Starting with v0.25, this model has completed the alpha testing phase. Settings and features will be stable, it will be a 12-step dmd2 model. Therefore, this page has been completely rewritten. If you are looking for information about older versions, refer to this link.

RDBT [Anima]

Finetuned, then distilled. It delivers faster speed and higher aesthetics with only 12 NFEs, 5x faster than base model (60 NFEs).

Sharing merges using this model is not allowed. If you think this model is useful, please share the link or the file.

Usage:

See circlestone-labs/Anima for basic usage about dataset, style and tags. Below are differences between this model and vanilla Anima.

Settings:

• Sampler: "euler_a" "euler" "er_sde".

• Steps: 12 or 24. Scheduler: simple. Important: training timestamps are fixed. Other inference timestamps might not work.

• CFG scale: 1~2. Cover images are all without CFG (CFG 1). You can enable CFG (CFG >1) if you need higher prompt adherence (e.g. style is too weak). Very high CFG is not necessary.

Prompt

Prefer natural language prompt. Prompt structure: style, subject, action, background.

There are two "rough" trigger words:

• "digital anime illustration": 2d anime.

• "digital art", 2d art but not anime, mostly digital art. (not many samples)

Quality tags:

You can omit all the quality tags. 1) The quality of training data is higher than "masterpiece". 2) Quality tags have been reinforced during distillation. Thus they don't have noticeable effects.

Same as negative tags. If you use cfg, there is no need to dump "score_1, blurry, worst quality, jpeg artifacts, extra arms,... x100 words" in your negative prompt. Those things have been distilled out.

Released models

RDBT LoRAs: Released as LoRA. For better distribution efficiency.

Update Logs

(4/18/2026) v0.25: It's based on anima p3.

Previous testing versions, see RDBT LoRAs