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noobai-XL-Vpred-0.5

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Diffusers
Safetensors
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metadata
license: other
license_name: fair-ai-public-license-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
  - en
base_model:
  - Laxhar/noobai-XL_v1.0
pipeline_tag: text-to-image
tags:
  - Diffusers
  - Safetensors

Model Introduction

This image generation model, based on Laxhar/noobai-XL_v1.0, leverages full Danbooru and e621 datasets with native tags and natural language captioning.

Implemented as a v-prediction model (distinct from eps-prediction), it requires specific parameter configurations - detailed in following sections.

Special thanks to my teammate euge for the coding work, and we're grateful for the technical support from many helpful community members.

⚠️ IMPORTANT NOTICE ⚠️

THIS MODEL WORKS DIFFERENT FROM EPS MODELS!

PLEASE READ THE GUIDE CAREFULLY!

Model Details

  • Developed by: Laxhar Lab

  • Model Type: Diffusion-based text-to-image generative model

  • Fine-tuned from: Laxhar/noobai-XL_v1.0

  • Sponsored by from: Lanyun Cloud

How to Use the Model.

Steps

  1. Clone the repository
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui
  1. Switch to dev branch
git switch dev
  1. Pull latest updates
git pull
  1. Use normally)
    • Follow standard procedures to launch and use the model

Note: Make sure Git is installed and environment is properly configured

Recommended Settings

Parameters

(For vpred model, recommend using low cfg and more Steps)

  • CFG: 4 ~ 5
  • Steps: 28 ~ 35
  • Sampling Method:Euler a
  • Resolution:aim for around 1024*1024

Prompts

  • Prompt:
masterpiece, best quality, newest, absurdres, highres, safe,
  • Negative Prompt:
 nsfw,worst quality,old,early,low quality,quality,lowres,signature,username,bad id,bad twitter id,english commentary,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(approximately before 2024-10-23)
  • E621 images e621-2024-webp-4Mpixel dataset on Hugging Face

Communication

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

For the open source community, you need to:

  • 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

Contributors