDA_AND_ROCM.md

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For downloads and instructions, visit the [NVIDIA CUDA Container
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Runtime Site](https://developer.nvidia.com/nvidia-container-runtime)
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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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