Anima — Karume

What is this

circlestone-labs/Anima-Base-v1.0-Diffusers — and community fine-tunes of it — converted into the WebGPU inference runtime Karume's container format (a single safetensors file = weights + a graph JSON embedded in __metadata__). Runs as-is in the browser and in Deno.

Ordinary many-step sampling — 20 steps / guidance 4 by default. Classifier-free guidance is on, which is what makes the negative prompt take effect. For a few-step distilled build see hdae/karume-anima-turbo.
Not readable by diffusers (it's a different container with an embedded graph); the reader is a pipeline that implements anima/1.
Exporter used for the conversion: karume/0.5.0. The distribution manifest is karume.json (karume/4).

Models and their origins

Each model below is either the CircleStone Anima base model itself or a community fine-tune of it. The text encoder, VAE and tokenizers are shared across them.

`anima-v1.0` — Anima Base v1.0

Author: circlestone_labs
Source: https://huggingface.co/circlestone-labs/Anima-Base-v1.0-Diffusers

`anima-wai-v1.0` — WAI-ANIMA v1.0 (base 1.0)

Author: WAI0731
Source: https://civitai.com/models/2544636/wai-anima
Converted from: waiANIMA_v10Base10.safetensors

Permissions listed on the source page (as of 2026-08-22):

allowNoCredit: true
allowCommercialUse: Image / RentCivit
allowDerivatives: true
allowDifferentLicense: true

`anima-copycat-20260610` — copycat-anima 20260610

Author: calculater
Source: https://civitai.com/models/2377376/copycat-anima
Converted from: copycatAnima_20260610.safetensors

Permissions listed on the source page (as of 2026-08-22):

allowNoCredit: true
allowCommercialUse: Image / RentCivit
allowDerivatives: true
allowDifferentLicense: false

License

Every model here derives from the CircleStone Anima base model and stays under the CircleStone Non-Commercial License (non-commercial use only). This repository ships LICENSE.md (the full license text) and NOTICE.md (this attribution plus the list of modifications).

The CircleStone Model is licensed by CircleStone Labs LLC under the CircleStone Non-Commercial License. Copyright CircleStone Labs LLC. IN NO EVENT SHALL CIRCLESTONE LABS LLC BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH USE OF THIS MODEL.

The community fine-tunes are redistributed under the permissions their source pages state (listed per model above); those permissions do not widen the base model's license.
anima-copycat-20260610: the source page sets allowDifferentLicense to false, so this redistribution keeps the same terms — do not relicense it.
This is not an official product of CircleStone Labs LLC, and it is not endorsed, approved or validated by CircleStone Labs LLC.

Models

Model	Pipeline	Quants	Default quant
`anima-v1.0` (default)	`anima/1`	`f16` / `f16+dit8` / `f16+dit8-a8` / `f16+dit8-a8-attn8` / `f16+dit8-a8-attn8-s16` / `f16-c16`	`f16+dit8-a8-attn8-s16`
`anima-wai-v1.0`	`anima/1`	`f16` / `f16+dit8` / `f16+dit8-a8` / `f16+dit8-a8-attn8` / `f16+dit8-a8-attn8-s16` / `f16-c16`	`f16+dit8-a8-attn8-s16`
`anima-copycat-20260610`	`anima/1`	`f16` / `f16+dit8` / `f16+dit8-a8` / `f16+dit8-a8-attn8` / `f16+dit8-a8-attn8-s16` / `f16-c16`	`f16+dit8-a8-attn8-s16`

model selects one of these; omitted, it is anima-v1.0. quant defaults to that model's own default quant.

Usage

import { AnimaPipeline, encodePng } from "jsr:@karume/models";

using pipeline = await AnimaPipeline.fromPretrained({
  repo: "hdae/karume-anima",
  // Pin a commit for reproducible builds — without it you track `main`, and a future
  // repo update (renamed files, new manifest format) may break your app.
  // Copy the full hash from this repo's "Files and versions" tab:
  // revision: "<full commit sha>",
}, {
  // model: "anima-v1.0", // default — available: anima-copycat-20260610 / anima-v1.0 / anima-wai-v1.0
  // quant: "f16+dit8-a8-attn8-s16", // default — available: f16 / f16+dit8 / f16+dit8-a8 / f16+dit8-a8-attn8 / f16+dit8-a8-attn8-s16 / f16-c16
});

const image = await pipeline.generate({
  prompt: "1girl, solo, long hair, blue eyes, school uniform, masterpiece",

  // steps: 20, // default — more steps trade time for detail
  // Resolution — non-square is fine; each side on a 16 px grid, between 512 and 2048 px:
  // resolution: { width: 1024, height: 1024 }, // default

  // Classifier-free guidance runs a second (uncond) branch — twice the work per step.
  // It is on by default here, which is what makes the negative prompt take effect:
  // guidanceScale: 4, // default
  // negativePrompt: "low quality, worst quality, blurry, bad anatomy, jpeg artifacts", // default

  seed: 42, // same seed + same request → same image
});

const png = await encodePng(image.data, image.width, image.height);
await Deno.writeFile("anima.png", png);

Weights are fetched once and cached (verified against karume.json's size / sha256). You can also load from a local directory (AnimaPipeline.fromAssets).

Model: anima-v1.0

Files

Key	Dtype	Path	Size	sha256
`text_encoder`	f16	`shared/text_encoder/model.safetensors`	1.11 GiB (1,194,220,916 B)	`4ae7c810de0edfbd…`
`text_conditioner`	f16	`anima-v1.0/text_conditioner/model.safetensors`	257.33 MiB (269,834,156 B)	`1509602ac1413354…`
`transformer`	f16	`anima-v1.0/transformer/model.f16.safetensors`	3.64 GiB (3,913,609,588 B)	`02cbc3fab2fc5008…`
`transformer`	i8	`anima-v1.0/transformer/model.i8.safetensors`	1.83 GiB (1,962,502,636 B)	`a86e038f09c85525…`
`transformer.rope_base`	f16 / i8	`shared/transformer/rope_base.safetensors`	64.42 KiB (65,968 B)	`42db9a3fc796c45f…`
`vae_decoder`	f16	`shared/vae_decoder/model.safetensors`	48.37 MiB (50,720,688 B)	`b50b65a028a8d108…`
`tokenizer`	—	`shared/tokenizer/qwen2-tokenizer.json`	3.35 MiB (3,514,619 B)	`0a7d6057ac8a2fe4…`
`tokenizer_2`	—	`shared/tokenizer_2/t5-tokenizer.json`	1.04 MiB (1,093,419 B)	`f86dfe21b12a175a…`

Only the first 16 hex digits of the sha256 are shown (the full value and size live in karume.json — verify against that at the fetch layer). Dtype labels use the runtime's storage dtype vocabulary (f16 / i8 / i4), not the fp16 spelling common elsewhere in the ecosystem. A path under shared/ is one this model shares byte for byte with another model in this repository (it is fetched and cached once).

Quants

Quant	What it is	Weights	Compute
`f16`	Full quality (f16) — Transformer in f16 storage with f32 compute — the largest download, and the reference the other quants here are judged against.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `f16` / `vae_decoder` = `f16`	—
`f16+dit8`	Half size (int8 transformer) — Transformer stored as int8 and computed in f32: roughly half its f16 download, with the execution path left unchanged.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `i8` / `vae_decoder` = `f16`	—
`f16+dit8-a8`	Half size, int8 linear — The int8 transformer with per-token int8 activations in its linear layers — faster on GPUs with dp4a, same download.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `i8` / `vae_decoder` = `f16`	`linearCompute` = `a8`
`f16+dit8-a8-attn8`	Half size, int8 linear and attention — Adds int8 activations inside attention on top of the int8 linear path; same weights, one more integer stage per step.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `i8` / `vae_decoder` = `f16`	`linearCompute` = `a8` / `attentionCompute` = `a8`
`f16+dit8-a8-attn8-s16` (default)	Balanced (int8) — The int8 linear and attention path with attention scores held in f16 — the fastest of the int8 seats here, at f16-level image quality.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `i8` / `vae_decoder` = `f16`	`linearCompute` = `a8` / `attentionCompute` = `a8` / `attentionScoreStorage` = `f16`
`f16-c16`	Full quality, f16 compute — f16 storage computed in f16 throughout. Needs the shader-f16 GPU feature, and trades numerical headroom for speed.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `f16` / `vae_decoder` = `f16`	`linearCompute` = `f16` / `attentionCompute` = `f16` / requires `shaderF16`

If no quant is given, it runs as f16+dit8-a8-attn8-s16 (this model's recommended default). In a quant name, dit is the transformer component.

Defaults

Any knob not passed to generate() is filled in from the manifest's defaults.

steps: 20
guidanceScale: 4
resolution: 1024 × 1024
negativePrompt: low quality, worst quality, blurry, bad anatomy, jpeg artifacts

Model: anima-wai-v1.0

Files

Key	Dtype	Path	Size	sha256
`text_encoder`	f16	`shared/text_encoder/model.safetensors`	1.11 GiB (1,194,220,916 B)	`4ae7c810de0edfbd…`
`text_conditioner`	f16	`anima-wai-v1.0/text_conditioner/model.safetensors`	257.33 MiB (269,834,156 B)	`2342e0d63fa15d63…`
`transformer`	f16	`anima-wai-v1.0/transformer/model.f16.safetensors`	3.64 GiB (3,913,609,588 B)	`c428facf80e3e325…`
`transformer`	i8	`anima-wai-v1.0/transformer/model.i8.safetensors`	1.83 GiB (1,962,502,636 B)	`c9204f16686b3c0d…`
`transformer.rope_base`	f16 / i8	`shared/transformer/rope_base.safetensors`	64.42 KiB (65,968 B)	`42db9a3fc796c45f…`
`vae_decoder`	f16	`shared/vae_decoder/model.safetensors`	48.37 MiB (50,720,688 B)	`b50b65a028a8d108…`
`tokenizer`	—	`shared/tokenizer/qwen2-tokenizer.json`	3.35 MiB (3,514,619 B)	`0a7d6057ac8a2fe4…`
`tokenizer_2`	—	`shared/tokenizer_2/t5-tokenizer.json`	1.04 MiB (1,093,419 B)	`f86dfe21b12a175a…`

Quants

Quant	What it is	Weights	Compute
`f16`	Full quality (f16) — Transformer in f16 storage with f32 compute — the largest download, and the reference the other quants here are judged against.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `f16` / `vae_decoder` = `f16`	—
`f16+dit8`	Half size (int8 transformer) — Transformer stored as int8 and computed in f32: roughly half its f16 download, with the execution path left unchanged.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `i8` / `vae_decoder` = `f16`	—
`f16+dit8-a8`	Half size, int8 linear — The int8 transformer with per-token int8 activations in its linear layers — faster on GPUs with dp4a, same download.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `i8` / `vae_decoder` = `f16`	`linearCompute` = `a8`
`f16+dit8-a8-attn8`	Half size, int8 linear and attention — Adds int8 activations inside attention on top of the int8 linear path; same weights, one more integer stage per step.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `i8` / `vae_decoder` = `f16`	`linearCompute` = `a8` / `attentionCompute` = `a8`
`f16+dit8-a8-attn8-s16` (default)	Balanced (int8) — The int8 linear and attention path with attention scores held in f16 — the fastest of the int8 seats here, at f16-level image quality.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `i8` / `vae_decoder` = `f16`	`linearCompute` = `a8` / `attentionCompute` = `a8` / `attentionScoreStorage` = `f16`
`f16-c16`	Full quality, f16 compute — f16 storage computed in f16 throughout. Needs the shader-f16 GPU feature, and trades numerical headroom for speed.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `f16` / `vae_decoder` = `f16`	`linearCompute` = `f16` / `attentionCompute` = `f16` / requires `shaderF16`

If no quant is given, it runs as f16+dit8-a8-attn8-s16 (this model's recommended default). In a quant name, dit is the transformer component.

Defaults

Any knob not passed to generate() is filled in from the manifest's defaults.

steps: 20
guidanceScale: 4
resolution: 1024 × 1024
negativePrompt: low quality, worst quality, blurry, bad anatomy, jpeg artifacts

Model: anima-copycat-20260610

Files

Key	Dtype	Path	Size	sha256
`text_encoder`	f16	`shared/text_encoder/model.safetensors`	1.11 GiB (1,194,220,916 B)	`4ae7c810de0edfbd…`
`text_conditioner`	f16	`anima-copycat-20260610/text_conditioner/model.safetensors`	257.33 MiB (269,834,156 B)	`510e4e64d10781ec…`
`transformer`	f16	`anima-copycat-20260610/transformer/model.f16.safetensors`	3.64 GiB (3,913,609,588 B)	`54a9cce1d3519725…`
`transformer`	i8	`anima-copycat-20260610/transformer/model.i8.safetensors`	1.83 GiB (1,962,502,636 B)	`8383e51fd1a8a9a2…`
`transformer.rope_base`	f16 / i8	`shared/transformer/rope_base.safetensors`	64.42 KiB (65,968 B)	`42db9a3fc796c45f…`
`vae_decoder`	f16	`shared/vae_decoder/model.safetensors`	48.37 MiB (50,720,688 B)	`b50b65a028a8d108…`
`tokenizer`	—	`shared/tokenizer/qwen2-tokenizer.json`	3.35 MiB (3,514,619 B)	`0a7d6057ac8a2fe4…`
`tokenizer_2`	—	`shared/tokenizer_2/t5-tokenizer.json`	1.04 MiB (1,093,419 B)	`f86dfe21b12a175a…`

Quants

Quant	What it is	Weights	Compute
`f16`	Full quality (f16) — Transformer in f16 storage with f32 compute — the largest download, and the reference the other quants here are judged against.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `f16` / `vae_decoder` = `f16`	—
`f16+dit8`	Half size (int8 transformer) — Transformer stored as int8 and computed in f32: roughly half its f16 download, with the execution path left unchanged.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `i8` / `vae_decoder` = `f16`	—
`f16+dit8-a8`	Half size, int8 linear — The int8 transformer with per-token int8 activations in its linear layers — faster on GPUs with dp4a, same download.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `i8` / `vae_decoder` = `f16`	`linearCompute` = `a8`
`f16+dit8-a8-attn8`	Half size, int8 linear and attention — Adds int8 activations inside attention on top of the int8 linear path; same weights, one more integer stage per step.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `i8` / `vae_decoder` = `f16`	`linearCompute` = `a8` / `attentionCompute` = `a8`
`f16+dit8-a8-attn8-s16` (default)	Balanced (int8) — The int8 linear and attention path with attention scores held in f16 — the fastest of the int8 seats here, at f16-level image quality.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `i8` / `vae_decoder` = `f16`	`linearCompute` = `a8` / `attentionCompute` = `a8` / `attentionScoreStorage` = `f16`
`f16-c16`	Full quality, f16 compute — f16 storage computed in f16 throughout. Needs the shader-f16 GPU feature, and trades numerical headroom for speed.	`text_encoder` = `f16` / `text_conditioner` = `f16` / `transformer` = `f16` / `vae_decoder` = `f16`	`linearCompute` = `f16` / `attentionCompute` = `f16` / requires `shaderF16`

If no quant is given, it runs as f16+dit8-a8-attn8-s16 (this model's recommended default). In a quant name, dit is the transformer component.

Defaults

Any knob not passed to generate() is filled in from the manifest's defaults.

steps: 20
guidanceScale: 4
resolution: 1024 × 1024
negativePrompt: low quality, worst quality, blurry, bad anatomy, jpeg artifacts

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for hdae/karume-anima

Base model

nvidia/Cosmos-Predict2-2B-Text2Image

Finetuned

circlestone-labs/Anima

Finetuned

circlestone-labs/Anima-Base-v1.0-Diffusers

Quantized

(2)

this model

Anima — Karume

What is this

Models and their origins

anima-v1.0 — Anima Base v1.0

anima-wai-v1.0 — WAI-ANIMA v1.0 (base 1.0)

anima-copycat-20260610 — copycat-anima 20260610

License

Models

Usage

Model: anima-v1.0

Files

Quants

Defaults

Model: anima-wai-v1.0

Files

Quants

Defaults

Model: anima-copycat-20260610

Files

Quants

Defaults

Model tree for hdae/karume-anima

`anima-v1.0` — Anima Base v1.0

`anima-wai-v1.0` — WAI-ANIMA v1.0 (base 1.0)

`anima-copycat-20260610` — copycat-anima 20260610