<h1 id="kirazuri-(anima)">Kirazuri (Anima) </h1>Kirazuri (Anima) 3.0 is a full fine-tune of the <a target="_blank" rel="ugc" href="https://huggingface.co/circlestone-labs/Anima/blob/main/split_files/diffusion_models/anima-base-v1.0.safetensors">Anima Base v1.0 model by CircleStone Labs</a> focused on several goals:<ul><li>Learn new concepts/styles/characters past the base model dataset cutoff of 2025 September</li><li>Enhance the model aesthetic guided by manually applied quality, aesthetic, and style tagging</li><li>Improve rendering and understanding of fine-details through high-resolution training for 1024^24, 1280^2, and 1536^2 resolutions</li></ul><h2 id="version-3.0-(latest)">Version 3.0 (Latest)</h2>For in-depth details of version 3.0 training and tooling, see: <a target="_blank" rel="ugc" href="https://github.com/motimalu/diffusion-training-configs/blob/main/diffusion-pipe/anima/notes/kirazuri3.0-notes.md">Kirazuri (Anima) 3.0 Training Diary</a><h3 id="training-details-summary">Training Details Summary</h3>Trainer: <a target="_blank" rel="ugc" href="https://github.com/tdrussell/diffusion-pipe">diffusion-pipe</a> <a target="_blank" rel="ugc" href="https://github.com/tdrussell/diffusion-pipe/commit/b0aa4f1e03169f3280c8518d37570a448420f8be">commit b0aa4f1e03169f3280c8518d37570a448420f8be</a>Training device: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation EditionTotal training time: ~10 daysTotal samples seen(unbatched steps): ~2,550,000Training resolutions:<ul><li>512^2</li><li>768^2</li><li>1024^2</li><li>1280^2</li><li>1536^2</li></ul><h3 id="stage-1">Stage 1</h3><ul><li>Samples seen(unbatched steps): ~2,000,000</li><li>Training time: ~125 hrs</li><li>Learning Rate: 6e-6</li><li>Learning Rate Scheduler: Cosine</li><li>LLM Adaptor Learning Rate: 8e-7</li><li>Precision: Mixed BF16</li><li>Optimizer: AdamW8bit with Kahan Summation</li><li>Weight Decay: 0.01</li><li>Timestep Sampling Strategy: Logit-Normal</li></ul><h3 id="stage-2">Stage 2</h3><ul><li>Samples seen(unbatched steps): ~550,000</li><li>Training time: ~118 hrs</li><li>Learning Rate: 3e-6</li><li>Learning Rate Scheduler: Cosine</li><li>LLM Adaptor Learning Rate: 0</li><li>Flux Shift: Enabled</li><li>Multi-Scale Loss Weight: 0.5</li><li>Precision: Mixed BF16</li><li>Optimizer: AdamW8bit with Kahan Summation</li><li>Weight Decay: 0.01</li><li>Timestep Sampling Strategy: Logit-Normal</li></ul><h3 id="additional-features">Additional Features</h3><ul><li>Tag Dropout: 30% with protected first 8 tags</li><li>Tag Shuffle: Applied to last unprotected tags</li><li>Natural Language: Short and Long Caption variants</li></ul><h2 id="changes-from-kirazuri-(anima)-v2.0">Changes from Kirazuri (Anima) v2.0</h2><ul><li>Dataset includes recently curated 7,071 images increasing total size from 35,537 to 42,608 images</li><li>Dataset cutoff now of 2026/05/12.</li><li>Trained at 5 total resolutions in two-stage training<ul><li>Stage 1 - 512^2, 768^2, 1024^2</li><li>Stage 2 - 1024^2, 1280^2 1536^2</li></ul></li><li>Introduced cosine learning rate scheduler for smooth learning rate transition between training stages</li><li>Re-captioned full dataset for a second natural language captions variant with updated captioning script</li></ul>Installing and runningWorkflow:<img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/9c85d9a8-e9a3-4dd0-8403-c87d0e3d5092/original=true/9c85d9a8-e9a3-4dd0-8403-c87d0e3d5092.jpeg" />Reference the <a target="_blank" rel="ugc" href="https://huggingface.co/circlestone-labs/Anima#installing-and-running">anima preview base instructions</a>. 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 <a target="_blank" rel="ugc" href="https://github.com/asagi4/comfyui-prompt-control">comfyui-prompt-control</a> node for schedule prompting syntax to mix concepts i.e. <code>[word1|word2]</code> 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:<ul><li><a target="_blank" rel="ugc" href="https://civarchive.com/models/2495369">anima-kirazuri-v3.safetensors</a><a target="_blank" rel="ugc" href="https://civarchive.com/models/2495369"> </a>(this model) goes in <code>ComfyUI/models/diffusion_models</code></li><li><a target="_blank" rel="ugc" href="https://huggingface.co/circlestone-labs/Anima/tree/main/split_files/text_encoders">qwen_3_06b_base.safetensors</a> goes in <code>ComfyUI/models/text_encoders</code></li><li><a target="_blank" rel="ugc" href="https://huggingface.co/circlestone-labs/Anima/tree/main/split_files/vae">qwen_image_vae.safetensors</a> goes in <code>ComfyUI/models/vae</code> (this is the Qwen-Image VAE, you might already have it)</li></ul>Generation SettingsTrained 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:<ul><li>er_sde: the recommended default for 30-50 steps.</li><li>sa_solver_pece: can converge with good detail in 15-20 steps.</li></ul>PromptingLike the base model, this model is trained on booru-style tags, natural language captions, and combinations of tags and captions.Tag order<code>[quality/meta/safety tags] [character] [series] [artist] [1girl/1boy/1other etc] [general tags]</code>Mostly the same order as the base model, only the <code>[1girl/1boy/other etc]</code> groups position is towards the end in this models dataset.<code>[quality/meta/safety tags] [character] [series] [artist]</code> tag groups are also not shuffled, so their order may have some influence on generations.Quality and Aesthetic tagsHuman score based: <code>masterpiece, best quality, very aesthetic, aesthetic</code>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 tagsabsurdres, official art, etcStylespainterly, chiaroscuro, ligne claire, flat color, no lineart, blending, etctraditional media, oil painting \(medium\), watercolor \(medium\), etc[Optional] ComfyUI-Autocomplete-Plus prompt input assistanceAn optional file <code>danbooru_tags_kirazuri_3.txt</code> 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 <a target="_blank" rel="ugc" href="https://github.com/newtextdoc1111/ComfyUI-Autocomplete-Plus">ComfyUI-Autocomplete-Plus</a> extension.Rename the file to <code>danbooru_tags_kirazuri_3.csv</code> and place it in your <code>ComfyUI/custom_nodes/comfyui-autocomplete-plus/data</code> directory.Known Limitations &amp; 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<ul><li>Thanks to <a target="_blank" rel="ugc" href="https://civarchive.com/user/circlestone_labs">CircleStone Labs</a> for the Anima Preview base model.</li><li>Thanks to tdrussell of CircleStone Labs for the <a target="_blank" rel="ugc" href="https://github.com/tdrussell/diffusion-pipe">diffusion-pipe</a> trainer.</li><li>Thanks to bluvoll for support using their fork of diffusion-pipe.</li><li>Thanks to narugo1992 and the deepghs team for open-sourcing various training sets, image processing tools, and models.</li></ul>LicenseThis model is released under the same license as the base model.See the base model for details of the <a target="_blank" rel="ugc" href="https://huggingface.co/circlestone-labs/Anima/blob/main/LICENSE.md">CircleStone Labs Non-Commercial License.</a> Built on NVIDIA Cosmos

Kirazuri (Anima)

<a target="_blank" rel="ugc" href="https://github.com/motimalu/diffusion-training-configs/blob/main/diffusion-pipe/anima/notes/kirazuri3.0-notes.md">Kirazuri (Anima) 3.0 Training Diary</a>

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