---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
- dreamshaper
- coreml
- apple-neural-engine
- palettized
- tokforge
base_model:
- Lykon/dreamshaper-8
pipeline_tag: text-to-image
library_name: ml-stable-diffusion
---

## LocalMuseAI distribution mirror

This repository is an unmodified distribution mirror of [`darkmaniac7/TokForge-DreamShaper-8-CoreML-6bit`](https://huggingface.co/darkmaniac7/TokForge-DreamShaper-8-CoreML-6bit) for the LocalMuse iOS app. The compiled Core ML binary artifacts are preserved unchanged. Model authorship, conversion credit, license terms, and the original model card are retained below.


## TokForge

- **Website:** https://tokforge.ai
- **Discord:** https://discord.gg/Acv3CBtfVm
- **Google Play:** https://play.google.com/store/apps/details?id=dev.tokforge
- **iOS TestFlight:** https://testflight.apple.com/join/jnufjzRr

Runs on-device in the TokForge app.

# TokForge — DreamShaper 8 · CoreML 6-bit (Apple Neural Engine)

A **6-bit palettized Apple CoreML** conversion of
**[DreamShaper 8](https://huggingface.co/Lykon/dreamshaper-8)** (Lykon, SD-1.5 realistic
finetune), built for on-device image generation in the **[TokForge](https://tokforge.ai)** iOS
app. Converted with Apple **[`ml-stable-diffusion`](https://github.com/apple/ml-stable-diffusion)**
(`torch2coreml`) using **`SPLIT_EINSUM_V2`** attention and **`--quantize-nbits 6`** (6-bit
palettized weights), so it compiles **fast on the Apple Neural Engine** — the ANE-fast beautiful
default replacing the FP16 finetune that hit a >11-minute ANE graph compile.

Part of the **[TokForge iOS · CoreML Image Models](https://huggingface.co/collections/darkmaniac7/tokforge-ios-coreml-image-models-6a38cca9b57803e6168ce232)** collection.

## Files

| File | Size | Contents |
|------|------|----------|
| `DreamShaper-8_palettized_split_einsum_v2_compiled.zip` | ~874 MB | The compiled Swift-CLI resource bundle (a single ZIP of `Resources/`) |
| `Resources/` | ~913 MB | The unzipped tree: `TextEncoder.mlmodelc` / `Unet.mlmodelc` / `VAEDecoder.mlmodelc` / `VAEEncoder.mlmodelc` + `vocab.json` + `merges.txt` |

The ZIP expands to one wrapper folder (`Resources/`) holding the compiled `.mlmodelc` models
plus the CLIP `vocab.json` + `merges.txt` — the exact layout Apples
`StableDiffusionPipeline` (and the TokForge installer) loads.

## Recommended render settings (standard SD-1.5)

```
attention:    split_einsum_v2 (Apple Neural Engine)
compute:      .cpuAndNeuralEngine  (palettized -> fast ANE compile)
steps:        20   (8 = fast floor, 40 = extra refinement)
cfg-scale:    7.5
resolution:   512x512  (SD-1.5 native; baked into the compiled model)
```

## How this was built

1. Loaded `Lykon/dreamshaper-8` (SD-1.5 diffusers format).
2. Converted UNet + text encoder + VAE decoder + VAE encoder to CoreML with Apple
   `ml-stable-diffusion` `python_coreml_stable_diffusion.torch2coreml`,
   `--attention-implementation SPLIT_EINSUM_V2` (ANE-shaped attention).
3. Applied **6-bit palettization** (`--quantize-nbits 6`) — the iOS-17 ANE runtime feature
   that makes the graph compile fast on the Neural Engine.
4. Bundled the compiled resources for the Swift CLI (`--bundle-resources-for-swift-cli`).

Conversion peaked at ~9.96 GB RAM (no `--chunk-unet` needed); runs on Apple silicon, iOS **17+**
(6-bit palettized weights require the iOS-17 runtime).

## License & attribution

- **License:** [CreativeML OpenRAIL-M](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
  (inherited from DreamShaper 8 / Stable Diffusion 1.5). Use is subject to the OpenRAIL-M use
  restrictions.
- **Base model:** **DreamShaper 8** by **Lykon** — https://huggingface.co/Lykon/dreamshaper-8
  (a Stable Diffusion 1.5 finetune). All credit for the model weights is Lykons.
- **Conversion tooling:** Apple **`ml-stable-diffusion`** —
  https://github.com/apple/ml-stable-diffusion (6-bit palettization, `SPLIT_EINSUM_V2` attention).
- Built on top of Stable Diffusion 1.5 (Runway/CompVis/Stability).

This repository is a **redistribution for on-device use** — a format conversion (PyTorch ->
CoreML) and 6-bit palettization of Lykons DreamShaper 8. No weights were retrained. The
original OpenRAIL-M terms and attribution requirements propagate to this conversion and any
images generated with it. No additional restrictions are imposed by this repackaging.