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Dependencies

Installation

Linux / macOS (Recommended)

bash ./install.sh
source ./activate

Windows

.\install.ps1
.\venv\scripts\activate

The install scripts handle everything: Python version detection (3.10-3.14), virtual environment creation, PyTorch, diffusers, and all dependencies.

Manual Installation

If you prefer manual control:

python3 -m venv venv
source venv/bin/activate

pip install torch torchvision torchaudio  # add --index-url https://download.pytorch.org/whl/cu130 for CUDA on Linux
pip install -r requirements.txt
pip install git+https://github.com/huggingface/diffusers

Where the Dependency List Lives

pyproject.toml is the single source: its dependencies list (plus the server and dev extras) is what the published wheel declares, what requirements.txt and requirements-test.txt resolve (they are one-line pointers, -e .[server] and -e .[dev]), and what the install scripts and CI install. To add or change a dependency, edit pyproject.toml - every install path picks it up.

Things pyproject can't express stay in the scripts: the PyTorch CUDA index, diffusers from git HEAD, and the macOS/Windows-specific extras below. bitsandbytes carries a sys_platform == 'linux' marker in pyproject; install.ps1 installs it explicitly on Windows.

Platform-Specific Dependencies

All platforms (core ML): peft, transformers, accelerate, safetensors, controlnet_aux, sentencepiece, torchsde, torchao, optimum-quanto, gguf, kornia, ftfy, sdnq, spandrel, facexlib (spandrel + facexlib back the upscale and restore_faces tasks)

All platforms (utilities): fastapi, uvicorn (the dw.serve HTTP server and web UI), av, aiohttp, matplotlib, opencv-python-headless, concurrent-log-handler, qrcode, protobuf, imageio, imageio-ffmpeg, beautifulsoup4, soundfile, jsonschema, black, python-dotenv

Linux (CUDA): bitsandbytes

Windows (CUDA): bitsandbytes, kernels

macOS (MPS): fp4-fp8-for-torch-mps (FP8/FP4 dtype support for Metal), fluidtop

Optional

flash_attn — Improved attention performance on CUDA. Requires the CUDA Toolkit:

pip install flash_attn

piexif (installed by default) — Embeds generation metadata as EXIF UserComment when a step's result.embed_metadata is true and the content type is JPEG/WebP (PNG embedding uses Pillow's PngInfo and needs nothing extra). Without it, saving falls back to a logged warning and no embedded metadata.

Specifying Python Version

export INSTALL_PYTHON_VERSION=3.13
bash ./install.sh

Troubleshooting

Hugging Face authentication (401/403 downloading a model): Most workflows under workflows/ (Flux, LTX-2, MiniMax...) point at gated models on the Hub — repos that require the owner to approve your account before you can download them. A run against one of these fails with an actionable error naming the repo and huggingface-cli login (mapped from the Hub's 401/403 in load_component(), dw/pipeline_processors/pipeline.py) — request access on the model's page (e.g. black-forest-labs/FLUX.1-dev) and then log in once, locally:

huggingface-cli login

workflows/templates/text-to-image.json uses an ungated model and needs no login - start there if you just want to confirm the install works.

Package conflicts: Re-run the install script — it recreates the venv from scratch.

CUDA not detected: install.sh probes for a working nvidia-smi on Linux and installs the CUDA build of torch (cu130) only when it finds one; otherwise (or on a manual install with plain pip install torch) you get PyPI's CPU-only Linux wheel. Verify with python -c "import torch; print(torch.cuda.is_available())".

MPS not detected: Requires Apple Silicon. Verify with python -c "import torch; print(torch.backends.mps.is_available())".