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: 16
  - Gradient Accumulation: 3
  - Warmup steps: 5%
  - Min SNR: 5
  - Epoch: 15
  - Random Cropping: True
  - 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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