v0.29: I changed a lot of things, from the dataset to the distillation algorithm, so v0.29 is very different from v0.27. In short: Maximum details. This version can squeeze every single pixel out of the VAE. No more blurry or simplified output. The file size of output images are 2x bigger than v0.27, even bigger than some images from qwen-image. (means more info/details). Also increased diversity. v0.27 is too stable. It always gives me backgrounds with symmetrical structures. Not ideal. See Update Log section for version info. v0.27: Improved stability (might be too stable) and details/textures. FYI: The model has lower contrast for a mathematical reason for better details. I know many people complain about this. If you want to boost contrast, I recommend post-editing. This model prioritizes "aesthetics" over "diversity" and "speed". They can't be achieved simultaneously. See Update Log section for all versions. 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 (commonly known as "turbo") 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. Usage: Below are differences between this model and vanilla Anima. Settings: Sampler: "euler_a" "euler" "er_sde". (from smooth to high variance) Steps: 8, 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). Prompt Prefer natural language prompt. Prompt structure: style, subject, action, background. This model does not provide default style. You should always prompt the style first. 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 Made by - reakaakasky https://civitai.com/models/2356447/rdbt-or-anima