Files

T

shaoxiongduan 21f25791e2 [feat] WanTrack: MotionStream-aligned track sampling + motion-CFG + Wan-200 cfg

- _augment_tracks: default random 1000-2500 track sampling (was 1-200), contiguous
  chunk masking (was per-frame), diversity weighting (favor moving/unique tracks,
  keep some static), and an object-coverage hook (>=1 track per SAM segment via
  optional object_ids). Sampling ON by default; CFG dropout now default off.
- trackwan_infer.generate: motion classifier-free guidance (guidance_scale) using
  the base no-tracks I2V branch as the unconditional.
- test_motion_cfg.py: quick guidance-scale EPE sweep.
- finetune_wantrack_wan200.yaml: 50->200 data-scale run (same params).

2026-07-02 07:05:14 +00:00

configs

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

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

scenario

[feat] WanTrack: MotionStream-aligned track sampling + motion-CFG + Wan-200 cfg

2026-07-02 07:05:14 +00:00

README.md

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

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

run_slurm.sh

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

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

run.sh

[feat] Add minimal LoRA finetuning support to the YAML training stack (#1242 )

2026-05-17 14:49:13 -07:00

README.md

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.