Refactor Wan Model Training & Add Wan-VACE Training Support#352
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Refactor Wan Model Training & Add Wan-VACE Training Support#352
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- Ensure `adam_weight_decay` is a float. - Add `tensorboard_dir` parameter for logging. Co-authored-by: martinarroyo <martinarroyo@google.com>
- Conditionally apply dropout only when rate > 0. - Use standard list initialization. - Add rngs parameter to layer_forward (essential for gradient checkpointing with dropout > 0) Co-authored-by: martinarroyo <martinarroyo@google.com>
Replaces the hardcoded learning rate in the optimizer creation with the value from `config.learning_rate`. Co-authored-by: martinarroyo <martinarroyo@google.com>
Co-authored-by: martinarroyo <martinarroyo@google.com>
Co-authored-by: martinarroyo <martinarroyo@google.com>
…lices and different topologies Co-authored-by: martinarroyo <martinarroyo@google.com>
…ling and introduce disable_training_weights, add max_grad_norm and max_abs_grad logging. - Switched timestamp sampling from discrete to continuous. - Add max_grad_norm and max_abs_grad calculation and logging. - Introduced `config.disable_training_weights` to optionally disable mid-point loss weighting. Co-authored-by: martinarroyo <martinarroyo@google.com>
The following key functionalities have been moved from WanTrainer to the new `BaseWanTrainer` ABC: - Initialization and config handling - Scheduler creation - TFLOPs calculation - Core training and evaluation loops (`start_training`, `training_loop`, `eval`) - Abstract methods for checkpointer, data loading, sharding, and step functions. Co-authored-by: martinarroyo <martinarroyo@google.com>
Introduces training support for WAN-VACE models. New files: - train_wan_vace.py: Main training script. - wan_vace_trainer.py: Trainer class for WAN-VACE. - wan_vace_checkpointing_2_1.py: Checkpointing logic for WAN-VACE. Co-authored-by: martinarroyo <martinarroyo@google.com>
entrpn
reviewed
Mar 12, 2026
| # Output directory | ||
| # Create a GCS bucket, e.g. my-maxtext-outputs and set this to "gs://my-maxtext-outputs/" | ||
| base_output_directory: "" | ||
| tensorboard_dir: "" |
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tensorboard_dir is created automatically inside the pyconfig. Is there a reason it needs to be in the config?
Collaborator
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As this is a fairly large refactor:
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This PR introduces several improvements and fixes to the Wan model training, as well as adds support for training Wan-VACE models.
Key changes include:
Bug fixes:
Config updates:
Wan-VACE Support:
New Features: