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name: fastvideo description: "Guides FastVideo video, image, and audio generation, config-first inference, serving, training, distillation, evaluation, and performance workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

FastVideo

Use this skill when a task involves the fastvideo Python package, its VideoGenerator API, registered video/image/audio diffusion models, the fastvideo CLI, attention/quantization optimizations, OpenAI-compatible or WebSocket serving, preprocessing, training, distillation, LoRA, checkpoint conversion, or evaluation.

First route

1. Read provenance when checking version alignment or deciding whether a refresh is needed. The structured routing metadata is consumed by managed repo-skill routing and is not a user configuration file.
2. Read troubleshooting before installing, changing backends, or diagnosing a failed run.
3. Choose exactly one focused route:
   • setup-and-configuration for installation, platform/backend selection, model registry, presets, and configuration structure.
   • inference for Python/CLI generation, model inputs, offload, attention, quantization, compile, and output files.
   • serving for HTTP, WebSocket streaming, health checks, continuation state, and server configuration.
   • training-and-data for dataset layouts, preprocessing, modular training, and legacy recipe boundaries.
   • distillation-and-adapters for DMD/self-forcing/QAD, LoRA, and checkpoint conversion decisions.
   • evaluation-and-performance for metrics, benchmarking, quality checks, and performance interpretation.

For a task spanning routes, read the route that owns the executable step first, then follow its sibling link. Do not mix the new modular trainer with the legacy training stack without an explicit reason.

Public install baseline

FastVideo 0.2.0 supports Python 3.10–3.12 in the documented path. For NVIDIA, create an isolated environment and choose the torch backend explicitly:

uv venv --python 3.12 --seed
# Activate the environment created by your shell/platform, then install:
UV_TORCH_BACKEND=cu126 uv pip install fastvideo

Use UV_TORCH_BACKEND=cu130 for a CUDA 13 installation. Apple Silicon uses MPS and the platform-specific install path; do not install Linux-only CUDA extensions there. ARM NVIDIA systems may need an editable source install so the kernel can compile. Read the setup route before selecting an install.

Minimal package and CLI checks:

python -c "import fastvideo; from fastvideo import VideoGenerator, PipelineConfig, SamplingParam; print(fastvideo.__version__)"
fastvideo --version
fastvideo --help

Do not treat package import as proof that a model, GPU kernel, remote checkpoint, or full training workflow is usable. Check the backend, model support, memory, weights access, and optional dependency requirements for the selected route.

Shared operating rules

• Prefer typed GenerationRequest plus VideoGenerator.generate() for new integrations. generate_video() remains a deprecated compatibility path.
• The CLI is config-first: use fastvideo generate --config FILE and dotted --request.* or --generator.* overrides, not flat ad-hoc flags.
• Keep model initialization settings under GeneratorConfig; keep per-request sampling and input settings under GenerationRequest.
• Set attention backend before constructing the generator and reinstantiate after changing it. A backend installed on the machine is not necessarily compatible with every model or GPU.
• Use deterministic seeds, fixed shapes, and a discarded warmup when comparing performance or eager versus compiled execution.
• Treat remote model/data downloads, credentials, multi-GPU jobs, training, distillation, and quality regressions as explicit operations requiring a suitable budget and hardware.

Runtime files

All linked references and helpers are bundled inside this skill. The focused routes own detailed API/configuration material and safe helpers; this root file is intentionally a router rather than a copy of the package manual.