Aryan Kumar
4b0dc99a73
- Allow the frozen FP32 boundary modules through the FSDP mixed-dtype guard when a model declares replicate_fp32_modules. - Make the tile-64 FP4 kernel and its reference handle a listed block with an empty query half instead of propagating NaN; add a CPU regression test. - Compare generated row ids in the row-plan test. - Consume method checkpoint/stop requests after the pre-loop eval, so best/ always refers to the evaluated weights. - Remove the unused audio-stats module and its claim. - Refresh the V2 model description and drop the dead compression script reference.
Fastvideo New Modular Training Framework
This is the new training infrastructure for FastVideo, replacing the
monolithic per-model pipeline scripts in fastvideo/training/.
Key differences from fastvideo/training/
fastvideo/training/ (legacy) |
fastvideo/train/ (new) |
|
|---|---|---|
| Architecture | One pipeline class per model+method combo | Composable: model plugins + method classes |
| Adding a model | Write a full training pipeline from scratch | Add a model plugin under models/ |
| Adding a method | Fork an existing pipeline | Add a method class under methods/ |
| Config | Flat argparse flags | Hierarchical YAML with dotted-key overrides |
| Distributed | Manual FSDP setup per pipeline | Shared trainer handles FSDP/HSDP |
Structure
fastvideo/train/
├── models/ # Per-model plugins (wan/, hunyuan/, ...)
├── methods/ # Training methods (fine_tuning/, distribution_matching/, ...)
├── callbacks/ # Validation, grad clipping, etc.
├── entrypoint/ # CLI entry points (train.py, dcp_to_diffusers.py)
├── trainer.py # Core training loop
└── utils/ # Config parsing, dataloader, etc.
Usage
See examples/train/ for configs and launch scripts.