linux_install.md

Developer Installation (Linux)

This section provides instructions for installing the app (CLI and GUI) from source on Linux.

Note

This is for Linux. If you're on Windows (and don't want to use WSL), follow the Windows Installation.

Flatpak and Docker Images are available here

Install CLI

1. Install system dependencies
   • uv
   • FFmpeg >= 4.4
   • git

Tip

Arch Linux: sudo pacman -Syu uv ffmpeg git

Ubuntu 25.04: sudo apt install ffmpeg git. uv is not yet available in Ubuntu/Debian repositories, see uv | Getting Started for alternative installation methods

Ubuntu 24.04: sudo apt install ffmpeg git. uv is not yet available in Ubuntu/Debian repositories, see uv | Getting Started for alternative installation methods

Tip

If you have an Intel GPU and want to use QSV hardware video encoding you'll need to install Intel VPL GPU Runtime.

Arch Linux: sudo pacman -S vpl-gpu-rt

Ubuntu: sudo apt install libmfx-gen1.2

2. Get the source code

   git clone https://codeberg.org/ladaapp/lada.git
   cd lada

3. Create a virtual environment to install python dependencies

   uv venv
   source .venv/bin/activate

4. Install Python dependencies

   Extra	Supported GPU architectures	Graphics Driver / Prerequisites	Notes
   nvida-legacy	Nvidia Maxwell(5.0), Pascal(6.0), Volta(7.0), Turing(7.5), Ampere(8.0, 8.6), Hopper(9.0)	Nvidia GPU driver version >= 560	For RTX 10xx
   nvidia	Nvidia Volta(7.0), Turing(7.5), Ampere(8.0, 8.6), Hopper(9.0), Blackwell(10.0, 12.0)	Nvidia GPU driver version >= 570	For RTX 16xx, RTX 20xx up to including RTX 50xx
   intel	Intel Discrete Arc GPUs: A-series (Alchemist), B-series (Battlemage) Intel Integrated Arc GPUs of Core Ultra Processors: Meteor Lake-H, Arrow Lake-H, Lunar Lake	Ubuntu: Intel GPU Driver Installation docs Arch Linux: sudo pacman -Syu intel-compute-runtime level-zero-loader
   cpu	-		Running Lada on CPU will be so slow that it's not really practical

   Based on your hardware, select the appropriate extra from the table above and install it with uv.

   You have to choose a single option in case your system contains GPUs of multiple vendors like an integrated Intel GPU and a dedicated Nvidia GPU.

   uv sync --extra nvidia # Adjust extra to your available hardware

   Before continuing let's test if the installation was successful by checking if PyTorch detects your GPU (skip if using CPU):

   # Nvidia
   uv run --no-project python -c "import torch ; print(torch.cuda.is_available())"
   # Intel
   uv run --no-project python -c "import torch ; print(torch.xpu.is_available())"

   If this prints True then you're good. If False, check your GPU drivers are up-to-date and ensure you've selected the right extra for your hardware.

5. Apply patches

   patch -u -p1 -d .venv/lib/python3.13/site-packages < patches/increase_mms_time_limit.patch
   patch -u -p1 -d .venv/lib/python3.13/site-packages < patches/remove_ultralytics_telemetry.patch
   patch -u -p1 -d .venv/lib/python3.13/site-packages < patches/fix_loading_mmengine_weights_on_torch26_and_higher.diff

6. Download model weights

   Download the necessary model weights from HuggingFace

   wget 'https://huggingface.co/ladaapp/lada/resolve/main/lada_mosaic_detection_model_v2.pt?download=true' -O model_weights/lada_mosaic_detection_model_v2.pt
   wget 'https://huggingface.co/ladaapp/lada/resolve/main/lada_mosaic_detection_model_v4_accurate.pt?download=true' -O model_weights/lada_mosaic_detection_model_v4_accurate.pt
   wget 'https://huggingface.co/ladaapp/lada/resolve/main/lada_mosaic_detection_model_v4_fast.pt?download=true' -O model_weights/lada_mosaic_detection_model_v4_fast.pt
   wget 'https://huggingface.co/ladaapp/lada/resolve/main/lada_mosaic_restoration_model_generic_v1.2.pth?download=true' -O model_weights/lada_mosaic_restoration_model_generic_v1.2.pth

   If you're interested in running DeepMosaics' restoration model you can also download their pretrained model clean_youknow_video.pth

   wget 'https://drive.usercontent.google.com/download?id=1ulct4RhRxQp1v5xwEmUH7xz7AK42Oqlw&export=download&confirm=t' -O model_weights/3rd_party/clean_youknow_video.pth

You can now run the CLI with lada-cli.

Tip

Remember: To start Lada ensure you:

• cd into the project root directory
• Activate the virtual environment with .\.venv\Scripts\Activate.ps1
• Run the CLI with lada-cli

Install GUI

1. Install the CLI as per instructions above

2. Install system dependencies

   • Gstreamer >= 1.14
   • PyGObject
   • GTK >= 4.0
   • libadwaita >= 1.6 [there is a workaround mentioned below to make it work with older versions]

Tip

Arch Linux:

sudo pacman -Syu python-gobject gtk4 libadwaita gstreamer gst-plugins-base gst-plugins-good gst-plugins-bad gst-plugins-ugly gst-plugins-base-libs gst-plugins-bad-libs gst-plugin-gtk4

Ubuntu 25.04:

sudo apt install gcc python3-dev pkg-config libgirepository-2.0-dev libcairo2-dev libadwaita-1-dev gir1.2-gstreamer-1.0
sudo apt install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-pulseaudio gstreamer1.0-alsa gstreamer1.0-tools gstreamer1.0-libav gstreamer1.0-gtk4

Ubuntu 24.04:

sudo apt install gcc python3-dev pkg-config libgirepository-2.0-dev libcairo2-dev libadwaita-1-dev gir1.2-gstreamer-1.0
sudo apt install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-pulseaudio gstreamer1.0-alsa gstreamer1.0-tools gstreamer1.0-libav
#
####### Gstreamer #######
# The gstreamer plugin gtk4 is not available as a binary package in Ubuntu 24.04 so we have to build it ourselves:
# Get the source code
git clone https://gitlab.freedesktop.org/gstreamer/gst-plugins-rs.git
cd gst-plugins-rs
# Install dependencies necessary to build the plugin
sudo apt  install rustup libssl-dev
rustup default stable
cargo install cargo-c
# Now we can build and install the plugin. Note that we're installing to the system directory, you might want to adjust this to another directory and set set the environment variable GST_PLUGIN_PATH accordingly
cargo cbuild -p gst-plugin-gtk4 --libdir /usr/lib/x86_64-linux-gnu
sudo -E cargo cinstall -p gst-plugin-gtk4 --libdir /usr/lib/x86_64-linux-gnu
# If the following command does not return an error the plugin is correctly installed
gst-inspect-1.0 gtk4paintablesink
#
####### libadwaita #######
# The version of libadwaita in Ubuntu 24.04 repositories is too old. Instead of building the new version the following patch will adjust the code so it's compatible with the version provided by Ubuntu 24.04:
patch -u -p1 -i patches/adw_spinner_to_gtk_spinner.patch
> ```

3. Install python dependencies

   uv pip install -e '.[gui]'

Tip

If you intend to hack on the GUI code install also the gui-dev group: uv pip install --group gui-dev.

You can now run the GUI with lada.

Tip

Remember: To start Lada ensure you:

• cd into the project root directory
• Activate the virtual environment with .\.venv\Scripts\Activate.ps1
• Run the GUI with lada

Install Translations (optional)

If you prefer the app in a language other than English, you can use translation files if available.

1. Install system dependencies

Tip

Arch Linux: sudo pacman -Syu gettext

Ubuntu: sudo apt install gettext

2. Compile translations

   bash translations/compile_po.sh

The app should now use the translations and be shown in your system language. If not then you may need to set the environment variable LANG (or LANGAUGE) to your preferred language e.g. export LANGUAGE="zh_TW".