v0.9.0

[bghira]

note: these changes include all of the v0.9 improvements through all RCs, since v0.8.2.

SimpleTuner v0.9.0 Release Notes

I'm excited to announce the release of SimpleTuner v0.9.0! This release includes numerous features, improvements, and bug fixes that enhance the functionality, stability, and performance of SimpleTuner.

An experimental multi-node captioning script is included that was used to create photo-concept-bucket, a free dataset produced by my group that contains roughly 568k CogVLM image-caption pairs, incl other metadata such as dominant colours and aspect ratio.

Below is a summary of the key changes for v0.9.0.

New Features

• Multi-Dataset Sampler: Enhanced support for training with multiple datasets, enabling more flexible and varied data feeding strategies.
• Caption Filter Lists for Dataloaders: Ability to filter captions directly in the text embed layer, improving data quality for training.
• Sine Learning Rate Scheduler: Introduced a sine scheduler to optimize learning rate adjustments, starting training at lr_end instead of learning_rate.
• LoRA Trainer Support: Integration of LoRA (Low-Rank Adaptation) for efficient model training for SDXL and SD 1.5/2.x.
• Advanced Caption Controls: Introduction of the parquet caption strategy, offering more efficient control over caption processing, especially for datasets with millions of images.
• Distributed VAE Encoding/Captioning: Support for distributed VAE encoding and captioning scripts, enhancing performance for large-scale datasets.
• Multi-Stage Resize for Very-Large Images: Improvements in handling very large images through multi-stage resizing, potentially reducing artifacts.
• CSV to S3 Direct Upload: Functionality to upload data directly to S3 from CSV without saving locally first, streamlining data preparation.

Improvements

• VAE Cache: Fixes and enhancements in VAE cache handling, including rebuilds in case of errors or every epoch for 'freshness'.
  • Dataset repeats are now implemented, such that an embed can be seen n times before it will be considered exhausted.
• Text Embedding Cache: Optimizations in text embedding cache generation, writing, and processing for improved performance. Fixes to the threading behaviour.
• CogVLM and Diffusers: Updates including default 4bit inference for CogVLM and bump to Diffusers 0.26.0.
• AWS S3 and SD 2.x Fixes: Various fixes and enhancements for S3 data backends and Stable Diffusion 2.x support, including multi-GPU training and LoRA support fixes.
• Logging Reduction: Major reduction in debug noise for cleaner and more informative logs.

Bug Fixes

• Caption Processing: Fixes for issues related to prepend_instance_prompt doubling up prompt contents and handling of captions from parquet databases.
• Optimizer Adjustments: Various fixes and adjustments for optimizers, including Adafactor and AdamW.
• Training State Handling: Fixes for save/load state issues, ensuring correct handling of global steps, epochs, and more.

Breaking Changes

• instance_data_dir is no longer in use - you must configure a data backend loader. See DATALOADER for more information.
• CogVLM Filename Cleaning: Disabled filename cleaning by default. Projects relying on automatic filename cleaning will need to adjust their preprocessing accordingly.
• Configuration Values: Configuration names and values have changed. Ensure to review your configuration.

Documentation and Miscellaneous

• Documentation Updates: Comprehensive updates to installation guides, tutorials, and README to reflect new features and changes.
• Kohya Config Conversion Script: Provided a script to convert Kohya basic parameters into SimpleTuner command-line arguments, facilitating easier migration and setup.

Full Changelog from v0.8.2: View the complete list of changes

We thank all contributors who have helped shape this release. Your contributions, bug reports, and feedback have been invaluable. Happy tuning!

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This discussion was created from the release v0.9.0.