
Resumed from Illustrious XL v0.1 by makkishizu.
Mainly trained on high-rated Danbooru 2024 images, using 40k images on a 4060Ti over 2+8 epochs.
Due to computing limitations, I did not train on an extremely large dataset. The primary goal was to improve the “default art style”(no artists tag) of Illustrious XL while tracking user preferences. Refer to the following comparison sample images:
If you prefer models trained on larger datasets, you can try noobai-xl-nai-xl.
Usage Guidelines
This model is trained using Danbooru-style tags, not natural language! Please use tags only for optimal results.
The usage is basically the same as Illustrious XL, you can adjust the art style using the artist list. If you are interested in the artist list but don't know how to write it, you can try using novelai_300artist
This model supports resolutions from ARB 1024x1024, with a minimum resolution of 256 and a maximum resolution of 2048. While standard SDXL resolution can be used, it is recommended to opt for a slightly higher resolution than 1024x1024. Applying a hires-fix is also suggested for better output quality.
For more details, check the sample images provided.
In addition to following the usage instructions of Illustrious XL:
Recommended sampling method: Euler a, Sampling Steps: 20–28, CFG: 5–7.5 (may vary based on use case).
The model supports quality tags such as: "worst quality," "bad quality," "average quality," "good quality," "best quality," and "masterpiece (quality)."
This model also balances the distribution of rating tags, allowing you to distinguish images by different rating levels:
Rating Modifier Rating Criterion
safe General
sensitive Sensitive
nsfw Questionable
explicit, nsfw Explicit
Recommended prompt format:
<|special|>,
<|characters|>, <|copyrights|>,
<|artist|>,
<|general|>,
<|quality|>, <|meta|>, <|rating|>
Recommended Negative Prompt:
worst quality, comic, multiple views, bad quality, low quality, lowres, displeasing, very displeasing, bad anatomy, bad hands, scan artifacts, monochrome, greyscale, twitter username, jpeg artifacts, 2koma, 4koma, guro, extra digits, fewer digits, jaggy lines, unclear
Training Details:
The dataset for training this model was sourced from hakubooru.
The training of MakkiXL was facilitated by the LyCORIS project and the trainer from lora-scripts.
The original LoKr file is also provided as the "makki_illustrious_lokr" version. For detailed settings, refer to the LyCORIS config file from makki_illustrious_lokr.
Hardware: RTX 4060Ti
Num Train Images: 43,216
Total Epoch: 2+8
Total Steps: 6760
Batch Size: 1
Grad Accumulation Step: 64
Equivalent Batch Size: 64
Optimizer: Lion8bit
Learning Rate: 5e-5 for UNet /NO train TE
LR Scheduler: Constant
Min SNR Gamma: 5
Noise Offset: 0.03
Resolution: 1024x1024
Min Bucket Resolution: 256
Max Bucket Resolution: 2048
Mixed Precision: BF16
License
This model is released under the Fair-AI-Public-License-1.0-SD.
Please check this website for more information:
Freedom of Development freedevproject.org
Contributors' Repositories
Thanks to onommai open source for providing such a powerful base model.
描述:
makki_illustrious_lokr
makki_illustrious_lokr
训练词语:
名称: makkixl_makkiIllustriousLokr.safetensors
大小 (KB): 1252399
类型: Model
Pickle 扫描结果: Success
Pickle 扫描信息: No Pickle imports
病毒扫描结果: Success