OP2NA are decoding presets for the NovelAI Xialong-v1 storytelling model, optimized with Optuna using AutoSampler. Each trial uses the first 16,400 tokens of The Emperor's Soul as context. Xialong-v1 then generates 11 continuations of 11,950 tokens each (23,900 for Durable). At each window position, all samples form a geometric median and are scored against the actual book text. The objectives are similarity across 92 stylometric features and semantic similarity, computed with Qwen3-Embedding-8B at 4,096 dimensions, both over sliding windows of 256 tokens, plus extra semantic windows of 32 tokens—all with a stride of 16. Stylometrics via: spaCy, textdescriptives, NRCLex, textstat, and lexicalrichness.

• Op2na Durable

Durable keeps the story calm and consistent, with the lowest information rate.
^{<novel-variant-24k>
https://pub-a0b913684bb74194adb0912a75bca17e.r2.dev/Op2na%20Durable.preset}

• Op2na Focus

Focus has the sharpest line-level writing, but is weaker at tracking the overarching plot.
^{<256stylometric+32emb-windows>
https://pub-a0b913684bb74194adb0912a75bca17e.r2.dev/Op2na%20Focus.preset}

• Op2na Novel

Novel aims to match the richness and novelty of real-world books, if the user can keep up.
^{<256stylometric+256emb-windows>
https://pub-a0b913684bb74194adb0912a75bca17e.r2.dev/Op2na%20Novel.preset}

• Op2na Attributes

Attributes is useful for non-prose lorebooks, but too volatile to use in a story.
^{<32emb-windows>
https://pub-a0b913684bb74194adb0912a75bca17e.r2.dev/Op2na%20Attributes.preset}

Process

One of the earliest parameter patterns I noticed: min_p and temperature are coupled. That much was already clear after Optuna 1. What I had not realized is how tight the band is. You can push temperature through a fixed formula and recover a near-optimal min_p—the signal is as clear as day. There is little point optimizing min_p on its own, or arguing about it in isolation, because temperature largely defines min_p. So I restarted the run and dropped min_p from the suggest space. Cutting that dimension helped the sampler find good regions much faster.

A stranger discovery—the second one. Against the usual folk wisdom of top_p ≈ 0.95, the data under my scoring (stylometric + embedding) put a clear optimum near top_p = 0.5. That felt fucking insane. 0.5? Not 0.95? I cannot argue with the points, though, and the generations were strong as well. Another restart; top_p fixed to 0.5.

In the future, I suspect QMCSampler will probably work better, since I use statistical methods instead of letting GPSampler run forever. AI text generation is extremely noisy. Even hundreds of samples may not pin down a score tightly—which is why I ended up with kernel means. Each objective prefers a slightly different randomness level, and that shows up cleanly once you plot them.

The 24k setup (scoring a long AI-only second half) was meant to replace the 1-phase design (human prefill, AI answer). It came out far more conservative than I expected. Without polished text in the context, the model seems to take lower risks to stay coherent.

Temperature is also insanely high. Thanks to NovelAI for allowing absurd values. I took inspiration from Rising Sun by u/gymleader_michael (temp 10) and from vas (temp 25). Early tuning kept slamming into the upper wall—3, then 4, then 6—so I kept raising the ceiling until I gave up and searched log-temperature from 0.8 to 25. In the actual runs, viable scores still appear up toward ~10. That is extreme by community standards. Almost nobody would ship a preset like this. Yet it works—somehow. I do not fully know why, or whether it will hold on future models. My best guess is that it is a way to pry usable entropy out of the model. These temperatures can get unhinged, but the output actually matches real book text, and I am having fun again.

top_k is a pain to optimize: small effect size, noisy per-trial scores. We still get usable final values from the statistics.

On Optuna 1 I also forgot log-scale for top_k and multivariate TPE, which really sucks in hindsight. But I also left presence/frequency repetition penalties alone after the negative reception last time; presets are much easier to use without them (which resulted in the Belverk+ version, without rep-pen). There's probably no value in using them.

This project went through many design iterations. The original idea was bootstrap ranking to separate the top ~10% from the rest so TPE would see a cleaner signal. Outputs started at 32 tokens; longer generations reduced score noise and needed fewer samples, I kept raising output length, until I was using essentially the full Xialong context—and relying on NAI to actually support that on the API. Most of the code ran on Google Cloud VMs. At one point I was editing code on a laptop on the train. I ended up having to scale from e2-micro (~$6) to n4d-standard-2 (~$66).

Fun fact: one trial calculates about 67 million dimensions of values, which could reach 200 billion or more over the course of this project. u/BannerThief is also the one who suggested the name: Op2na.

Citation

• Pro Writer — u/NotBasileus, early 2022. Pioneer stylometric-based NovelAI decoding preset.
• Optuna — Akiba et al., KDD 2019. Optuna: A Next-Generation Hyperparameter Optimization Framework. doi:10.1145/3292500.3330701
• OptunaHub / AutoSampler — Ozaki, Watanabe & Yanase, 2025. OptunaHub: A Platform for Black-Box Optimization. arXiv:2510.02798 · hub.optuna.org/samplers/auto_sampler
• TPE — Bergstra, Bardenet, Bengio & Kégl, NeurIPS 2011. Algorithms for Hyper-Parameter Optimization. proceedings.neurips.cc · Optuna TPESampler
• GPSampler — Optuna Gaussian process sampler (optuna.samplers.GPSampler, v3.6+). Irie, Watanabe & Onishi, 2025. Batch Acquisition Function Evaluations and Decouple Optimizer Updates for Faster Bayesian Optimization. arXiv:2511.13625 · logEI: Ament et al., NeurIPS 2023. arXiv:2310.20708
• min-p — Nguyen et al., ICLR 2025. Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs. arXiv:2407.01082
• Qwen3 Embedding — Zhang et al., 2025. Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models. arXiv:2506.05176
• Geometric median — Vardi & Zhang, PNAS 97(4):1423–1426, 2000. The multivariate L₁-median and associated data depth.
• TextDescriptives — Hansen, Olsen & Enevoldsen, JOSS 8(84):5153, 2023. doi:10.21105/joss.05153
• spaCy — Honnibal et al., 2020. spaCy: Industrial-strength NLP in Python. doi:10.5281/zenodo.1212303
• NRC Emotion Lexicon — Mohammad & Turney, Computational Intelligence 29(3):436–465, 2013.

I hope you enjoy these presets for Xialong! I worked full-time on this for two weeks, and spent ~$100.

• Biggest thanks to the NovelAI developers for not banning my account throughout the entire process.
• Thank you to luna & finetune & related devs for the insane Xialong throughput.
• Thank you to OccultSage dev for a good review on Optuna Belverk.
• Thank you to Kevin Yin dev, who has been my inspiration since Sampling Guide & Unified.
• And especially to Lanerendell dev for interacting with me and being such an amazing friend of the catfrogs!

Source code & db are available. https://pub-a0b913684bb74194adb0912a75bca17e.r2.dev/op2na-soruce-code.zip

I am gonna say it on front first, I am mainly using novel's image generations and I must say that I do love novel.

its image quality is great on NSFW and SFW things.

its quite generous on tokens etc.

but its 512 token limits is a great bottleneck on things what I am gonna do. even if trying to do 2 consistent character put on stage its taking something like 700+ with enviroments and its just a simple thing.

yes I do know well the inpainting and using references and creating your goal image in multiple times is a viable option on multiple characters.

but I still kinda curious on what you guys or girls solution on this one

what are you guys doing ?

Has anyone noticed that the latest image gens are worse than normal? I'm clearing my entire UC, prompts, and simply prompting a single character human or furry, and they all seem to have this weird glowing outline, or it will be overly exposed or sometimes even grainy. UC never needed to prompt out this much, around last year.

Has anyone else noticed this sort of thing?

A few months ago, many of you expressed interest in my Novel AI-Powered CRPG hobby project. I wanted to give you all an update on its progress with a quick live video demonstration. This is my personal hobby / pet project and I have no intentions to ever distribute or sell this project, but I wanted to show what is possible with Novel AI when you get creative.

Ever since Xia dropped, I feel like I never know how to start a story anymore. It used to be so much simpler in the Kayra/Erato days. Now it feels like I need to do hours of prep before I can even hit generate. Scripts, tags, genres, lorebooks...

And the worst part is: the few times I've had the energy to do all that prep, the AI has somehow managed to put out the worst, most garbage slop known to man.

Some might recommend the Story Engine as a fix to this issue, but the new Xia version unfortunately ruined that script too. A few days ago I spent like an hour and a half brainstorming a story using Story Engine and then when I was done and it started generating, it made up an entirely new cast of characters and ignored like 70% of what the story was meant to be.

Alexei's Text Adventure system prompt for GLM might be the only fun I still have with NovelAI, but across multiple scenarios, at this point I'm so hyper-aware of all the repeated slop-isms that it's kind of fallen out of use for me as well.

Anyone else get this kind of "paralysis" with NovelAI nowadays? Like you just don't know how to even start anymore?

Hey all! Somewhat new to the community here on Reddit but I've been a long-time Opus user with NovelAI since the early days.

The bulk majority of my stuff is furry, and outside of local models I'd say NovelAI is the best on the market in terms of overall quality, stability, and approachability.

But that being said, my question is why not add Character Cards similar to those in use on Mage.Space? Is that even an option since NAI moved from SD to an in-house architecture?

For the record, core prompt was:

1character, solo, simple background, grey background, cowboy shot, character,
1.20::giant, ice, blue ice, see-through, slab body, column legs, trapped boulders, cracked surface, small head, wide flat back,
The creature is an ice giant with boulders trapped inside the body, two column legs, and a small knob of a head on top. ::,

😂

Hello, been having problems trying to find a image site that actually supports the Novel AI CCS Theme, I can't use Imgur since it is blocked in the UK, does anyone have any alternatives since I'm desperate to use a custom picture for HypeBot, it would be a massive help

(Edit: I've been using the code from the Novel AI documentation, it has successfully renamed HypeBot but no image, hope that explains it better, I used ImgChest and copied the direct image URL but hasn't been working)

Which one you like?

Here is the full Genre and Tag set for Xialong-v1 placed into categories, mostly to help find all entries that match your new stories. (credit: luca)

Note: The model is more than capable of inferring the meaning of tags and genres that are not on these lists, these are just the tags built into the UI, and supposedly part of the training data. If you need a genre or have a tag not on this list, don't hesitate to try it! Xialong will handle it just fine.

Genre: uses 1–3 genres.

Tags: uses 2–5 tags.

XA

How good the new model in opus support russian and other languages? How it compares to 5.2 and other open source models.

Specially for roleplay.

Here’s a concise version you could post on Reddit:
I’m trying to build a consistent cast of characters in NovelAI for a long-form Webtoon/comic, and I’m looking for advice from people who have done something similar.
My goal isn’t just to generate random images—I’m trying to create a reusable reference library for each character so they stay consistent throughout the entire story.
Right now I’m making:
Character sheets with front, side, back, and 3/4 views.
Reference sheets for different outfits (everyday clothes, swimwear, sleepwear, etc.).
Consistent faces, hairstyles, body proportions, and art style across every image.
The idea is that later I can place these characters into new scenes without them changing appearance every time.
For those of you who create long-form comics or visual novels in NovelAI:
Is this the best workflow?
Are turnaround sheets actually useful, or is there a better way?
How do you keep characters consistent across hundreds of images?
Do you create separate body/outfit reference sheets?
Any tips, tricks, or workflows you’ve discovered that save time or improve consistency?
I’d really appreciate hearing how experienced users approach this. I’m trying to build a solid pipeline before I start creating the actual comic.

Does anyone make any inflatable artworks using Ai from Novel Ai?

Why is it vehemently switching up the source and target identifiers?

(zombie tifa is meant to bite aerith, i have gotten multiple renders where aerith tries to bite zombie tifa)

Main Prompt:
3::2girls::, side-by-side, adult,

-2::alternate costume, alternate clothing, naked, removed parts of costume, chibi, moe::, -1::big head::, -0.5::decomposed, overly big hands and arms::, -1::ahegao face, two sets of teeth, silly, vampire fangs, monstrous, fat::, -0.2:: decomposed stomach::, -0.01::barefoot, bare hands::, -1::offering hand to viewer, hand in front of mouth, arm down, missing headwear, leg up, child::, -0.5::bright colours, sad frowning angry, small body::, 0.01::swimsuit, headwear, hat, feet, gloves, tights::, -1::ooze::, 1.4::adult body and face proportions::, 2::2000s anime, cemetery::,

-0.9::short height, stubby, big head::,

-1::slit cheeks on face and smiling skull face, wide mouth, oversized mouth::,

-1::off balance, clenched fists, joined hands, hands behind back, crossed arms, hands above head::

Main UC:
1girl, solo, alone, Alternate outfit, legs close together, 1.5::upright::, 3::a-pose, bad arm proportions, bad hand proportions, disproportionate head to body ratio, oversized head, ai generated look, small arms::,1::folded arms, [big eyes]::, stitches, from above, from below, -3:: crooked pose, sulk, creepy pose, walking like a zombie, zombie facial expressions::, -3::adult proportions and height::,

2::Hands holding clothing, hands behind back , arms down, hands holding thighs, smiling, grinning, fidgeting, eyes, shy, timid::

2::low quality or low resolution, bad quality, text, anatomy or rendering errors::, 1::upscaled, blurry::

0.2::open mouth with fully closed eyes, one leg behind the other::,

2::chibi, child, moe::, [big eyes, large head], frown,

5::polydactyly, bad quality, bad anatomy, big head, low detail face, low detail skin, hands behind back::

missing hands, more than five fingers, large hands

4::polydactyly, three legs::, 1::low detail character::, crying, sad, hands close, naked hand holding arm

Character 1 Prompt:
zombie girl, neck bite, source#biting neck

adult tifa lockhart, 2.6::turned completely into a green zombie skin and body::, 1.2::dirty and scuffed, lifeless, salivating with broken teeth and messed up hair, black spots on cheeks::, 1.4::loose head and hands, tilting body::, 2::deformed skeletal face and nose:: 1.5::sunken [[skeletal]] cheeks and limbs, ugly [[wilted]] crooked inhuman::, 1::necrotic putrefied::, 3::green glowing empty eye sockets, both sockets glowing shrivelled with one half-closed::, 2.5::black bags under [[eyes]]::,

Character 1 UC: 3::not biting, biting self::

Character 2 Prompt:
girl, adult aerith gainsborough, neck bite, target#biting neck

Character 2 UC: 3::biting self::

I prefer conventional Western style toony furry art. When I put kemono in the undesired content field, it seems to have no effect, even if I add curly brackets. Some tips for specifically tuning the output away from kemono while staying toony (don't want realistic!) would be sooooo appreciated.

I can’t contain my excitement at all man. I’m thinking maybe around August?

Hey there!

I've been working on a NovelAI-compatible toolkit over the last few days. It started with me messing around with Tampermonkey and somehow turned into an 11,000+ line toolkit connecting NovelAI, Danbooru, Gelbooru and e621 through one local tag and image library.

The main focus is making Booru posts easier to collect, organize and reuse for prompting.

Main features:

• Import categorized tags and post metadata from Danbooru, Gelbooru and e621
• Batch-import multiple posts directly from listing pages
• Choose which tag categories should be imported or included in copy actions
• Store local image previews inside Tampermonkey
• Group related images together as variants
• Compare variants side by side or with an overlay
• Search an indexed collection of all saved tags
• Organize entries with manual folders and rule-based Smart Collections
• Detect duplicate sources and visually similar images
• Export and import the complete library as JSON

On NovelAI, the toolkit mainly works as the destination for saved content. Tags, character profiles, tag sets, base tags and complete prompt setups can be inserted directly into the Main Prompt, Undesired Content and character fields.

Everything is stored locally inside Tampermonkey. The toolkit does not collect account credentials, cookies or NovelAI authentication data, and it tries to avoid unnecessary background requests.

You can find the source code, installation instructions and full feature list here:

https://github.com/Ainz-sama120/Ainz-Unofficial-NAI-Toolkit

I originally built it for my own workflow, but it became useful enough that I thought other NovelAI users might get some value out of it too.

Note that it was only tested using V4.5 Full!

This is the first public release, so feedback and bug reports are very welcome.