- Add caching mechanism to store VAE-encoded video latents to disk
- Cache latents in a "latent" subdirectory alongside video files
- Skip re-encoding when cached latent file exists
- Add logging for successful cache saves
- Minor code cleanup and formatting improvements
This change improves training efficiency by avoiding redundant video encoding operations.
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
- Add dataset implementations for text-to-video and image-to-video
- Include bucket sampler for efficient batch processing
- Add utility functions for data processing
- Create dataset package structure with proper initialization
