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:
Collaborative testing:
How to Use the Model.
Guidebook for NoobAI XL:
ENG:
https://civarchive.com/articles/8962
CHS:
https://fcnk27d6mpa5.feishu.cn/wiki/S8Z4wy7fSiePNRksiBXcyrUenOh
Recommended LoRa List for NoobAI XL:
https://fcnk27d6mpa5.feishu.cn/wiki/IBVGwvVGViazLYkMgVEcvbklnge
Method I: reForge
(If you haven't installed reForge) Install reForge by following the instructions in the repository;
Launch WebUI and use the model as usual!
Method II: ComfyUI
SAMLPLE with NODES
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:
427280545
677964513
852429527
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://civarchive.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://civarchive.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.
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
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, 年糕特工队,
Description
A new version of traditional-para training that supports concepts around img count 300 (styles and chars)
This is still an intermediate version and can be run using the traditional A41 Webui
For the convenience of users with different needs, Laxhar Lab will release a V-pred version and an epsilon-pred version
The model is in the middle stage of learning, and we will continue to optimize the model by responding to comments and suggestions from the community, and we ask for your suggestions, and we will try our best to implement themミ(・・)ミ
We hope you like this 50% version lol
FAQ
Comments (86)
Can you upload the latest checkpoint into civitai generator?
>For the convenience of users with different needs, Laxhar Lab will release
>an epsilon-pred version
huh?
These models are really nice. Personally I would like to see a full v-pred finetune as the dynamic range it offers is the closest we have to NAIv3, but even the traditional eps-pred is fun to use. Good job on the models.
There is already no reason to use NAI3.
Basically good, but it seems very easy to generate signature and watermark when you use some artist style, ignoring negative prompts. Looking forward to future updates, good job anyway.
A nice improvement over the pre-mature test release although it's still a bit weak in some styles that have over 300+ hits on danbooru like itou yuuji, noya makoto, kurogaraso, etc. (rather, these also seem to jump from style to style.) Regardless of that, this is the best anime model I've had so much fun with and used so far, really looking forward to the full release, especially the vpred one.
It's incredible.
0.5版本提示服从性已经完爆nai3了,强得让人怀疑nai3的200块H100是不是用去挖矿了
For anyone using ADetailer on a1111 or forge for vpred version: go to settings > adetailer and append ",advanced_model_sampling_script" in "script names to apply to adetailer"
how do I do this? I'm in forge and not sure how, do I just paste it in?
@stygianwizard42 yes
This finetune is amazing but please, please, PLEASE prioritize the vpred+stsnr version. The results are amazing even for such an early epoch, I believe it will be the best of the best once it's had time to converge.
虽然搞比较可能会降低开发者的积极性,但实际上来说vpred版本就是要好于eps版本,不管是画面还是提示词效果,前者都更好
tag的服从性比pony强太多了,画面颜色也要比pony原生模型强得多,在lora模型训练上,个人体验下,风格学习比pony强,但角色lora学习上相较于pony还是差点,两个lora对比均是同一批数据与参数,不过单从tag的服从性上来看,noob可以说是下一个sdxl的支柱级模型,狠狠期待正式版了
请问这个模型需要使用refiner么?我在使用refiner工作流的时候基本上生成不了正常的图片,使用直出+高分重绘才会能正常出图。
不需要,直出就好。
@Euge_ 好的,谢谢了
相较于v-pred版本0.5的eps版本有更好的颜色和画面稳定性,我用用的是comfyui生成的,v-pred总是颜色太过鲜艳,画面稍微模糊,可能是我设置问题吧,但是0.5的eps版本就是正常的,总之支持支持,除了pony这是我用过的最好的动漫模型了,感谢作者们!!!
我更喜欢early版本,eps版本的颜色艳的有些脏,没之前柔和了。vpred版本我只能用comfy用,感觉和early没有大的差别,就继续用习惯的webui用early版本了。
新版本支持更多角色并提高了画师还原度
comfyui出来的图质量有点低,请问你们是怎么调的,我出来的图饱和度太高了同时质量也不行
How to use NoobAI-XL v-pred in comfyui always produces overly bright or blurry images.
just search ModelSamplingDiscrete at link it to models,and switching sampling to v-predction
Something I have notice in these models is that even if you have the artist at 0, it still affects the result. Ex: Prompt, Artist1, (Artist2:0)
The Artist 2 even at 0 will add its Style into the image, not sure if it's a bad or good thing, but I just wanted to point it out. But it's good to know.
Great models, I really enjoy the fact that is keeps the artstyle of series quite well, especially if you intend to replicate it.
That's how it would work with any token, If you don't want an artist name to influence it, either do not put it or put it in negative prompt.
fixed retry pls
@L_A_X Sorry, I only saw the reply three days later.invalid invite again.
我把之前用 Novel AI 生成的,我认为质量很高的那些图片的提示词,用在这个模型上,效果还能更好。主要有两点:
一些 Novel AI 需要加权重才能表现的角色/细节,这个模型不用加权重也表现得很好;
有时候,这个模型能准确生成同时满足十多个标签的图。Novel AI有时加了一个标签就会盖掉其他标签的概念。
不过也有少数标签在这个模型上不管用,但在 Novel AI 上管用。
总体上0.5版本已经超越了Novel AI。
作为对比,换作 Animagine XL V3.1 / PonyDiffusion v6 的话,绝大多数概念都表现不出来,前者缺少动作/姿势/场景,后者缺少角色。
这个模型其他的tag遵从度都很高,就只有2girls老不认,有什么更好的办法么
加上两人互动的TAG
比如什么“看另外一个人”“抓住对方的胸部”之类的
comfyui出来的图为什么怪怪的,怎么改都没用
为什么eps用comfyui出的图感觉图片线条特别黑而且粗
俺也一样,怎么弄都很怪
请问解决了吗
@wumingzi49357 我也是,用几个几乎没黑线的画师凑起来硬是多了一堆黑线
@wumingzi49357 可以试一下降低去噪强度,好像会好一些
@akiame_ 没有啥用
This is by far my favorite model for imagen. Having All the character and artist tags really is a blessing. Still not as much detail as Pony, but it's the best one out there that isn't Pony. If there is, would love to hear about it
Very nice model, but when it comes to artist tags, can I use the same as NAI3?
i found the NAI3 format works but i just use the names like ciloranko, sho \(sho lwlw\), etc
What I did is use the format artist: XXX(artist's name), as long as the artist has a tag on Danbooru it will work if you are not sure the artist you want has a tag or not you can copy the artist's name and try to research on Danbooru's artist page.
Holy f, what the f kind of data did you guys feed this model with, like this model is absolutely bonker, like this model is on an entirely different level. I mean prop to the team, but like this model basically make most other XL model obsolete.
This is what happens when things stay open source and non-gate kept, the community comes together and makes amazing models like this.
@Light7799 indeed open source FOREVER.
one should just discard their "muh ethics" to achieve this
I am sorry for noob comment, what can it do over pony? like it has built in characters or something different?
@PM786 not a noob comment. What it does over pony is it can do pretty much everything pony is able to do, but it makes the generations look more human-like, and less-ai. like, it doesn't have that AI feel or look that most models do. (I can't explain it, it's just something you notice if you've used AI for a long time) It knows a lot of artists also, on top of that it's pretty good with knowing obscure characters out of the box, can do some more niche concepts, too. It also doesn't need that score nonsense
@Light7799 Yup, pony also have pretty horrid lighting.
太厉害了,期待正式版
OMG... Normally, when you specify 2girls kissing and give their names, it often results in them being either blended together or inconsistently rendered. But this model managed to avoid that! So, I thought to myself, 'Okay, let's raise the stakes and really test how good it is.' I decided to try 3girls with three different character names: two of them kissing while the third sat in the NTR chair watching. I was shocked by the results I got—there was zero cross-contamination! And I didn't even lock in specific character traits like eye color or outfits; I just used the character names to define their appearance. (For context, it was Fauna kissing Mumei while Kronii sat in the NTR chair watching.)
whoa what you can do that?? I always thought it will bleed into each other. how do you prompt it assigning one character trait for that character only and not having it bleed to the other ones??
@Ainokura like this, 3girls, yuri, breast press, (Character name), (Character name), (Character name), on chair, voyeurism, bed, bedroom, netorare. i also added thing things like hololive english or nier automata/pokemon etc, it is highly dependable on how many images you can find on danbooru of that character tho anything under 1k is not guaranteed be a trained tag. the logic of how make describe a scene with only tags instead of natural language is each character has a task, so you know that on chair and voyeurism and netorare is locking them to chair, i think order have deciding factor so. also, if there is a lot of fan art of kronii getting cucked by mumei and fauna so it kind of knows what order to put them in.
@zhanzix93913 I see, I thought so as well. maybe its just me but if I put things directly next to the name of the character's name it most likely will assign it to that one, maybe its just placebo effect lol but you're right about the order of which things you prompt first is important as well.
Is the v-pred version worth swapping a setup for? Is it that much better?
for most people, it will be better to wait until it's finished training, since this version is an extremely limited proof of concept.
@hasoo Hmm, well when it's finished, is v-pred going to be the "offical" version? Does it have that much of an advantage over the legacy samplers?
I know it sounds lazy, but I just have Fooocus set up now and am wondering if I should take the time to set up a diffusers-based script or UI like reforged.
@brucethemoose2 in terms of official by the creators, they said there will be both versions available fully completed. vpred will have a full range of color and should make much better shaded/night scenes and contrast especially, if those things are important to you.
here are some prompts I stole from the gallery and made lazy comparison gens of between the current unfinished vpred vs eps 0.5 models, to try to highlight some of the difference: https://files.catbox.moe/mfjaoj.jpg
https://files.catbox.moe/2am859.jpg
https://files.catbox.moe/w8629d.jpg
(please note the judgement in these images should be largely focused on color/contrast since we don't have a 1:1 comparison with training)
you can see that the eps version has a tendency to "wash out" colors with grey and cause night/dark scenes to have a look a bit like there is a spotlight in the image, or lean towards grey instead of pure white. it can still look very nice on its own, but vpred/zsnr is used to fix this coloring fault. According to the NAI paper that was released, vpred zsnr may also positively impact image composition, but I think it's too early to tell yet if that will be the case with the noob models.
I'm biased towards personally preferring vpred, but if you're already comfortable as-is, I'd wait til they're both released then look at both posted galleries. If you notice the color difference of vpred and prefer it, might as well switch your setup if fooocus doesn't get support for it. In terms of official as in loras, my guess is most creators will bake eps loras or loras for other eps models that still have some forward compatibility, which I think should work on either version? (I might be wrong there and vpred needs vpred loras, sorry for misinfo if I'm mistaken..)
V-pred is heavily advantaged over non-vpred versions of equivalent training time. The primary advantage is color accuracy, composition and diversity. You can get dark blacks and brighter brights with high amounts of sharpening with v-pred models. But the main rule of thumb at the end of the day is more training time = better model, so just use whichever one has the most epochs.
@hasoo Do I need a config file to go with it? I read that that's the case for v-pred models.
I figured out that I have to add a "ModelSamplingDiscreet" node to the mix but I'm not sure if I need to do anything else.
Also is it worth using that node for the epsilon model too? It seems to make things look better with that too but it might be confirmation bias.
@patchworkpants Generally you don't need the config yaml anymore. Comfy has a ModelSamplingDiscrete node that can handle v-pred without issue. reForge inherits the comfy node as a built in extension in the upstream_dev branch. You need to look for the advanced sampling extension. If you're using Forge or the dev branch of Automatic1111 webui @nubby baked the keys into the model so they'll autodetect and run:
https://huggingface.co/nubby/noobaiXLNAIXL_vPredTestVersion_with_keys
You can't use v-prediction sampling with non-vpred models. It just makes a bunch of jumbled noise.
@patchworkpants what @Jemnite said is accurate. if anyone has any issues getting things set up further or related questions, I recommend considering joining the noobxl discord group linked in the model description - lots of helpful people there that usually respond pretty fast ❤️ (sorry for my late reply 😅)
@hasoo awesome! I will. Thanks
@Jemnite I'm finding I'm getting lots of banding or weird glitchy looking backgrounds, especially with img2img or unsampling. I'm at work now so I can't really check the discord but I will later on
@Jemnite what's the deal with the epsilon setting on that node? I tried it with the epsilon version of this model and the results were different but I couldn't tell if they were necessarily better
Epsilon noise is just the regular sort of noise that SDXL is trained on. I'm not sure why it's different, possibly because it's detecting ZSNR settings? I use reForge so I may not be of much help if you're using ComfyUI. You can try asking their matrix space here:
Thank you very much for your creation, I think it works very well💝
This model is really something else...Its pure magic. you have set a new bar for others to reach 10/10
jesus christ what did you do with this model? its f*cking amazing!
非常好模型,就是comfyui跑fp8 e4m3fn会全黑,要换e5m2
超棒的模型,大佬能不能发一个标准COMFYUI流程
目前跑下来epsilion不同参数差距超大
而且WEIUI i2i会非常琐碎 有点奇怪,
总是无法跑出测试报告的图片的完成度
idk why but the quality is less than u made idk but we actually using the same words right????
https://civitai.com/posts/8305441
一个测试,文生图后使用局部重绘和photoshop加入对话,最后五张图有图片信息,平均大概一张图一个小时,全部20张跑完大概需要一天吧~实际上选图和修改图片的时间很少,大部分时间是在调整提示词
This is just a test. After generating images from text, we'll use inpainting and Photoshop to add dialogue. The final five images contain image data. On average, each image takes about an hour, so finishing all 20 images would take around a day.Actually, very little time is spent selecting and editing images. Most of the time is spent adjusting the prompts.
this is awesome. But what is the difference with Illustrious? You train the smae base modle on the same dataset with the same captions?
Or I am getting something wrong?
To my understanding Illustrious is trained on only a danboory dataset while noob is trained on both danbooru and e621 making for a much bigger dataset although I could be wrong
@Idkanymore50 Oh. Thanks
Is there a list of all tags compatible with this model?
There is this list for novelai https://raideninfinity.pythonanywhere.com/naidv3_tag_search
im pretty sure they share the same tags
What is the new discord invitation link? I want to help with datasets and captions.
我喜欢它
https://civitai.com/posts/8381783
全本完工,没有拼接,所有分镜都是文生图整页直接生成~~
算是给noob做了次漫画工作流的质量测试
平均时长15小时,主要时间浪费在嵌字上,因为我没啥字体可用,熟练后估计每页只需要半小时
制作思路可能会让侄女在b站做个教程
Completed the entire project with no stitching; every panel was generated as a full page directly from text-to-image.
This was essentially a quality test of a comic workflow for beginners.
Average time spent was around 15 hours, with most of it used on text embedding since I didn’t have many font options. Once proficient, each page should only take about half an hour.
The large scene always shown many colorful dots or parts that shouldn't appear.
Are there any prompts and negatives that must be filled out?
I don't know what to write because there are no recommended negatives.
Details
Files
noobaiXLNAIXL_epsilonPred05Version.safetensors
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noobaiXLNAIXL_epsilonPred05Version.safetensors
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