About this version
The final version of the UNet-only training converged exceptionally well.
It's sometimes referred to as "178000" and has been quite popular in test qq group.
However, its knowledge base is less extensive than subsequent models.
New Image Generation Model
This is an image generation model based on training from Illustrious-xl.
It utilizes the latest full Danbooru and e621 datasets for training, with native tags caption.
Model Introduction
Model Details
-
Developed by: Laxhar Lab
-
Model Type: Diffusion-based text-to-image generative model
-
Fine-tuned from: Laxhar/noobai-XL-0.5
-
Sponsored by from: Lanyun Cloud
Recommended Settings
Parameters
- CFG: 5 ~ 6
- Steps: 25 ~ 30
- Sampling Method:Euler a
- Resolution: Total area around 1024x1024. Best to choose from: 768x1344, 832x1216, 896x1152, 1024x1024, 1152x896, 1216x832, 1344x768
Prompts
- Prompt Prefix:
masterpiece, best quality, newest, absurdres, highres, safe,
- Negative Prompt:
nsfw, worst quality, old, early, low quality, lowres, signature, username, logo, bad hands, mutated hands, mammal, anthro, furry, ambiguous form, feral, semi-anthro
Usage Guidelines
Caption
<1girl/1boy/1other/...>, <character>, <series>, <artists>, <special tags>, <general tags>, <other tags>
Quality Tags
For quality tags, we evaluated image popularity through the following process:
- Data normalization based on various sources and ratings.
- Application of time-based decay coefficients according to date recency.
- Ranking of images within the entire dataset based on this processing.
Our ultimate goal is to ensure that quality tags effectively track user preferences in recent years.
| Percentile Range | Quality Tags |
|---|---|
| > 95th | masterpiece |
| > 85th, <= 95th | best quality |
| > 60th, <= 85th | good quality |
| > 30th, <= 60th | normal quality |
| <= 30th | worst quality |
Date tags
| Year Range | Period |
|---|---|
| 2005-2010 | old |
| 2011-2014 | early |
| 2014-2017 | mid |
| 2018-2020 | recent |
| 2021-2024 | newest |
Datasets
- Latest Danbooru images up to the training date(for v1.0,it mean approximately before 2024-10-23)
- E621 images e621-2024-webp-4Mpixel dataset on Hugging Face
Communication
-
QQ Groups:
- 875042008
- 914818692
- 635772191
-
Discord: Laxhar Dream Lab SDXL NOOB
Utility Tool
Laxhar Lab is training a dedicated ControlNet model for NoobXL, and the models are being released progressively. So far, the normal, depth, and canny have been released.
Model link: https://civitai.com/models/929685
Model License
This model's license inherits from https://huggingface.co/OnomaAIResearch/Illustrious-xl-early-release-v0 fair-ai-public-license-1.0-sd and adds the following terms. Any use of this model and its variants is bound by this license.
I. Usage Restrictions
- Prohibited use for harmful, malicious, or illegal activities, including but not limited to harassment, threats, and spreading misinformation.
- Prohibited generation of unethical or offensive content.
- Prohibited violation of laws and regulations in the user's jurisdiction.
II. Commercial Prohibition
We prohibit any form of commercialization, including but not limited to monetization or commercial use of the model, derivative models, or model-generated products.
III. Open Source Community
To foster a thriving open-source community,users MUST comply with the following requirements:
- Open source derivative models, merged models, LoRAs, and products based on the above models.
- Share work details such as synthesis formulas, prompts, and workflows.
- Follow the fair-ai-public-license to ensure derivative works remain open source.
IV. Disclaimer
Generated models may produce unexpected or harmful outputs. Users must assume all risks and potential consequences of usage.
Participants and Contributors
Participants
- L_A_X: Civitai | Liblib.art | Huggingface
- li_li: Civitai | Huggingface
- nebulae: Civitai | Huggingface
- Chenkin: Civitai | Huggingface
- Euge: Civitai | Huggingface | Github
Contributors
-
Narugo1992: Thanks to narugo1992 and the deepghs team for open-sourcing various training sets, image processing tools, and models.
-
Onommai: Thanks to OnommAI for open-sourcing a powerful base model.
-
V-Prediction: Thanks to the following individuals for their detailed instructions and experiments.
- adsfssdf
- bluvoll
- bvhari
- catboxanon
- parsee-mizuhashi
- very-aesthetic
- momoura
- madmanfourohfour
-
Community: aria1th261, neggles, sdtana, chewing, irldoggo, reoe, kblueleaf, Yidhar, ageless, 白玲可, Creeper, KaerMorh, 吟游诗人, SeASnAkE, zwh20081, Wenaka~喵, 稀里哗啦, 幸运二副, 昨日の約, 445, EBIX, Sopp, Y_X, Minthybasis, Rakosz