Files

T

Aryan Kumar fed3ce18b6 [fix]: align the QAD configs and docs with the released provenance

- V2 config now matches the canonical 16-GPU stage 1: warmup 10,
  min LR ratio 0.3, HSDP 16.
- State that the published V2.1 checkpoint is linears-only (BF16 VSA
  attention); the FP4 adaptation is a separate, non-public stage.
- Name the released checkpoints and starting models.
- Vendor and pin both calibrated amax tables.
- Add the V2 prompt/embedding and OmniRef eval data-prep scripts.

2026-10-19 12:38:43 -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

[perf]: QAT/QAD add safe regional compile for modular training (#1718 )

2026-10-07 05:24:24 -07:00

fine_tuning

[misc] Remove DreamX World, HunyuanGameCraft, GLM-Image, LongCat, TurboDiffusion, and Z-Image (#1926 )

2026-10-14 23:16:32 -07:00

knowledge_distillation

[fix]: align the QAD configs and docs with the released provenance

2026-10-19 12:38:43 -07:00

rl/wan

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

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

example.yaml

[feat] Add end-to-end MMAudio V2A training, inference, and evaluation (#1648 )

2026-10-07 04:25:00 -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_ref2va_lora.yaml

[feat]: add MiniMax H3 Ref2VA and LoRA training support (#1757 )

2026-10-07 03:21:45 -07:00

overfit_minimax_h3_ref2va.yaml

[feat]: add MiniMax H3 Ref2VA and LoRA training support (#1757 )

2026-10-07 03:21:45 -07:00

overfit_minimax_h3_t2va_lora.yaml

[feat]: add MiniMax H3 Ref2VA and LoRA training support (#1757 )

2026-10-07 03:21:45 -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/.