- Remove redundant comments and debug information
- Adjust default parameters in training scripts
- Clean up code in lora_trainer and trainer implementations
- Add DeepSpeed ZeRO-3 configuration support
- Optimize memory usage during training
- Rename training scripts to reflect ZeRO usage
- Update related configuration files and trainers
- Add SFT (Supervised Fine-Tuning) trainers for all model variants:
- CogVideoX I2V and T2V
- CogVideoX-1.5 I2V and T2V
- Add DeepSpeed ZeRO configuration files:
- ZeRO-2 with and without CPU offload
- ZeRO-3 with and without CPU offload
- Add base accelerate config for distributed training
- Update trainer.py to support SFT training mode
This enables full-parameter fine-tuning with memory-efficient distributed training using DeepSpeed ZeRO optimization.
Before precomputing the latent cache and text embeddings, cast the VAE and
text encoder to the target training dtype (fp16/bf16) instead of keeping them
in fp32. This reduces memory usage during the precomputation phase.
The change occurs in prepare_dataset() where the models are moved to device
and cast to weight_dtype before being used to generate the cache.
- Add pipe.remove_all_hooks() after validation to prevent memory leaks
- Clean up validation pipeline properly to avoid potential issues in subsequent training steps
- 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 and refactor trainers for CogVideoX model variants:
- Implement CogVideoXT2VLoraTrainer for text-to-video generation
- Refactor CogVideoXI2VLoraTrainer for image-to-video generation
Both trainers support LoRA fine-tuning with proper handling of:
- Model components loading and initialization
- Video encoding and batch collation
- Loss computation with noise prediction
- Validation step for generation
- 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
