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    Kirazuri (Anima) - v3.0 [anima-base-1]
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    Kirazuri (Anima)

    Kirazuri (Anima) 3.0 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 1024^24, 1280^2, and 1536^2 resolutions

    Version 3.0 (Latest)

    For in-depth details of version 3.0 training and tooling, see: Kirazuri (Anima) 3.0 Training Diary

    Training Details Summary

    Trainer: diffusion-pipe commit b0aa4f1e03169f3280c8518d37570a448420f8be

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

    Total training time: ~10 days

    Total samples seen(unbatched steps): ~2,550,000

    Training resolutions:

    • 512^2

    • 768^2

    • 1024^2

    • 1280^2

    • 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

    Stage 2

    • Samples seen(unbatched steps): ~550,000

    • Training time: ~118 hrs

    • Learning Rate: 3e-6

    • Learning Rate Scheduler: Cosine

    • LLM Adaptor Learning Rate: 0

    • Flux Shift: Enabled

    • Multi-Scale Loss Weight: 0.5

    • Precision: Mixed BF16

    • Optimizer: AdamW8bit with Kahan Summation

    • Weight Decay: 0.01

    • Timestep Sampling Strategy: Logit-Normal

    Additional Features

    • 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) v2.0

    • Dataset includes recently curated 7,071 images increasing total size from 35,537 to 42,608 images

    • Dataset cutoff now of 2026/05/12.

    • Trained at 5 total resolutions in two-stage training

      • Stage 1 - 512^2, 768^2, 1024^2

      • Stage 2 - 1024^2, 1280^2 1536^2

    • Introduced cosine learning rate scheduler for smooth learning rate transition between training stages

    • Re-captioned full dataset for a second natural language captions variant with updated captioning script

    Installing and running

    Workflow:

    Reference the anima preview 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:

    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.

    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

    [Optional] ComfyUI-Autocomplete-Plus prompt input assistance

    An optional file danbooru_tags_kirazuri_3.txt is included with the version 3.0 model details.

    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_3.csv and place it in your ComfyUI/custom_nodes/comfyui-autocomplete-plus/data directory.

    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 Preview base model.

    • 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

    FAQ

    Checkpoint
    Anima
    by motimalu
    Download (Beta) View on CivitAI
    digital illustration
    video games
    base model
    anima
    manga
    anime
    aesthetic

    Details

    Downloads
    611
    Platform
    CivitAI
    Platform Status
    Available
    Created
    6/11/2026
    Updated
    6/12/2026
    Deleted
    -
    Trigger Words:
    masterpiece
    best quality
    very aesthetic

    Files

    kirazuriAnima_v30AnimaBase1_txt.safetensors

    Size:
    1.11 GB
    SHA256:
    cd2a512003e2f9f3cd3c32a9c3573f820bb28c940f73c57b1ddaa983d9223eba

    Mirrors

    HuggingFace (59 mirrors)
    CivitAI (33 mirrors)
    ModelScope CN (2 mirrors)

    kirazuriAnima_v30AnimaBase1.txt

    Size:
    494.30 KB
    SHA256:
    bc036321336581420b6085b64903c1d253e71ff2331d21b84e3207ae00700cf9

    Mirrors

    CivitAI (1 mirrors)

    kirazuriAnima_v30AnimaBase1.safetensors

    Size:
    3.89 GB
    SHA256:
    3679ed7dc092b0c85c1b4b8d36b1741149a8ebcb0be832f71aeb801fe1d50003

    Mirrors

    HuggingFace (1 mirrors)
    CivitAI (1 mirrors)

    kirazuriAnima_v30AnimaBase1.txt

    Size:
    644.74 KB
    SHA256:
    8950c198eb5edc8c66fd6d4d28b8fd66bff6a2cf46b50a23fc88673965b800b0

    Mirrors

    CivitAI (1 mirrors)

    qwen_image_vae.safetensors

    Size:
    242.05 MB
    SHA256:
    a70580f0213e67967ee9c95f05bb400e8fb08307e017a924bf3441223e023d1f

    Mirrors

    HuggingFace (126 mirrors)
    CivitAI (41 mirrors)
    TensorFiles (1 mirrors)
    ModelScope (1 mirrors)
    ModelScope CN (5 mirrors)