v20.7.0

• 2023/02/06 (v20.7.0)
  • --bucket_reso_steps and --bucket_no_upscale options are added to training scripts (fine tuning, DreamBooth, LoRA and Textual Inversion) and prepare_buckets_latents.py.
  • --bucket_reso_steps takes the steps for buckets in aspect ratio bucketing. Default is 64, same as before.
    • Any value greater than or equal to 1 can be specified; 64 is highly recommended and a value divisible by 8 is recommended.
    • If less than 64 is specified, padding will occur within U-Net. The result is unknown.
    • If you specify a value that is not divisible by 8, it will be truncated to divisible by 8 inside VAE, because the size of the latent is 1/8 of the image size.
  • If --bucket_no_upscale option is specified, images smaller than the bucket size will be processed without upscaling.
    • Internally, a bucket smaller than the image size is created (for example, if the image is 300x300 and bucket_reso_steps=64, the bucket is 256x256). The image will be trimmed.
    • Implementation of #130.
    • Images with an area larger than the maximum size specified by --resolution are downsampled to the max bucket size.
  • Now the number of data in each batch is limited to the number of actual images (not duplicated). Because a certain bucket may contain smaller number of actual images, so the batch may contain same (duplicated) images.
  • --random_crop now also works with buckets enabled.
    • Instead of always cropping the center of the image, the image is shifted left, right, up, and down to be used as the training data. This is expected to train to the edges of the image.
    • Implementation of discussion #34.