RDBT [Anima]

This is a finetuned model with 10k images paired with natural language captions from LLM. Then distilled to further improve quality and stability.

This model is used by several artists to assist in drawing. All images are handpicked, finished, high aesthetic and accurate anatomy. Dataset does not contain any shiny plastic glossy AI image (not contaminated by glossy AI style and fixed AI faces).

Because the dataset is big enough, it was not overfitted, and does not has default style (no bias). Still as creative as the pretrained model. It's a clean starting point for style LoRAs. You can stack whatever you want and get exactly you stacked.

Compared with the pretrained anima model, this finetuned model has better prompt adherence, more stable composition, fewer logical errors, better lighting, background, detail, ~4x faster.

See this page for update log and version info.

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

For ComfyUI users: Int8convrot version is recommended.

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+ (some versions can be 8)

• Sampler: euler (er_sde usually is not recommended for distilled model)

Prompt:

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

Quality tags:

Omit ALL quality tags. You don't need those. The fine-tuning dataset has higher quality than "masterpiece". Thus quality tags don't have effects. Omitting those redundant tokens allows LLM to pay more attention on other words.

Misc.

Base model:

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

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

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.

I don't see any effect:

This is not a style model which is overfitted to a style and can change the image drastically. Because the dataset is big and diverse enough, this model was not overfitted and does not have (noticeable) bias.