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fb5efebdab [feat] Add HunyuanVideo 1.5 T2V training support (#1662 )

Co-authored-by: William Lin <willlin@nvidia.com>
Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>
Co-authored-by: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

2026-10-07 02:19:22 -07:00

consistency_model/wan

[feat] Add Clean-history Teacher Forcing and Causal Consistency Distillation (#1505 )

2026-07-07 08:13:11 +00:00

distribution_matching

[feat] Add Kandinsky5 QAD training pipeline: data preprocessing, QAT finetune, QAT-aware DMD distillation (#1601 )

2026-07-26 02:59:21 -07:00

fine_tuning

[feat] Add HunyuanVideo 1.5 T2V training support (#1662 )

2026-10-07 02:19:22 -07:00

knowledge_distillation/wan

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

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

rl/wan

[feat] Add Wan RL DiffusionNFT training (#1450 )

2026-06-11 21:18:59 -07:00

example.yaml

[feat] Add phase-aware modular training performance metrics (#1826 )

2026-10-07 01:30:11 -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_hunyuan15_t2v.yaml

[feat] Add HunyuanVideo 1.5 T2V training support (#1662 )

2026-10-07 02:19:22 -07:00

overfit_hunyuan_t2v.yaml

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

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

overfit_ltx2_3_t2v.yaml

[misc] Move environment var reads into env registry and rename unprefixed variables (#1897 )

2026-09-29 14:36:16 -07:00

overfit_ltx2_t2v_nvfp4_qat.yaml

[feat]: FA4-FP4 ATTN_QAT_INFER on sm_100/sm_103 + NVFP4 weight purge (#1647 )

2026-07-27 15:46:52 -07:00

overfit_ltx2_t2v.yaml

[feat] Add LTX-2 and LTX-2.3 fine-tuning to the modular trainer (#1624 )

2026-07-20 11:58:39 -07:00

overfit_minimax_h3_t2va.yaml

[feat] add Minimax H3 sft pipeline (#1688 )

2026-08-07 16:12:04 -07:00

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/.