Laxhar/noobai-XL-0.6

NoobAI XL 0.6

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.

• Mikubill: Thanks to Mikubill for the Naifu trainer.

• 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