hdae/anima-turbo

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 is karume.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: true
• allowCommercialUse: Image / RentCivit / Rent
• allowDerivatives: true
• allowDifferentLicense: 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).