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Enderfga e8516c2fa2 [feat] AnyFlow YAML configs (pretrain + on-policy) for Wan 2.1 T2V 1.3B

Two reference configs slot into examples/train/configs/distribution_matching/wan/:

- anyflow_pretrain_t2v.yaml: single-student pretrain stage. Enables
  r_embedder under pipeline.dit_config, sets diffusion/consistency
  ratios, epsilon=5, weight_type=beta08, fuse_guidance_scale=3.0, lr 5e-5,
  6k steps, Wan2.1 T2V 1.3B init.

- anyflow_onpolicy_t2v.yaml: full DMD2 trio (student + Wan2.1 14B teacher
  + Wan2.1 1.3B critic). Pins the 4-step rollout schedule
  [999, 937, 833, 624, 0] from the AnyFlow paper, use_mean_velocity=true,
  generator_update_interval=5, lr 2e-6, 4k steps. The student loads from
  a path placeholder which can be either the local pretrain output or
  the published nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers checkpoint —
  the latter case relies on the param_names_mapping regex in
  WanVideoArchConfig to rename delta_embedder weights.

2026-07-11 18:36:14 -07:00

configs

[feat] AnyFlow YAML configs (pretrain + on-policy) for Wan 2.1 T2V 1.3B

2026-07-11 18:36:14 -07:00

scenario

[feat] World model training using third person games (#1443 )

2026-07-10 05:09:07 +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.