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NoobAI-XL (NAI-XL)

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102k
538k
1.7k
Updated: Dec 8, 2024
base modelanimebasedstyles
Download (6.62 GB)
Verified:
SafeTensor
Type
Checkpoint Trained
Stats
6,741
Reviews
(467)
Published
Dec 8, 2024
Base Model
SDXL 1.0
Hash
AutoV2
466A9208BC
404 Image Contest Participant
L_A_X
License:
CreativeML Open RAIL++-MAddendum

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

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

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

Contributors