v2.3.0

[bmaltais]

• 2023/03/22 (v21.3.0)
  • Add a function to load training config with .toml to each training script. Thanks to Linaqruf for this great contribution!
    • Specify .toml file with --config_file. .toml file has key=value entries. Keys are same as command line options. See #241 for details.
    • All sub-sections are combined to a single dictionary (the section names are ignored.)
    • Omitted arguments are the default values for command line arguments.
    • Command line args override the arguments in .toml.
    • With --output_config option, you can output current command line options to the .toml specified with--config_file. Please use as a template.
  • Add --lr_scheduler_type and --lr_scheduler_args arguments for custom LR scheduler to each training script. Thanks to Isotr0py! #271
    • Same as the optimizer.
  • Add sample image generation with weight and no length limit. Thanks to mio2333! #288
    • ( ), (xxxx:1.2) and [ ] can be used.
  • Fix exception on training model in diffusers format with train_network.py Thanks to orenwang! #290
  • Add warning if you are about to overwrite an existing model: Feature Request: Overwrite File Warning #404
  • Add --vae_batch_size for faster latents caching to each training script. This batches VAE calls.
    • Please start with2 or 4 depending on the size of VRAM.
  • Fix a number of training steps with --gradient_accumulation_steps and --max_train_epochs. Thanks to tsukimiya!
  • Extract parser setup to external scripts. Thanks to robertsmieja!
  • Fix an issue without .npz and with --full_path in training.
  • Support extensions with upper cases for images for not Windows environment.
  • Fix resize_lora.py to work with LoRA with dynamic rank (including conv_dim != network_dim). Thanks to toshiaki!
  • Fix issue: Subfolders name issue in Linux pod #406
  • Add device support to LoRA extract.