---
license: other
license_name: faipl
license_link: https://freedevproject.org/faipl-1.0-sd
language:
- en
tags:
- text-to-image
- stable-diffusion
- safetensors
- stable-diffusion-xl
base_model: Minthy/RouWei-0.6
widget:
- text: >-
1girl, green hair, sweater, looking at viewer, upper body, beanie,
outdoors, night, turtleneck, masterpiece, best quality
parameter:
negative_prompt: >-
lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, signature, watermark, username, blurry
example_title: 1girl
---
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.