Davids048andClaude Opus 5.5
00a8e5bcb6
- moduleloader builds the component-loading config and the inference
config as resolved configs (_build_training_resolved_config and
build_inference_resolved_config) instead of TrainingArgs objects mutated
after construction; the transformer override, weights path, and
teacher/critic flag are config values or load_module arguments.
keep_checkpoint_component_config keeps the trainer's PipelineConfig view
of the checkpoint-filled VAE and text-encoder configs.
- The validation callback builds its pipeline with
from_pretrained(path, resolved_config=...) and forwards with a resolved
config built per run; it no longer replaces or mutates the trainer's
pipeline_config, and generator_overrides takes nested GeneratorConfig
fields instead of flat names.
- Against the 3f6893a0 snapshots of the 67 trainer YAML files, every
PipelineConfig and value matches except ltx2_vae_tiling (the model's
vae_tiling) and the unread pretrained_model_name_or_path.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
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/.