AkashicPulse v1.0

AkashicPulse is a finetune based on RouWei, an Illustrious-based model.

The model has gone through 1 step of merging, and 3 steps of finetuning to make sure the model able to give stunning results, superior from the competitions.

Recommended settings:

  • Sampling: Euler a

  • Steps: 20-30, the sweet spot is 28.

  • CFG: 4-10, the sweet spot is 7.

  • [Not mandatory] On reForge or ComfyUI, have MaHiRo CFG enabled.

Recommended prompting format:

  • Prompt: [1girl/1boy], [character name], [series], by [artist name], [the rest of the prompt], masterpiece, best quality

  • Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, signature, watermark, username, blurry, [the rest of the negative prompt]

Training Process:

  • Step 1:

    • Giving RouWei a CyberFix treatment.
  • Step 2:

    • Training new concept

      • Dataset size: ~10.000 images

      • GPU: 2xA100 80GB

      • Optimizer: AdaFactor

      • Unet Learning Rate: 7.5e-6

      • Text Encoder Learning Rate: 3.75e-6

      • Batch Size: 16

      • Gradient Accumulation: 3

      • Warmup steps: 2 * 100 steps

      • Min SNR: 5

      • Epoch: 10

      • Random Cropping: True

      • Loss: Huber

      • Huber Schedule: SNR

  • Step 3:

    • Finetuning I

      • Dataset size: ~4.500 images

      • GPU: 1xA100 80GB

      • Optimizer: AdaFactor

      • Unet Learning Rate: 3e-6

      • Text Encoder Learning Rate: N/A

      • Batch Size: 16

      • Gradient Accumulation: 3

      • Warmup steps: 5%

      • Min SNR: 5

      • Epoch: 15

      • Random Cropping: True

      • Loss: Huber

      • Huber Schedule: SNR

      • Multires Noise Iteration: 8

  • Step 4:

    • Finetuning II

      • Dataset size: ~4.500 images

      • GPU: 1xA100 80GB

      • Optimizer: AdaFactor

      • Unet Learning Rate: 3e-6

      • Text Encoder Learning Rate: N/A

      • Batch Size: 48

      • Gradient Accumulation: 1

      • Warmup steps: 5%

      • Min SNR: 5

      • Epoch: 15

      • Loss: L2

      • Noise Offset: 0.0357

Added series:

  • DanDaDan

The model falls under Fair AI Public License 1.0-SD with no additional terms.

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