RDBT [Anima]
Personal finetuned model:
Comprehensive NL captions from LLM, instead of tags in random order.
0% glossy AI slop style in dataset. I handpicked every single image.
Guidance distilled to further improve quality. Also makes the model 3~4x faster.
No bias, no default style. You get exactly what you prompted/stacked.
I use it as a starting point to stack more style LoRAs.
See for update log. See for LoRA version (update more frequently).
Sharing merges using this model is not allowed. It has special trigger words (or tokens). I can test it very early. There is no false positive. Unfortunately, somebody might think I was joking, so I have no choice, but making a list: Known model thieves: NukeA.I (closed-weight merged model on tensorart),
This model is based on
Usage:
Settings:
CFG scale: 1~4. This model has been guidance distilled. You can disable CFG (CFG 1) and run the model 2x faster. Cover images are without CFG for demonstration.
Steps: 12~24. 12 steps is doable, but quality is not guaranteed as there is no step distillation. It's recommended to add 0.2x if you need lower steps (8~12).
Prompt
Specific style is required! This model does not provide a default style. You should always prompt specific style. Or use a style LoRA. Otherwise, you will get random/mixed style. This is a feature, not a bug.
Quality tags:
It's recommended to omit all the quality tags, or just keep the "masterpiece". Omitting those redundant tokens allows LLM to pay more attention on other words.
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.
Training:
Anima pretrained base ckpt -> ~10k general images finetuning -> guidance distillation.
All captions are NL from Google Gemini.
Optimizer: adamw, constant lr 0.00002.
LoRA rank/alpha 24.
Guidance distillation target CFG 4.
Block 0-2 and adaln linear layers are skipped.