RDBT | Anima

RDBT [Anima]

This is a finetuned model with 10k high aesthetic images paired with natural language captions from LLM. Then distilled to further improve quality and stability. Dataset does not contain any shiny plastic glossy AI image.

Because the dataset is big enough, it was not overfitted and does not have a default style (in other words, it won't change the style). I use it as a clean starting point to stack more style LoRAs. I can stack whatever I want and get exactly what I stacked.

See this page for update log.

For advanced users: The RDBT model is trained as LoRA natively. See this page for original LoRA.

This model is based on:

• prefix with ym: AnimaYume (hf link) (civitai link).

• prefix with b,p: Anima pretrained (hf link)

EDIT: this model did NOT merge turbo LoRA.

I saw some guys on discord thought this is another model merged Anima turbo. No, it's not. This distilled model exists way before official anima turbo. First version RDBT for anima released at Feb 4, 2026. Anima turbo v0.1 released at Apr 21, 2026 (2 months after).

The codebase exists even before Anima model, it was for Lumina 2, and I released RDBT for lumina 2 at Dec 26, 2025.

Sharing merges using this model is not allowed. This "restriction" won't affect anyone. It's only aimed at those who steal others' models to sell. If someone is selling this model as their own, I'm happy to list them here so everyone knows.

Known model thieves: NukeA.I (selling this model behind paywall on tensorart).

I wrote a story about it. Also contains a guide for trainers about "how to bake special trigger word into your model".

Usage:

Settings:

CFG: 1~3. This model has been distilled. You can disable CFG (CFG 1) and run the model 2x faster. Cover images are without CFG for demonstration. "RenormCFG" node is highly recommended if CFG is enabled (CFG > 1).

Steps: 16+

Prompt:

Always specify style, or use a style LoRA. Otherwise, you will get random/mixed style. This is a feature, not a bug. This model does not provide overfitted default style.

Quality tags:

It's recommended to omit ALL quality tags. The fine-tuning dataset has higher quality than "masterpiece". Thus they don't have noticeable effects. Omitting those redundant tokens allows LLM to pay more attention on other words

Training settings

All captions are NL from Google Gemini.

Optimizer: adamw, constant lr 0.00002, weight decay 0.1, batch size 16.

LoRA rank/alpha 24.

Timesteps shift 3.

Block 0-2 and adaln linear layers are skipped.