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https://github.com/zackelia/amd-diffusion
Docker image with AMD support for AUTOMATIC1111/stable-diffusion-webui
https://github.com/zackelia/amd-diffusion
Last synced: about 1 month ago
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Docker image with AMD support for AUTOMATIC1111/stable-diffusion-webui
- Host: GitHub
- URL: https://github.com/zackelia/amd-diffusion
- Owner: zackelia
- License: mit
- Created: 2023-11-22T20:18:18.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-24T02:23:29.000Z (10 months ago)
- Last Synced: 2024-02-24T03:22:52.806Z (10 months ago)
- Language: Dockerfile
- Size: 10.7 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# amd-diffusion
Docker image with AMD support for AUTOMATIC1111/stable-diffusion-webui
## Prerequisites
Install amdgpu driver with ROCm 6.0.2 support. Download at https://www.amd.com/en/support/linux-drivers
Instructions adapted from https://amdgpu-install.readthedocs.io/en/latest/install-installing.html
```
sudo apt install ./amdgpu-install_6.0.60002-1_all.deb
amdgpu-install -y --accept-eula --usecase=rocm
sudo usermod -a -G render $LOGNAME
sudo usermod -a -G video $LOGNAME
```
Reboot for changes to take effect.
## Usage
`pytorch` is currently built for gfx90a, gfx900, gfx906, gfx908, gfx1030, gfx1100. You can check what processor your GPU is by either visiting LLVM's documentation (https://llvm.org/docs/AMDGPUUsage.html#id16) or by running the following command:
```
rocminfo | grep gfx
```
If your processor is not built by `pytorch`, you will need to provide the `HSA_OVERRIDE_GFX_VERSION` environment variable with the closet version. For example, an RX 67XX XT has processor gfx1031 so it should be using gfx1030. To use gfx1030, set `HSA_OVERRIDE_GFX_VERSION=10.3.0` in docker-compose.yml.
For some cards, you will need to provide extra arguments in docker-compose.yml (some of which will boost performance) https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs
> For many AMD GPUs, you must add --precision full --no-half or --upcast-sampling arguments to avoid NaN errors or crashing. If --upcast-sampling works as a fix with your card, you should have 2x speed (fp16) compared to running in full precision.
To run the server, run the following:
```
docker compose up -d
```
Download compatible stable diffusion models (*.safetensors) to the `models/Stable-diffusion` directory.