Kirazuri (Anima)

Kirazuri (Anima) is a full fine-tune of the Anima Base v1.0 model by CircleStone Labs focused on several goals:

• Learn new concepts/styles/characters past the base model dataset cutoff of 2025 September

• Enhance the model aesthetic guided by manually applied quality, aesthetic, and style tagging

• Improve rendering and understanding of fine-details through high-resolution training for 1024^2, 1280^2, and 1536^2 resolutions

Version 4.0 (Latest)

For in-depth details of training and tooling, see:

• Kirazuri (Anima) 4.0 Training Diary

• Kirazuri (Anima) 3.0 Training Diary

Training Details Summary

Trainer: diffusion-pipe

Training device: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition

Total training time: ~10 days

Total samples seen(unbatched steps): ~3,000,000

Training resolutions:

• 512^2

• 768^2

• 1024^2

• 1536^2

Stage 1

• Samples seen(unbatched steps): ~2,000,000

• Training time: ~125 hrs

• Learning Rate: 6e-6

• Learning Rate Scheduler: Cosine

• LLM Adaptor Learning Rate: 8e-7

• Precision: Mixed BF16

• Optimizer: AdamW8bit with Kahan Summation

• Weight Decay: 0.01

• Timestep Sampling Strategy: Logit-Normal

• Training Resolutions: 512^2, 768^2, 1024^2

Stage 2

• Samples seen(unbatched steps): ~1,000,000

• Training time: ~84 hrs

• Learning Rate: 2e-6

• Learning Rate Scheduler: Cosine

• LLM Adaptor Learning Rate: 2e-7

• Precision: Mixed BF16

• Optimizer: AdamW8bit with Kahan Summation

• Weight Decay: 0.01

• Timestep Sampling Strategy: Logit-Normal

• Training Resolutions: 512^2, 1024^2, 1536^2

Additional Features

• Masked Training

• Tag Dropout: 30% with protected first 8 tags

• Tag Shuffle: Applied to last unprotected tags

• Natural Language: Short and Long Caption variants

Changes from Kirazuri (Anima) v3.0

• Dataset includes recently curated 2,450 images increasing total size from 42,608 to 45,058 images

• Dataset cutoff now of 29/06/2026

• Introduced Masked Training for images with simple backgrounds

• Updated tags+caption variants structure

Installing and running

Workflow:

Reference the anima base instructions. The model is natively supported in ComfyUI. The above image contains a workflow; you can open it in ComfyUI or drag-and-drop to get the workflow.

Note: Most preview images on the model card additionally use the custom comfyui-prompt-control node for schedule prompting syntax to mix concepts i.e. [word1|word2]
This custom node is entirely optional but required to exactly recreate the outputs in ComfyUI.

The model files go in their respective folders inside your model directory:

• anima-kirazuri-v4.safetensors (this model) goes in ComfyUI/models/diffusion_models

• qwen_3_06b_base.safetensors goes in ComfyUI/models/text_encoders

• qwen_image_vae.safetensors goes in ComfyUI/models/vae (this is the Qwen-Image VAE, you might already have it)

Quantizations

Int8 supported with latest ComfyUI:

• anima-kirazuri-v4-int8-convrot.safetensors

• anima-kirazuri-v3-int8convrot.safetensors

Distillations

4 Step CFG 1 Turbo LoRA distilled with DP-DMD - diversity-preserved few-step distillation:

• kirazuri-anima-turbo

Generation Settings

Trained in mixed resolutions for the majority of training, and finished with dedicated high resolution training.

Previews are generated mostly at 1280^2 e.g. 1520x1040 or 1536^2 e.g. 1248x1824 resolutions.

30-50 steps, CFG 4-5.

Same samplers as recommended for the base model work, I like to use:

• er_sde: the recommended default for 30-50 steps.

• sa_solver_pece: can converge with good detail in 15-20 steps.

[Optional] ComfyUI-Autocomplete-Plus prompt input assistance

An optional file danbooru_tags_kirazuri_4.txt is included to provide prompt tag autocomplete.

This file contains metadata that is derived from public sources for prompt assistance only, and is intended to be used with the ComfyUI-Autocomplete-Plus extension.

Rename the file to danbooru_tags_kirazuri_4.csv and place it in your ComfyUI/custom_nodes/comfyui-autocomplete-plus/data directory.

You should see suggestions when writing comma separated tags in ComfyUI:

Prompting

Like the base model, this model is trained on booru-style tags, natural language captions, and combinations of tags and captions.

Tag order

[quality/meta/safety tags] [character] [series] [artist] [1girl/1boy/1other etc] [general tags]

Mostly the same order as the base model, only the [1girl/1boy/other etc] groups position is towards the end in this models dataset.

[quality/meta/safety tags] [character] [series] [artist] tag groups are also not shuffled, so their order may have some influence on generations.

Quality and Aesthetic tags

Human score based: masterpiece, best quality, very aesthetic, aesthetic

The very aesthetic and aesthetic tags are where this model diverges from the base, with the intent these can be used to guide the model toward a different aesthetic - a kind of house model bias.

Meta tags

absurdres, official art, etc

Styles

painterly, chiaroscuro, ligne claire, flat color, no lineart, blending, etc

traditional media, oil painting \(medium\), watercolor \(medium\), etc

Known Limitations & Issues:

Some concept bleeding and instability is noticeable when using short prompts, especially tag-only prompts.

Longer tag strings and natural language prompts describing the image in detail should help with this.

This reflects how the model was trained with a combination of natural language and tags.

Recognitions

• Thanks to CircleStone Labs for the Anima base models.

• Thanks to tdrussell of CircleStone Labs for the diffusion-pipe trainer.

• Thanks to bluvoll for support using their fork of diffusion-pipe.

• Thanks to narugo1992 and the deepghs team for open-sourcing various training sets, image processing tools, and models.

License

This model is released under the same license as the base model.

See the base model for details of the CircleStone Labs Non-Commercial License.

Built on NVIDIA Cosmos