RDBT [Anima]
Finetuned + distilled model. Doesn't have a default style. I use it to stack style LoRAs.
See Update Log section for version info. See this page for LoRA version.
All cover images are "raw" output, 1024px, no editing/upscale etc. Metadata included.
Sharing merges using this model is not allowed. It has special trigger words. There is no false positive. Known model thieves: NukeA.I (closed-weight on tensorart)
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.
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. I use this model as a starting point to stack more style LoRA.
(v0.32+) There are some "roughly classified" trigger words, they are trained so they have effect, but they are not "specific style":
@anime sketch: Low complexity. Rough outlines.
@digital anime illustration: Typical "anime". Clear and fine outlines. General complexity.
@digital art: More complex lighting, textures than typical "anime".
@cinematic digital art: More lighting, postprocess effects, semi-realistic, etc.
Quality tags:
It's recommended to omit all the quality tags, or just keep the "masterpiece".
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.
Omitting those redundant tokens also allows LLM to better focus its attention on other words.
Update Logs
(5/18/2026): b1 v0.35:
No step distillation. Just guidance distilled.
I'm dropping step distillation. Anima official has their plan to do step distillation (aka, turbo, 4/8-step, or whatever). They have the money and recourse and full dataset. I don't. And my cheap step distillation is kind of sh*tty, tbh.
If you need higher stability or speed, you can stack the extracted cosmos dmd2 lora the anima-turbo, basically can achieve the same thing, probably even better. I prefer 0.2x cosmos dmd2 lora.
(5/12/2026): p3 v0.32.b:
Less step distillation (means higher diversity but less stability). 12 steps is still doable, 24 steps is recommended for complicated prompt.
Styles reinforcement learning. I did this in v0.29, but not in v0.32.
(5/10/2026): p3 v0.32:
No more green-ish, color shifting.
Trigger words have been reclassified to avoid model learning a unified style. See updated "Usage" section.
Old trigger words for backup (v0.29 and before):
"digital anime illustration": common 2d anime.
"digital art", 2d art but not anime, mostly digital art.
"anime sketch": simplified/unfinished anime drawing.
(4/27/2026): p3 v0.29: Distillation algorithm was almost completely rewritten.
Increased diversity. This also improved lighting range, styles and LoRA compatibility.
Better details. This version can squeeze every single pixel out of the VAE.
(4/23/2026) p3 v0.27: Improved stability, details.
(4/18/2026) p3 v0.25: It's based on anima p3.
Previous testing versions, see this page
Description
FAQ
Comments (39)
We might finally have the "Illustrious killer"
Good to see NoobAI's approach of focusing on artist names.
Model is still in training, so results are can suffer from bad anatomy or inconsistency. Also artist names are not an addition like with Illustrious, but almost a requirement to get good results. Use them like @{artist name}.
This model has zero involvement with the NoobAI team!
@NamelessKing2 I know, if I am not mistaken, it is one guy. I am just glad that shizo tagging of PonyXL is not standard
As for now it just feels like much worse, but easier to use and faster newbie image.
it's not bad but a bit disapointing with output quality
Prompt adherence is just insane!
Getting great results ♪(^∇^*). (it's uncensored btw, understands a lot of concepts, better than lumina)
Hope this model gonna hype up, cause it is a potential Illustrious killer.
On my 3080 (12 gb), 30 steps it takes 22 sec per image generation
Uhm... where is the patch? i m missing something?
it is for old GPUs like turing architecture, if have 30 series or newer than you not need to worry about it.
I tried it, and placed the file in this directory: ComfyUI\custom_nodes\anina_fp16_patch.py
@krigeta I have an rtx 2060...so yea
this patch is good for amd gpu's as well
The model is a beast, fast, real good quality even on base res gens without hires, very good prompt adherence, more artist and character knowledge than Illus and nsfw capable with regular language and danbooru tags, and this only the preview model lol.
Thank you. Where do I put the patch file?
I tried it, and placed the file in this directory: ComfyUI\custom_nodes\anina_fp16_patch.py
lora training and controlnet and we got the best anime model right here of 2026.
I dont understand, what are those files? Is circlestone labs released fp16 model?
Hello, can you also add fp16 patch to z image base?
read on comfyui github he did a patch for z-image but can't find it now
@xpnrt that patch no longer works.
@3059714361 no, z image base is way more f**ked up than turbo. Seems they continue training the model, a lot. I guess the "base" mode we see right here, is not the real base model of turbo.
Holy this model is insanely good also MOSTY follow your prompt
The model is great! :3 can't waut to see others use it!
Can someone please explain why I'm getting a mismatch with the suggested text encoder? Despite using Qwen_3_06B_base, I still get "Error(s) in loading state_dict for Qwen3_4B: size mismatch for model.embed_tokens.weight: copying a param with shape torch.Size([151936, 1024]) from checkpoint, the shape in the current model is torch.Size([151936, 2560])."
I've tried both anima_preview and animaFP8_preview, as well as changing the various load clip types (Stable Diffusion, Cosmos, Qwen Image). Thanks in advance for any response.
Update your comfyui?
@ZootAllures9111 Thanks for the reply.
After updating comfyui, everything worked as it should 🤷.
FYI, the FP16 patch will not be faster on 1650 / 1660 series Turing GPUS, they don't have proper FP16 support.
Thank you so much for creating this patch! However, I encountered an issue during sampling: expected mat1 and mat2 to have the same dtype, but got: float != struct c10::BFloat16. > Does this patch only support FP8? I am currently using the original version of Anima. If that’s the case, I will switch to the FP8 version instead. 🥹💕
I initially encountered a similar issue; simply removing the UNET settings in FP16 resolved it.
Perhaps it conflicted with a patch, so restoring the default settings might be necessary.
@dawn66666666 Thank you for your reply! How should I go about deleting the 'fp16 unet setting'? I'm so sorry, I’m not very tech-savvy with this. 🥹
@shigjfg I apologize, I'm not an English user, and I'm not sure if the following translation will be accurate. In my ComfyUI launcher, there's an advanced option called "Calculation Precision Settings," where you can set "fp16, bf16, fp8, or determined by ComfyUI."
@dawn66666666 okay,thank you very much for your answer.❤️
The patch made my anima gens 6x faster, thx man
Thanks, this greatly improved the speed of my Tesla V100; it now takes about 35 seconds.
It used to take 130 seconds.
It's a f***ing amazing model! Know a lot of characters, A LOT of styles/authors, follow the prompt, very fast and lightweight....
Well, my job is done here. Why should I retrain all my styles for ZIT/Klein when this model perfectly replicate almost all of them out of the box, and it's not even a final version?
12/10 for model, and HUGE respect for a company that produced it.
You said it, it's killing creators and you're happy about it 😂🤣
@GlowingGuardianGirl There are still many things that this model lacks, so there is room for improvement. Because booru-dataset contains... mostly very specific artists.
But yes, the ideal model is one that can do everything out of the box and does not require additional training =)
Dumb question,where should I put the patch to apply it?
# How to use:
# Put this file in the ComfyUI "custom_nodes" dir.
# Use "ModelComputeDtype" node and set dtype to "fp16".
# To disable the patch, remove the file, or rename the ".py" suffix, to something
# like ".disable", whatever.
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