Noobai XL Vpred 0.75s

🚀 NoobAI XL V-Pred 0.75s

This image generation model is based on Laxhar/noobai-XL_v1.0, leveraging full Danbooru and e621 datasets for high - quality image outputs.

🚀 Quick Start

This diffusion - based text - to - image generative model has specific usage methods and requirements. Please follow the steps below to use the model.

✨ Features

• Based on Laxhar/noobai - XL_v1.0, it uses full Danbooru and e621 datasets with native tags and natural language captioning.
• Implemented as a v - prediction model, different from eps - prediction models.
• Special parameter configurations are required.

📦 Installation

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 the 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.
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")

⚠️ Important Note

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

💻 Usage Examples

Basic Usage

The above installation method IV provides a basic usage example. You can run the code to generate images.

Advanced Usage

You can adjust the parameters such as prompt, negative_prompt, width, height, num_inference_steps, and guidance_scale according to your needs to generate different images.

📚 Documentation

Model Details

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.

Aesthetic Tags

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:

Dataset

• The latest Danbooru images up to the training date (approximately before 2024 - 10 - 23)
• E621 images [e621 - 2024 - webp - 4Mpixel](https://huggingface.co/datasets/NebulaeWis/e621 - 2024 - webp - 4Mpixel) dataset on Hugging Face

Communication

• QQ Groups:
  • 875042008
  • 914818692
  • 635772191
• 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

📄 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](https://huggingface.co/parsee - mizuhashi)
  • [very - aesthetic](https://github.com/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