v1.0: I think it's time to release v1.0. I've migrated to the krea2 model (never imagined that krea2 would be my next choice...). Fine-tuning the anima model is really fun.

I also uploaded int8 version for ComfyUI hardware int8 (labeled as fp8).

About 8/16 step distillation:

• 8-step has higher stability. Easy to use and prompt.

• 16-step (same as previous old versions) has higher diversity and more dynamic details, less sloppy but less stable, more small errors. (I still prefer this one because anime/art model needs a little bit creative and chaotic.)

• Comparison images are uploaded as 16-step version showcase images. You can feel the "complexity" of 16-step is noticeable higher than 8-step.

• FYI: More distillation = higher stability = lower steps = lower diversity/complexity = more slop-ish. For reference, official anima turbo is 4-step distillation, means highest stability and lowest diversity.

Interesting old version:

• b1 v0.39.b: compared to v1.0 16-step, more creative, more dynamic details, but more logical errors. Good for creative, abstract effect, etc.

• b1 v0.37.1: 2-stage finetuned, highest aesthetic among all versions, but due to small dataset it has bias. Also has several built-in quality/style trigger words, see update log "b1 v0.32".

EDIT: Why Krea2?

• It knows everything. Default quality is off the roof already. (Of course, it's a large 12B commercial model.)

• It knows anime/art design, even knows popular characters. (Means it has been trained on a much more creative art dataset. Previous open source models didn't, they mainly for photorealistic)

• Official turbo model, 8-step. (FYI: Turbo model is as fast as non-turbo 20-step Anima.)

• If you want anime art. All you need is a style/character LoRA.

• Life is much easier...

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.

It's not overfitted and doesn't have a default 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)

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 scale: 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 recommended

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.