AkashicPulse v1.0
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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.