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	AMD installation instructions for RDNA3.x+Windows
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# Installation Guide
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This guide covers installation for specific RDNA3 and RDNA3.5 AMD CPUs (APUs) and GPUs
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running under Windows. 
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tl;dr: Radeon RX 7900 GOOD, RX 9700 BAD, RX 6800 BAD. (I know, life isn't fair).
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Currently supported (but not necessary tested):
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**gfx110x**:
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* Radeon RX 7600
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* Radeon RX 7700 XT
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* Radeon RX 7800 XT
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* Radeon RX 7900 GRE
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* Radeon RX 7900 XT
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* Radeon RX 7900 XTX
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**gfx1151**:
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* Ryzen 7000 series APUs (Phoenix)
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* Ryzen Z1 (e.g., handheld devices like the ROG Ally)
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**gfx1201**:
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* Ryzen 8000 series APUs (Strix Point) 
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* A [frame.work](https://frame.work/au/en/desktop) desktop/laptop
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## Requirements
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- Python 3.11 (3.12 might work, 3.10 definately will not!)
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## Installation Environment
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This installation uses PyTorch 2.7.0 because that's what currently available in
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terms of pre-compiled wheels.
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### Installing Python
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Download Python 3.11 from [python.org/downloads/windows](https://www.python.org/downloads/windows/). Hit Ctrl+F and search for "3.11". Dont use this direct link: [https://www.python.org/ftp/python/3.11.9/python-3.11.9-amd64.exe](https://www.python.org/ftp/python/3.11.9/python-3.11.9-amd64.exe) -- that was an IQ test.
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After installing, make sure `python --version` works in your terminal and returns 3.11.x
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If not, you probably need to fix your PATH. Go to:
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* Windows + Pause/Break
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* Advanced System Settings
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* Environment Variables
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* Edit your `Path` under User Variables
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Example correct entries:
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```cmd
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C:\Users\YOURNAME\AppData\Local\Programs\Python\Launcher\
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C:\Users\YOURNAME\AppData\Local\Programs\Python\Python311\Scripts\
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C:\Users\YOURNAME\AppData\Local\Programs\Python\Python311\
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```
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If that doesnt work, scream into a bucket.
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### Installing Git
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Get Git from [git-scm.com/downloads/win](https://git-scm.com/downloads/win). Default install is fine.
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## Install (Windows, using `venv`)
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### Step 1: Download and Set Up Environment
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```cmd
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:: Navigate to your desired install directory
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cd \your-path-to-wan2gp
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:: Clone the repository
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git clone https://github.com/deepbeepmeep/Wan2GP.git
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cd Wan2GP
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:: Create virtual environment using Python 3.10.9
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python -m venv wan2gp-env
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:: Activate the virtual environment
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wan2gp-env\Scripts\activate
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```
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### Step 2: Install PyTorch
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The pre-compiled wheels you need are hosted at [scottt's rocm-TheRock releases](https://github.com/scottt/rocm-TheRock/releases). Find the heading that says:
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**Pytorch wheels for gfx110x, gfx1151, and gfx1201**
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Don't click this link: [https://github.com/scottt/rocm-TheRock/releases/tag/v6.5.0rc-pytorch-gfx110x](https://github.com/scottt/rocm-TheRock/releases/tag/v6.5.0rc-pytorch-gfx110x). It's just here to check if you're skimming.
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Copy the links of the closest binaries to the ones in the example below (adjust if you're not running Python 3.11), then hit enter.
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```cmd
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pip install ^
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    https://github.com/scottt/rocm-TheRock/releases/download/v6.5.0rc-pytorch-gfx110x/torch-2.7.0a0+rocm_git3f903c3-cp311-cp311-win_amd64.whl ^
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    https://github.com/scottt/rocm-TheRock/releases/download/v6.5.0rc-pytorch-gfx110x/torchaudio-2.7.0a0+52638ef-cp311-cp311-win_amd64.whl ^
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    https://github.com/scottt/rocm-TheRock/releases/download/v6.5.0rc-pytorch-gfx110x/torchvision-0.22.0+9eb57cd-cp311-cp311-win_amd64.whl
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```
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### Step 3: Install Dependencies
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```cmd
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:: Install core dependencies
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pip install -r requirements.txt
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```
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## Attention Modes
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WanGP supports several attention implementations, only one of which will work for you:
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- **SDPA** (default): Available by default with PyTorch.  This uses the built-in aotriton accel library, so is actually pretty fast.
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## Performance Profiles
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Choose a profile based on your hardware:
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- **Profile 3 (LowRAM_HighVRAM)**: Loads entire model in VRAM, requires 24GB VRAM for 8-bit quantized 14B model
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- **Profile 4 (LowRAM_LowVRAM)**: Default, loads model parts as needed, slower but lower VRAM requirement
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## Running Wan2GP
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In future, you will have to do this:
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```cmd
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cd \path-to\wan2gp
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wan2gp\Scripts\activate.bat
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python wgp.py
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```
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For now, you should just be able to type `python wgp.py` (because you're already in the virtual environment)
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## Troubleshooting
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- If you use a HIGH VRAM mode, don't be a fool.  Make sure you use VAE Tiled Decoding.
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### Memory Issues
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- Use lower resolution or shorter videos
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- Enable quantization (default)
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- Use Profile 4 for lower VRAM usage
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- Consider using 1.3B models instead of 14B models
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For more troubleshooting, see [TROUBLESHOOTING.md](TROUBLESHOOTING.md) 
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