Davids048andClaude Opus 5.5
0b80dd52db
These inference parameters could be set only through argparse flags or the untyped pipeline.experimental dict. Each one gains a typed GeneratorConfig field whose flat name is its FastVideoArgs or PipelineConfig field, so YAML, dotted CLI overrides, and from_pretrained keywords all reach it: - engine.attention: backend, vsa_sparsity, vsa_tile_size, moba_config_path - engine.precision: dit, vae, vae_decode, image_encoder, text_encoders - engine.compile.regional (inference_torch_compile) - pipeline: vae_sp, flow_shift, embedded_cfg_scale, dmd_denoising_steps - pipeline.components.lora_target_modules Every field defaults to None, which keeps the runtime or model default; a None field is not passed to FastVideoArgs. pipeline.experimental still accepts the flat names and still wins over the typed fields. The parity inventory lists the typed paths, and the docs show the typed forms. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
FastVideo Documentation
This directory contains the FastVideo documentation built with MkDocs.
Build the docs locally
# Install dependencies
uv pip install -r requirements-mkdocs.txt
# Serve docs with live reload (recommended for development)
mkdocs serve
# Or build static site
mkdocs build
View the docs
Development server (with live reload)
mkdocs serve
Then open your browser to: http://127.0.0.1:8000
Static build
mkdocs build
python -m http.server -d site/
Then open your browser to: http://localhost:8000
Automatic Deployment
Documentation is automatically built and deployed to GitHub Pages when changes are pushed to the main branch via the .github/workflows/infra-docs.yml workflow.
Update documentation dependencies
Edit requirements-mkdocs.in, then regenerate the pinned Linux/Python 3.12 lock file:
uv pip compile requirements-mkdocs.in \
-o requirements-mkdocs.txt \
--python-platform x86_64-manylinux_2_28 \
--python-version 3.12