Downloads · 30 days
0
hdae/karume-anima-extra
karume-anima-extra is a text-to-image model from hdae. Use it when you need an image from a text prompt. It is set up for karume. The card lists the license as other.
Community fine-tunes of the CircleStone Anima base model (circlestone-labs/Anima-Base-v1.0-Diffusers), converted into the WebGPU inference runtime Karume's container format (a .krm part sequence whose first part carri…
Downloads · 30 days
0
Access
Public
Updated Sep 25, 2026
Repo size
24.6 GB
Likes
0
Public
Click a slice to open those files.
.krm12.3 GB · 100%
From the Hugging Face model README
Community fine-tunes of the CircleStone Anima base model (circlestone-labs/Anima-Base-v1.0-Diffusers),
converted into the WebGPU inference runtime Karume's container format (a .krm part
sequence whose first part carries the graph and model descriptors). Runs as-is in the
browser and in Deno.
anima/1.karume/0.13.0. The distribution manifest is karume.json (karume/5).Each model below is a community fine-tune of the CircleStone Anima base model. The text
encoder, VAE and tokenizers are shared with the official repository
(hdae/karume-anima) through pinned
cross-repository references in karume.json.
anima-wai-v1.0 — WAI-ANIMA v1.0 (base 1.0)waiANIMA_v10Base10.safetensorsPermissions listed on the source page (as of 2026-08-22):
allowNoCredit: trueallowCommercialUse: Image / RentCivitallowDerivatives: trueallowDifferentLicense: trueanima-copycat-20260610 — copycat-anima 20260610copycatAnima_20260610.safetensorsPermissions listed on the source page (as of 2026-08-22):
allowNoCredit: trueallowCommercialUse: Image / RentCivitallowDerivatives: trueallowDifferentLicense: falseEvery 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).
Any rights to use the CircleStone Models and/or Derivatives in this repository are granted to you directly by CircleStone Labs LLC under the CircleStone Labs Non-Commercial License (LICENSE.md).
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.
anima-copycat-20260610: the source page sets allowDifferentLicense to false, so this redistribution
keeps the same terms — do not relicense it.| Model | Pipeline | Quants | Default quant |
|---|---|---|---|
anima-wai-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-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-wai-v1.0. quant defaults to that model's own default quant.
import { AnimaPipeline, encodePng } from "jsr:@karume/models";
await using pipeline = await AnimaPipeline.fromPretrained({
repo: "hdae/karume-anima-extra",
// 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-wai-v1.0", // default — available: anima-copycat-20260610 / 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:
// Sides above 1920 px sit outside the range this model declares (its RoPE tables
// declare 120 tokens = 1920 px per side); those positions are extrapolated with the
// same formula, so what can differ there is the image quality.
// 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).
| Quant | What it is | Download | 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. | 5.06 GiB (1.16 GiB shared) | 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. | 3.24 GiB (1.16 GiB shared) | 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. | 3.24 GiB (1.16 GiB shared) | 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. | 3.24 GiB (1.16 GiB shared) | 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. | 3.24 GiB (1.16 GiB shared) | 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. | 5.06 GiB (1.16 GiB shared) | 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.
Per-file size and sha256 live in karume.json — verify against that at the fetch layer.
Dtype labels use the runtime's storage dtype vocabulary (f16 / i8 / i4 / i2), not the fp16 spelling common elsewhere in the ecosystem.
Weights ship as Karume container files (.krm), split into numbered parts; a part is fetched and verified on its own.
A path under shared/ is one this model shares byte for byte with another model in this repository (it is fetched and cached once).
Some components are fetched from hdae/karume-anima at commit adb9dcf054400671… — those bytes are identical to this model's own, so they are not stored here a second time.
Any knob not passed to generate() is filled in from the manifest's defaults.
low quality, worst quality, blurry, bad anatomy, jpeg artifacts| Quant | What it is | Download | 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. | 5.06 GiB (1.16 GiB shared) | 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. | 3.24 GiB (1.16 GiB shared) | 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. | 3.24 GiB (1.16 GiB shared) | 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. | 3.24 GiB (1.16 GiB shared) | 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. | 3.24 GiB (1.16 GiB shared) | 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. | 5.06 GiB (1.16 GiB shared) | 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.
Per-file size and sha256 live in karume.json — verify against that at the fetch layer.
Dtype labels use the runtime's storage dtype vocabulary (f16 / i8 / i4 / i2), not the fp16 spelling common elsewhere in the ecosystem.
Weights ship as Karume container files (.krm), split into numbered parts; a part is fetched and verified on its own.
A path under shared/ is one this model shares byte for byte with another model in this repository (it is fetched and cached once).
Some components are fetched from hdae/karume-anima at commit adb9dcf054400671… — those bytes are identical to this model's own, so they are not stored here a second time.
Any knob not passed to generate() is filled in from the manifest's defaults.
low quality, worst quality, blurry, bad anatomy, jpeg artifacts