This commit introduces a knowledge distillation module to enhance logo generation in the CogVideoX-2B text-to-video model.
The key changes include:
- A new `KDTrainer` class that inherits from `CogVideoXT2VLoraTrainer`. This trainer loads a teacher model (OpenLogo Faster R-CNN) and computes a knowledge distillation loss to guide the student model.
- The `kd` training type is now supported, allowing users to select it from the command line.
- New command-line arguments (`teacher_model_path`, `teacher_model_num_classes`, `kd_loss_weight`) have been added to configure the knowledge distillation process.
- A new configuration file (`cogvideox_2b_kd.yaml`) is provided as an example for running a `kd` training session.
When training i2v models without specifying image_column, automatically extract
and use first frames from training videos as conditioning images. This includes:
- Add load_images_from_videos() utility function to extract and cache first frames
- Update BaseI2VDataset to support auto-extraction when image_column is None
- Add validation and warning message in Args schema for i2v without image_column
The first frames are extracted once and cached to avoid repeated video loading.
- Add validation to ensure (frames - 1) is multiple of 8
- Add specific resolution check (480x720) for cogvideox-5b models
- Add error handling for invalid resolution format
- Add field validators for model type and validation settings
- Implement command line argument parsing with argparse
- Add type hints and documentation for training parameters
- Support configuration of model, training, and validation parameters
Add Pydantic models to handle:
- CLI arguments and configuration (Args)
- Model components and pipeline (Components)
- Training state and parameters (State)
