REED_Anima_XXX

Anima 模型真的驚豔到了我！所以我使用 Anima_base 模型進行多次 Mix 以及細節調試後，REED_Anima_XXX_v10 誕生啦！我依舊會進行常規化更新以及數據庫維護，並持續提供免費更新。同時，我會在 SeaArt 提供優先版本更新以及免費線上繪圖。

REED_Anima_XXX - Seaart AI

如果喜歡我的作品並且想支持我的話，歡迎在 Seaart.ai 訂閱我的帳戶～

ReedVaughn - SeaArt AI

我認為 Anima 包含了一些類似於 SD1.5 的新技術，我也在嘗試中，想試試看有沒有更適合它的參數以及更合適的高解析度放大模型（Hi-Res/Upscale Model）。

以下是一些相關數據，歡迎參考：

Works at resolutions between 512^2 and 1536^2 pixels

• Steps: 20–35 (Best around 30 )

• CFG Scale: 3-5 (Best around 4 )

• Sampler: er_sde (or Euler )

• CLIP Skip: 2

• VAE: Already integrated

• Restore Faces: Off

• Adetailer: On (for clean facial detail)

✅ Positive Prompt (Universal & Stable)

masterpiece, best quality, amazing quality, absurdres,

🚫 Negative Prompt (Recommended for Clean Results)

worst quality, low quality, early, old, score_1, score_2, score_3, cartoon, graphic, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured, long body, bad anatomy, bad hands, missing fingers, extra fingers, extra digits, fewer digits, cropped, very displeasing, artist name, blurry, jpeg artifacts, lowres, censor

High-Res

• Base Resolution: 1024×1024 or larger

• Highres Fix: On

• Upscale Factor: 1.5×

• Hires Steps: 10

• CFG Scale: 5.5

• Upscaler: 4x-AnimeSharp

• Denoising Strength: 0.8

以下為我自用的 Anima T2I（文字生成圖片）工作流：

Reed Anima T2I Workflow

可以看到，v10 版本的圖片集我提供了圖片對比，單數張為模型直出圖，而雙數張為透過 4x-AnimeSharp 進行高解析度放大的結果，我相信這會為大家提供不小的幫助！所有圖片均採用同一個種子（Seed）與同一個負面提示詞（Negative Prompt），方便大家了解真實情況。透過實際測試下來，原圖直出反而會保留更多細節。

I created a keyword generation Bot, and I deployed it in the Prysai Discord channel. It supports comic style/Furry style. It is free. Word Agent Bot for REED | Civitai

[[EN]]

The Anima model truly blew me away! After multiple mixes and detailed fine-tuning based on the Anima_base model, REED_Anima_XXX_v10 is finally born! As always, I will continue to provide regular updates, database maintenance, and free versions. Meanwhile, I’ll be releasing early-access updates and offering free online generation on SeaArt.

REED_Anima_XXX - Seaart AI

If you love my work and want to support me, please consider subscribing to my profile on Seaart.ai!

ReedVaughn - SeaArt AI

I believe Anima incorporates some new technologies similar to SD 1.5. I’m still experimenting with it, trying to find better-optimized parameters and high-resolution fix models that suit it best.

Below are some relevant generation settings/data for your reference:

Works at resolutions between 512^2 and 1536^2 pixels

• Steps: 20–35 (Best around 30 )

• CFG Scale: 3-5 (Best around 4 )

• Sampler: er_sde (or Euler )

• CLIP Skip: 2

• VAE: Already integrated

• Restore Faces: Off

• Adetailer: On (for clean facial detail)

✅ Positive Prompt (Universal & Stable)

masterpiece, best quality, amazing quality, absurdres,

🚫 Negative Prompt (Recommended for Clean Results)

worst quality, low quality, early, old, score_1, score_2, score_3, cartoon, graphic, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured, long body, bad anatomy, bad hands, missing fingers, extra fingers, extra digits, fewer digits, cropped, very displeasing, artist name, blurry, jpeg artifacts, lowres, censor

High-Res

• Base Resolution: 1024×1024 or larger

• Highres Fix: On

• Upscale Factor: 1.5×

• Hires Steps: 10

• CFG Scale: 5.5

• Upscaler: 4x-AnimeSharp

• Denoising Strength: 0.8

And here is my personal Anima T2I (Text-to-Image) workflow:

Reed Anima T2I Workflow

As you can see in the v10 showcase, I have provided a side-by-side image comparison. The odd-numbered images are raw text-to-image outputs straight from the model, while the even-numbered ones are the upscaled results using 4x-AnimeSharp. I believe this will be super helpful for everyone! All images use the exact same seed and negative prompt so you can see the true, unedited performance. Based on my actual testing, the raw generation (direct output) actually retains more fine details.

Form Anima Base

Prompting

The model is trained on Danbooru-style tags, natural language captions, and combinations of tags and captions.

Use lowercase for tags, and spaces instead of underscores. Score tags are the only tags that use underscores.
Recommended positive prefix: "masterpiece, best quality, score_7, safe, "
Recommended negative: "worst quality, low quality, score_1, score_2, score_3, artist name"
When using a tag that is different between Danbooru and Gelbooru, prefer the Gelbooru version.
Prompt weighting works, but needs a weight higher than typically used for SDXL. Example: "(chibi:2)"

Tag order

[quality/meta/year/safety tags] [1girl/1boy/1other etc] [character] [series] [artist] [general tags]

Within each tag section, the tags can be in arbitrary order.

Quality tags

Human score based: masterpiece, best quality, good quality, normal quality, low quality, worst quality

PonyV7 aesthetic model based: score_9, score_8, ..., score_1

You can use either the human score quality tags, the aesthetic model tags, both together, or neither. All combinations work.

Time period tags

Specific year: year 2025, year 2024, ...

Period: newest, recent, mid, early, old

Meta tags

highres, absurdres, anime screenshot, jpeg artifacts, official art, etc

Safety tags

safe, sensitive, nsfw, explicit

Artist tags

Prefix artist with @. E.g. "@big chungus". You must put @ in front of the artist. The effect will be very weak if you don't.

Full tag example

year 2025, newest, normal quality, score_5, highres, safe, 1girl, oomuro sakurako, yuru yuri, @nnn yryr, smile, brown hair, hat, solo, fur-trimmed gloves, open mouth, long hair, gift box, fang, skirt, red gloves, blunt bangs, gloves, one eye closed, shirt, brown eyes, santa costume, red hat, skin fang, twitter username, white background, holding bag, fur trim, simple background, brown skirt, bag, gift bag, looking at viewer, santa hat, ;d, red shirt, box, gift, fur-trimmed headwear, holding, red capelet, holding box, capelet

Tag dropout

The model was trained with random tag dropout. You don't need to include every single relevant tag for the image.

Dataset tags

To improve style and content diversity, the model was additionally trained on two non-anime datasets: LAION-POP (specifically the ye-pop version) and DeviantArt. Both were filtered to exclude photos. Because these datasets are qualitatively different from anime datasets, captions from them have been labeled with a "dataset tag". This occurs at the very beginning of a prompt followed by a newline. Optionally, the second line can contain either the image alt-text (ye-pop) or the title of the work (DeviantArt). Examples:

ye-pop
For Sale: Others by Arun Prem
Abstract, oil painting of three faceless, blue-skinned figures. Left: white, draped figure; center: yellow-shirted, dark-haired figure; right: red-veiled, dark-haired figure carrying another. Bold, textured colors, minimalist style.

deviantart
Flame
Digital painting of a fiery dragon with glowing yellow eyes, black horns, and a long, sinuous tail, perched on a glowing, molten rock formation. The background is a gradient of dark purple to orange.

Natural language prompting tips

Follow standard English capitalization rules for character and series names.
If using pure natural language, more descriptive is better. Aim for at least 2 sentences. Extremely short prompts can give unexpected results.
You can mix tags and natural language in arbitrary order.
You can put quality / artist tags at the beginning of a natural language prompt.
- "masterpiece, best quality, @big chungus. An anime girl with medium-length blonde hair is..."
Name a character, then describe their basic appearance.
- "Digital artwork of Fern from Sousou no Frieren, with long purple hair and purple eyes, wearing a black coat over a white dress with puffy sleeves..."
- This is extra important when prompting for multiple characters. If you just list off character names with no description of appearance, the model can get confused.

Limitations

The model doesn't do realism well. This is intended. It is an anime / illustration / art focused model.
The model may generate undesired content, especially if the prompt is short or lacking details.
- Avoid this by using the appropriate safety tags in the positive and negative prompts, and by writing sufficiently detailed prompts.
The model isn't great at text rendering. It can generally do single words and sometimes short phrases, but lengthy text rendering won't work well.
The base version is a true base model. It hasn't been aesthetic tuned on a curated dataset. The default style is very plain and neutral, which is especially apparent if you don't use artist or quality tags.