nodes/hao-ai-lab-FastVideo

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d6dfe95466 [feat] FastVideo World Model Training (#1179 )

Co-authored-by: mignonjia <mhuo@ucsd.edu>
Co-authored-by: alexzms <3036648523@qq.com>
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>

2026-05-11 05:13:12 +00:00

distribution_matching/wan

[misc]: reorganize training configs and add documentation (#1177 )

2026-03-26 16:38:31 -07:00

fine_tuning

[feat] FastVideo World Model Training (#1179 )

2026-05-11 05:13:12 +00:00

knowledge_distillation/wan

[misc]: reorganize training configs and add documentation (#1177 )

2026-03-26 16:38:31 -07:00

example.yaml

[refactor] train v1 clean up

2026-03-09 18:58:56 -07:00

overfit_cosmos25_t2w.yaml

[feat] Cosmos 2.5 training support in fastvideo.train (#1224 )

2026-05-01 01:15:02 +00:00

overfit_hunyuan_t2v.yaml

[bugfix]: fix VAE temporal tiling blend corruption in tiled_encode (#1181 )

2026-03-29 23:12:42 +00:00

README.md

[misc]: reorganize training configs and add documentation (#1177 )

2026-03-26 16:38:31 -07:00

README.md

Training Configs

Single-step training configurations organized by method and model.

configs/
├── fine_tuning/          # Standard finetuning and DFSFT
├── distribution_matching/  # DMD2 and Self-Forcing
├── knowledge_distillation/ # KD from teacher to student
└── example.yaml          # Annotated reference config with all fields

Each method directory contains per-model subdirectories (e.g. wan/, hunyuan/).

Launch any config with:

bash examples/train/run.sh examples/train/configs/<method>/<model>/<config>.yaml

For multi-step training pipelines, see examples/train/scenario/.