HARUKI_MIX

8194°

#torrent #anime #1girls #asian #pony #japanese

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Created by

HARUKI_KAWAI

on Civitai

Pony checkpoint, shared as a torrent on CivitasBay (★Pony_v2.3). The model is also listed on Civitai.

Creator notes

日本人特化の実写専用AIモデルである「HARUKI_MIX」は、『誰でも簡単に可愛い女の子が生成できるモデル』を目指した入門者用モデルというコンセプトで、2024年10月にリリースしてから早2年が経過。昨今のモデルの進化を鑑み、このコンセプトとしてのモデルの役割はもう必要ないと思うので順次非公開にしていきます。引き続き、モデルは作っていきますが、コンセプトを変えて完全クローズドな場所で配布していく予定です。 ※有料・無料・非公開モデルに関わらず、ダウンロードしたモデルの再配布・共有・販売・転載（ファイルのアップロード、譲渡、URLの共有などを含む）は一切禁止します。ご利用はダウンロードしたご本人のみに限ります。 ※私のモデルをSNS等で紹介してくれるのは非常に有難いのですが、事前または事後にでもXのDM等で教えていただけると喜びます・Xフォロー → ・ko-fi → 生成される人物は、完全に架空のAI生成キャラクターであり、現実世界には存在しません。実在の人物との類似点はすべて偶然の一致です。 The characters generated are entirely fictional, AI-generated characters that do not exist in the real world. Any resemblance to real people is purely coincidental. ■ KR2_V2.0について【V1.0からの変更点】・複雑な性行為のポーズにおける解剖学的精度を向上したint8_convrotモデル・NSFW LoRAとの互換性を向上・顔を再チューニングバージョンが上がったからと言って、各個人が求める品質が向上しているとは限りませんが、引き続き、Krea 2での画像生成をお楽しみくださいませ。 ■ KR2_V1.0についてフォトリアルを追究したNSFW対応済みのマージモデルに、自作のキャラクターのLoRAをファインチューニングしたINT8-ConvRotモデル。 ※bf16、fp8モデルを公開する予定はありません。 ※このモデルで生成できないNSFWの表現はを活用してください（推奨設定） ■Sampling method（Schedule type）：Euler（simple / beta / bong_tangent） ■Sampling steps：8 ■CFG Scale：1 ■ ZIT2606についてファインチューニングしたキャラクターLoRAを見直し、NSFWの表現力が向上 ■ ZIT2606RealについてファインチューニングしたキャラクターLoRAを見直し、フォトリアル感が向上 ■ ZIT2603について細かい部分までリアルに再現し、色鮮やかで光の当たり方も自然になりました。こちらも自作のキャラクターLoRAをファインチューニングしているので顔の多様性はありません。 ■ ZIT2602についてモデルマージを複数回繰り返し、最後に自作のキャラクターLoRAをファインチューニングしました。 ※1 お尻を突き出した時のアナルが、描写されたりされなかったりと不安定です ※2 トレーニングアダプターファイルの提供はありません・ZITモデルの推奨設定モデルを何度も統合し、自作キャラクターのLoRAをファインチューニングしました。 ※1 お尻を突き出した際の肛門の描写に一貫性がなく、表示される場合とされない場合があります ※2 トレーニング用アダプターファイルは提供されていません ■Sampling method（Schedule type）：Euler/res_multistep/res_2s（simple/beta） ■Sampling steps：8～15 ■CFG Scale：1 ■ illustrious V4.0β版の推奨設定 ■画像サイズ：1024×1368（1368×1024）、1152×1536（1536×1152） ※当然ですがヤフオク用（A4）の画像サイズではありません ■VAE：不要 ■Sampling method（Schedule type）・つるテカな質感が好きなら：Euler a（Karras） / DPM++2M（Karras / KL Optimal）・リアルな質感が好きなら：DPM++2S a（SGM Uniform / Simple）・顔LoRA使用時は：DPM++ 2M SDE（SGM Uniform） ■Sampling steps：18～30 ■CFG Scale：4.0～7.0（好みに応じて） ■ADtailer：必須 ■ Ponyモデルの推奨設定画像サイズ：896×1152（1152×896）/960×1280（1280×960）/1024×1364（1364×1024） VAE：必須 Sampling method：Euler a Sampling steps：25～30 Schedule type：Karras or SGM Uniform CFG Scale：3.5～5.5 ADtailer：必須【ライセンス】使用制限としては、以下の通りです。主にモデル自体の取り扱いに制限があります。〇作成者をクレジットせずにモデルを使用する〇生成した画像を販売する ×有料で画像を生成するサービスで実行する ×このモデルを使用してマージする ×このモデルを使用したマージモデルを共有、公開する ×このモデルを販売、配布する〇このモデルを使用したLoRAを作成、共有、販売、公開する

5,698 downloads · 315 likes on Civitai

SHA256

7B903F38BC8A6F9E988A46F373B70BEAF4FB70534A7A94FB94F300D461361BB9

HARUKI_MIX Popularity Trend

Heat score since 2 Aug 2026

Heat score

8,194°

This week

▲

8.8%

8,179° then, 8,194° now

This month

▲

54.6%

8,116° then, 8,194° now

Past year

n/a

Wasn't released/tracked

7,000°

Floor

The scale runs from 0° to 9,999°. Everything below 7,000° is kept for models no source reports on yet, which carry a stand-in number there so lists stay complete. 7,000° is where measured data begins: a model with real data but almost no downloads sits at the floor, and the measurements fill the degrees above it. The floor says nothing about the model; only the degrees above it do, and they are what separates one model from another.

+863°

Civitai downloads

5,698 downloads of this exact version on Civitai. Downloads are the heaviest ingredient and are counted on a log scale: ten times the downloads adds the same 300°, so a model with a million downloads is not a thousand times hotter than one with a thousand. The six download sources share that one term, split between them in proportion to their counts.

+303°

Torrent seeders

2 people keep serving the weights over torrents or hosted mirrors. Serving a file costs real bandwidth and says the weights are still wanted, so each seeder counts as 1,000 downloads in the download term.

+375°

Likes

315 likes and thumbs up across the sources. A like weighs half a download, on the same log scale.

+150°

Full model

+150° because this is a full model: a checkpoint, the weights themselves. A LoRA or an embedding is an add-on to someone else's weights and does not outrank a checkpoint of equal popularity.

−497°

Age

−497° because the SDXL weights came out in 2023. Every model inherits the age of the weights it is built on: 150° a year, up to 750°, and it keeps growing until then. Without it the top of the list would be 2022 and 2023 checkpoints on the strength of downloads that had years to pile up.

Show detailed breakdown

Every number on this site can be checked. This model's heat score is 8,194°, and here it is built one ingredient at a time, with the running total after each: the floor, the degrees each source adds, the bonus for the kind of model and what the age of its weights takes away. The colors are the bands of the chart above. The glossary at the end defines every term.

1
Start at the floor.
Why 7,000° and not zero: the scale runs from 0° to 9,999°, and the part below 7,000° is reserved for models no source reports on yet, which carry a stand-in number there so the lists stay complete and never outrank a model with data. 7,000° is where measured data begins. A model with real data but almost no downloads sits here; everything that follows is measurement, and the room above the floor holds ten tenfold steps in downloads at 300° each. The floor is the same for every model, so it decides nothing; the degrees above it do.
7,000°
running total
2
Add the downloads.
7,698 download-equivalents in all, from Civitai downloads, Torrent seeders. Ten times the downloads adds the same 300°, so size matters but never runs away with the score.
+863° Civitai downloads
+303° Torrent seeders
+1,166°
8,166°
running total
3
Add the likes.
315 likes and thumbs up, at half the weight of a download.
+375°
8,541°
running total
4
Bonus for a full model.
A checkpoint carries the weights themselves; add-ons do not outrank one of equal popularity.
+150°
8,691°
running total
5
Take off the age.
The SDXL weights came out in 2023: 150° a year, up to 750°, growing until then.
−497°
8,194°
running total
=
The heat score.
Where this model stands on every list of this site. Nothing else adjusts it.
8,194°

Glossary: the terms used here

Heat score: One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
Floor: The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
Download-equivalents: Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
Log scale: Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
Likes: Thumbs up and likes from every source, worth half a download each on the log scale.
Adoption rate: How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
Measured and estimated: Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
Seeder: Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
AI Horde: A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
Generation sites: Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
OpenRouter tokens: A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
Full model (checkpoint): The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
LoRA and embedding: Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
Family and weights: The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
Age: Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
Family boost: A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
Stand-in score: A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
New! (grace period): A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
Trend: How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
Typical model: The middle of the catalog: half the models with a figure grew faster over the period, half slower.

How this model's popularity has moved against the rest of the catalog: its growth in downloads, likes and usage over each period, measured against the typical model's growth over the same period, with its heat score at the time. Above the typical growth reads as a rise, below it as a fall. The past is reconstructed from today's totals, the model's first day and its measured download speed where two readings exist; a model that did not exist at the start of a period shows n/a.

Example renders

NSFW

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