Files

T

Will LinandClaude Fable 5 b65b37f73e [train] run ops: checkpoint start-step gating, LR reset on resume, W&B fallback, weights-only DCP export

- CheckpointConfig.start_step: suppress saves before a given step
- TrainingMethod.apply_configured_lrs(): re-apply YAML LRs after a DCP
  resume restores the checkpoint's optimizer/scheduler LRs
- tracker: if online W&B init fails at boot, retry offline, then run
  trackerless instead of crashing a multi-node job
- dcp_to_diffusers: --weights-only (skip optimizer state that OOMs a
  single-GPU conversion) and robust base-pipeline copytree
- GB200 sbatch: lustre HOME, NCCL/MNNVL env pins, boot watchdog,
  SP/HSDP topology env overrides; plus the 32-GPU test configs and the
  64-prompt held-out H3 validation set
(dataclass/parse plumbing for the new checkpoint fields lands with the
next commit, which touches the same config files)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

2026-08-14 02:07:51 +00:00

consistency_model/wan

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

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

distribution_matching

[train] run ops: checkpoint start-step gating, LR reset on resume, W&B fallback, weights-only DCP export

2026-08-14 02:07:51 +00:00

fine_tuning

[train] MiniMax-H3 config suite: SFT sparsity sweep, data-free DMD2, 32-GPU Slurm launch, README

2026-08-11 04:41:49 +00:00

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] Enable LTX-2 NVFP4 linear and attention QAT fine-tuning (#1626 )

2026-07-26 02:19:34 -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

overfit_ltx2_3_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_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/.