- Replace bucket-based dataset with simpler resize-based implementation
- Add video latent precomputation during dataset initialization
- Improve code readability and user experience
- Remove complexity of bucket sampling for better maintainability
This change makes the codebase more straightforward and easier to use while
maintaining functionality through resize-based video processing.
Add validation capabilities to the Trainer class including:
- Support for validating images and videos during training
- Periodic validation based on validation_steps parameter
- Artifact logging to wandb for validation results
- Memory tracking during validation process
- Add Trainer base class with core training loop functionality
- Implement distributed training setup with Accelerate
- Add training script with model/trainer initialization
- Support LoRA fine-tuning with checkpointing and validation
