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shaoxiongduan 495dd27d33 [feat] WanTrack: golf/synth overfit YAMLs for A/B ablations

Add the training configs used for the golf-overfit and 20-clip synthetic
overfit series exercising every combination of the recent changes:

  golf_nonzero / golf_overfit_nonzero     -- non-zero-init proj ablation
  synth_generic / synth_varied             -- prompt-conditioning ablation
                                              (generic vs per-clip caption)
  synth_varied_d64                         -- id_dim=64 (paper spec)
  synth_varied_d64_nobias                  -- + bias=False on track head
  synth_varied_d64_nobias_fixed            -- + WANTRACK_FIXED_SAMPLE=1
  synth_curriculum                         -- random-sample warmstart from
                                              a fixed-sample checkpoint
  synth_varied_full_val                    -- max_train_steps=1 harness that
                                              regenerates all 20 val clips
                                              in 3 modes from a checkpoint
  synth_sparse_fixed / synth_sparse_random -- 1-per-object + 20 background
                                              extras (Yongqi's recipe)

All resume from the trackwan_1.3b_i2v_control_init family (or the
d64_nobias variant) and enable the new joint text+motion CFG (w_t=3,
w_m=1.5) in track_validation.

2026-07-06 04:11:32 +00:00

configs

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

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

scenario

[feat] WanTrack: golf/synth overfit YAMLs for A/B ablations

2026-07-06 04:11:32 +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.