- Add caching for prompt embeddings
- Store cached files using safetensors format
- Add cache directory structure under data_root/cache
- Optimize memory usage by moving tensors to CPU after caching
- Add debug logging for cache hits
- Add info logging for cache writes
The caching system helps reduce redundant computation and memory usage during training by:
1. Caching prompt embeddings based on prompt text hash
2. Caching encoded video latents based on video filename
3. Moving tensors to CPU after caching to free GPU memory
- 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 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
