library_name: karume
pipeline_tag: text-to-image
base_model: circlestone-labs/Anima-Base-v1.0-Diffusers
base_model_relation: quantized
license: other
license_name: circlestone-labs-non-commercial-license
license_link: https://huggingface.co/circlestone-labs/Anima/blob/main/LICENSE.md
tags:
- text-to-image
- webgpu
Anima Turbo — Karume
What is this
A distribution that bakes Anima Turbo LoRA v0.2 into circlestone-labs/Anima-Base-v1.0-Diffusers and converts it 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.
- A few-step distillation (from the LoRA) tuned for 8 steps / guidance 1.
- 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/0.1.0. The distribution manifest iskarume.json(karume/1).
Baked-in LoRA
Folded into the weights — not distributed as a separate file.
- Name: Anima Turbo LoRA v0.2
- Author: circlestone_labs (same author as the base model)
- Source: https://civitai.com/models/2560840?modelVersionId=2979642
- File:
anima-turbo-lora-v0.2.safetensors - sha256:
1b55e40bdb1d0e5a78cb498f245fccfdaae97823265db957d2aabdcf4cd3caf1
Permissions listed on the source page (as of retrieval):
allowNoCredit: trueallowCommercialUse: Image / RentCivit / RentallowDerivatives: trueallowDifferentLicense: true
Files
| Key | Variant | Path | Size | sha256 |
|---|---|---|---|---|
text_encoder |
— | text_encoder/model.safetensors |
1.11 GiB (1,194,225,572 B) | 79dc23f2d45c8f3e… |
text_conditioner |
— | text_conditioner/model.safetensors |
257.34 MiB (269,838,156 B) | a704ba27c865cd4e… |
transformer |
f16 | transformer/model.f16.safetensors |
3.64 GiB (3,913,665,620 B) | 57c8a08be56c6fea… |
transformer |
i8 | transformer/model.i8.safetensors |
1.83 GiB (1,962,558,660 B) | df3cc9b539f30670… |
transformer.rope_base |
f16 / i8 | transformer/rope_base.safetensors |
64.42 KiB (65,968 B) | 42db9a3fc796c45f… |
vae_decoder |
— | vae_decoder/model.safetensors |
48.37 MiB (50,720,688 B) | b50b65a028a8d108… |
tokenizer |
— | tokenizer/qwen2-tokenizer.json |
3.35 MiB (3,514,619 B) | 0a7d6057ac8a2fe4… |
tokenizer_2 |
— | 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).
Variant labels use the runtime's storage dtype vocabulary (f16 / i8), not the fp16 spelling common elsewhere in the ecosystem.
Presets
| Preset | Weights | Compute |
|---|---|---|
f16 |
transformer = f16 |
— |
i8 |
transformer = i8 |
— |
w8a8 |
transformer = i8 |
linearCompute = i8a8 |
w8a8-a8 |
transformer = i8 |
linearCompute = i8a8 / attentionCompute = i8a8 |
w8a8-s16 (default) |
transformer = i8 |
linearCompute = i8a8 / attentionCompute = i8a8 / attentionScoreStorage = f16 |
f16-c16 |
transformer = f16 |
linearCompute = f16 / attentionCompute = f16 / requires shaderF16 |
If no preset is given, it runs as w8a8-s16 (the distribution's recommended default).
Usage
import { AnimaPipeline, encodePng } from "jsr:@karume/models";
// The preset defaults to w8a8-s16.
using pipeline = await AnimaPipeline.fromPretrained("hdae/anima-turbo");
const image = await pipeline.generate({
prompt: "1girl, solo, long hair, blue eyes, school uniform, masterpiece",
seed: 42,
});
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).
Defaults
Any knob not passed to generate() is filled in from the manifest's defaults.
- steps: 8
- guidanceScale: 1
- resolution: 1024 × 1024
- negativePrompt:
low quality, worst quality, blurry, bad anatomy, jpeg artifacts
At guidance 1, the second CFG branch is skipped, so the negative prompt is not used (it only takes effect once guidance is raised).