lora-training / junko /README.md
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updates Junko README to reflect correct training resolution
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Akashi Junko (Blue Archive)

赤司ジュンコ (ブルーアーカイブ) / 아카시 준코 (블루 아카이브) / 赤司淳子 (碧蓝档案)

Download here.

Table of Contents

Preview

Junko portrait Junko preview 1 Junko preview 2 Junko preview 3

Usage

Use any or all of the following tags to summon Junko: junko, slit pupils, demon horns, halo, twintails, hair ribbon, pointy ears, demon wings

  • You can also use low wings if the wings appear too high.

For her normal outfit: military uniform, short sleeves, black shirt, plaid skirt, red necktie, thigh strap, black boots

For her New Year alt: japanese clothes, yellow kimono, black hakama skirt, black boots, kinchaku

For her normal expression: closed mouth, smile, :3

  • You may need to prefix the colon with a backslash character.

For her hangry expression: open mouth, wavy mouth, skin fang, (tearing up, crying with eyes open:0.5)

  • The AI is very aggressive about drawing tears/crying. You may need to reduce the emphasis.

Training

Exact parameters are provided in the accompanying JSON files.

  • Trained on a set of 140 images.
    • 131 normal images (9 repeats)
    • 9 "multiple views" images (6 repeats)
      • These were reduced because the AI was generating too many "multiple views" images.
    • 3 batch size, 4 epochs
    • (131 * 9 + 9 * 6) / 3 * 4 = 1644 steps
  • 0.0749 loss
  • Initially tagged with WD1.4 swin-v2 model. Tags pruned/edited for consistency.
  • constant_with_warmup scheduler
  • 1.5e-5 text encoder LR
  • 1.5e-4 unet LR
  • 1e-5 optimizer LR
  • Used network_dimension 128 (same as usual) / network alpha 128 (default)
    • Resized to 32 after training
  • Training resolution 768x768.
    • Reduced from 832x832. Junko doesn't really have many very fine details that benefit from the higher resolution, and I think training at 832x832 may negatively impact the quality of images generated at lower resolutions.
  • Trained without VAE.
  • Dataset can be found on the mega.co.nz repository.

Revisions

  • v1c (2023-02-15)
    • Initial release.