Davids048andClaude Fable 5.1
0bcd15d5e2
ResolvedGeneratorConfig is the only runtime config. It answers only its
typed fields, pipeline_config, mode, training_mode, and inference_mode;
the transitional flat-name fallback is gone.
Deleted: fastvideo/fastvideo_args.py (FastVideoArgs, TrainingArgs, their
argparse, prepare/set/get_current_fastvideo_args), the flat-name fallback,
generator_config_to_fastvideo_args and the other flattening helpers,
PipelineConfig/model-config/PreprocessConfig/SamplingParam add_cli_args,
configs/utils.py, and the tests of the deleted code.
Materialization builds the model's PipelineConfig directly from the typed
values through an explicit table of typed path to PipelineConfig mirror
attribute (PIPELINE_CONFIG_MIRRORS); PipelineConfig.from_source replaces
from_kwargs. flat_field metadata stays only on the fields behind the 24
from_pretrained keywords; a pipeline.experimental key that names a mirror
attribute, and pipeline.preset / preset_version / components.vae_weights,
are rejected during resolution. The training root keeps the DiT on the
device through a resolution step instead of a from_pretrained override.
The goldens snapshot the end-state objects (resolved config, PipelineConfig,
decisions, request); the cli category is gone. Against the 3f6893a0
goldens, every field matches except disable_autocast, boundary_ratio,
ltx2_vae_tiling (DESIGN section 9), and the fields that no longer exist.
The schema parity inventory classifies the typed GeneratorConfig paths.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
Training
Quick Start
Single-node
bash examples/train/run.sh <config.yaml> [--dotted.key value ...]
The script auto-detects available GPUs. Override with NUM_GPUS:
NUM_GPUS=4 bash examples/train/run.sh \
examples/train/configs/fine_tuning/wan/t2v.yaml
Multi-node (Slurm)
bash examples/train/run_slurm.sh <config.yaml> <num_nodes> [--dotted.key value ...]
bash examples/train/run_slurm.sh \
examples/train/configs/fine_tuning/wan/t2v.yaml 4 \
--training.distributed.hsdp_shard_dim 8 \
--training.distributed.hsdp_replicate_dim 4
Slurm environment variables:
| Variable | Default | Description |
|---|---|---|
PARTITION |
main |
Slurm partition |
NUM_GPUS |
8 |
GPUs per node |
CPUS_PER_TASK |
128 |
CPUs per task |
MEM |
1440G |
Memory per node |
EXCLUDE |
"" |
Nodes to exclude |
Overriding config values
Any config field can be overridden from the command line using dotted keys:
bash examples/train/run.sh examples/train/configs/fine_tuning/wan/t2v.yaml \
--training.optimizer.learning_rate 1e-5 \
--training.loop.max_train_steps 1000 \
--training.checkpoint.output_dir outputs/my_experiment
Resuming from a checkpoint
bash examples/train/run.sh examples/train/configs/fine_tuning/wan/t2v.yaml \
--training.checkpoint.resume_from_checkpoint outputs/my_experiment/checkpoint-500
W&B Logging
Training metrics and validation videos are logged to Weights & Biases. Set your API key before launching:
export WANDB_API_KEY=your_key_here
To disable W&B (e.g. for local debugging):
export WANDB_MODE=offline
The project name and run name are set in the config under training.tracker:
training:
tracker:
project_name: my_project
run_name: my_run
Directory Layout
examples/train/
├── configs/ # Single-step training configs (by method and model)
├── scenario/ # Multi-step end-to-end training pipelines
├── run.sh # Single-node launcher
└── run_slurm.sh # Multi-node Slurm launcher
See configs/README.md and scenario/README.md for details.
The featured QAD Wan2.1 MixKit scenario runs Attn-QAT supervised fine-tuning, checkpoint export, and three-step DMD2. See the Attn-QAT training guide.