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

  Civitai & Seaart

• Collaborative testing:

  LiblibAI

  Nieta

---

How to Use the Model.

Guidebook for NoobAI XL:

ENG:

https://civitai.com/articles/8962

CHS:

https://fcnk27d6mpa5.feishu.cn/wiki/S8Z4wy7fSiePNRksiBXcyrUenOh

Method I: reForge

1. (If you haven't installed reForge) Install reForge by following the instructions in the repository;

2. Launch WebUI and use the model as usual!

Method II: ComfyUI

SAMLPLE with NODES

comfy_ui_workflow_sample

Method III: WebUI

Note that dev branch is not stable and may contain bugs.

1. (If you haven't installed WebUI) Install WebUI by following the instructions in the repository. For simp

2.Switch to dev branch:

git switch dev

3. Pull latest updates:

git pull

4. Launch WebUI and use the model as usual!

Method IV: Diffusers

import torch

from diffusers import StableDiffusionXLPipeline

from diffusers import EulerDiscreteScheduler



ckpt_path = "/path/to/model.safetensors"

pipe = StableDiffusionXLPipeline.from_single_file(

    ckpt_path,

    use_safetensors=True,

    torch_dtype=torch.float16,

)

scheduler_args = {"prediction_type": "v_prediction", "rescale_betas_zero_snr": True}

pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, **scheduler_args)

pipe.enable_xformers_memory_efficient_attention()

pipe = pipe.to("cuda")



prompt = """masterpiece, best quality,artist:john_kafka,artist:nixeu,artist:quasarcake, chromatic aberration, film grain, horror \(theme\), limited palette, x-shaped pupils, high contrast, color contrast, cold colors, arlecchino \(genshin impact\), black theme,  gritty, graphite \(medium\)"""

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"



image = pipe(

    prompt=prompt,

    negative_prompt=negative_prompt,

    width=832,

    height=1216,

    num_inference_steps=28,

    guidance_scale=5,

    generator=torch.Generator().manual_seed(42),

).images[0]



image.save("output.png")

Note: Please make sure Git is installed and environment is properly configured on your machine.

---

Recommended Settings

Parameters

• CFG: 4 ~ 5

• Steps: 28 ~ 35

• Sampling Method: Euler (⚠️ Other samplers will not work properly)

• 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 RangeQuality Tags> 95thmasterpiece> 85th, <= 95thbest quality> 60th, <= 85thgood quality> 30th, <= 60thnormal quality<= 30thworst quality

Aesthetic Tags

TagDescriptionvery awaTop 5% of images in terms of aesthetic score by waifu-scorerworst aestheticAll the bottom 5% of images in terms of aesthetic score by waifu-scorer and aesthetic-shadow-v2......

Date Tags

There are two types of date tags: year tags and period tags. For year tags, use year xxxx format, i.e., year 2021. For period tags, please refer to the following table:

Year RangePeriod tag2005-2010old2011-2014early2014-2017mid2018-2020recent2021-2024newest

Dataset

• The latest Danbooru images up to the training date (approximately before 2024-10-23)

• E621 images e621-2024-webp-4Mpixel dataset on Hugging Face

Communication

• QQ Groups:

  • 875042008

  • 914818692

  • 635772191

  • 870086562

• Discord: Laxhar Dream Lab SDXL NOOB

How to train a LoRA on v-pred SDXL model

A tutorial is intended for LoRA trainers based on sd-scripts.

article link: https://civitai.com/articles/8723

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, 孤辰NULL, 汤人烂, 沅月弯刀,David，

描述:

Merry Christmas! NOOBAI XL-VPred 1.0 has been released! The V prediction series has come to a successful end, and what an interesting journey it has been. Who knows, we might have the chance to do this again in the future. With this, our Laxhar Lab's weekly update plan comes to a grand finale!

By the way, here are the advantages of this version:

1. Fine-tuned with high-quality datasets: We have optimized the model for anatomical accuracy and compositional rationality through meticulous adjustments with high-quality datasets.

2. Flexible style combination weights: The model now offers more flexibility in combining different painting styles, with improved robustness when overlaying multiple styles.

3. Enhanced utility of quality words: The effectiveness of quality words has become more pronounced in this version.

4. A blend of features from the standard and S versions: The color style is vibrant yet less prone to overexposure, combining the best of both worlds.

Usage recommendations and future work:

1. Use with dynamic CFG plugin: We recommend using the dynamic CFG & CFG Rescale plugin when employing the V prediction model to prevent oversaturation or overly gray images. You can refer to the configuration of 0.2 for the best results.

2. Choice of sampling methods: Although NOOBAI XL-VPred 1.0 supports most sampling methods, V prediction does not support the Karras series of sampling. Therefore, we suggest using Euler and DDIM sampling methods for more stable outcomes.

3. Ongoing updates and support: We will continue to update VPred 1.0, including the ControlNet model and other plugins. The main model will also be updated irregularly when there are significant improvements (let's see how the DIT effect of NAI4 is and learn from it), so stay tuned!

Lastly, I must share a personal recommendation: the recent game "MiSide" is truly a great game. It has been a long time since a solo game has moved me so much. I strongly recommend it. Wishing you all a Merry Christmas. After a year of hard work, it's time to rest. Until we meet again in this world of possibilitiesミ(・・)ミ

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