coreml-dreamshaper-8-lcm-6bit

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Document LocalMuse mirror and include model license

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metadata

license: creativeml-openrail-m
tags:
  - text-to-image
  - stable-diffusion
  - dreamshaper
  - lcm
  - latent-consistency-model
  - coreml
  - apple-neural-engine
  - palettized
  - tokforge
base_model:
  - Lykon/dreamshaper-8-lcm
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-LCM-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 LCM · CoreML 6-bit (Apple Neural Engine)

A 6-bit palettized Apple CoreML conversion of DreamShaper 8 LCM (Lykon/dreamshaper-8-lcm, Lykons SD-1.5 DreamShaper 8 finetuned for Latent Consistency few-step sampling), built for on-device image generation in the TokForge iOS app. Converted with 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 fast slot in the TokForge model set.

Part of the TokForge iOS · CoreML Image Models collection.

Files

File	Size	Contents
`Resources/`	~913 MB	`TextEncoder.mlmodelc` / `Unet.mlmodelc` / `VAEDecoder.mlmodelc` / `VAEEncoder.mlmodelc` + `vocab.json` + `merges.txt`

The Resources/ tree holds the compiled .mlmodelc models plus the CLIP vocab.json + merges.txt — the exact layout Apples StableDiffusionPipeline (and the TokForge installer) loads.

Recommended render settings (LCM)

attention:    split_einsum_v2 (Apple Neural Engine)
compute:      .cpuAndNeuralEngine  (palettized -> fast ANE compile)
steps:        10-15  (works with the stock scheduler today; drops to 4-8 once an
                      LCM scheduler ships — apple/ml-stable-diffusion #319)
cfg-scale:    1.5-2.0  (LCM prefers low guidance)
resolution:   512x512  (SD-1.5 native; baked into the compiled model)

How this was built

Loaded Lykon/dreamshaper-8-lcm (SD-1.5 diffusers format, LCM-finetuned UNet).
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.
Applied 6-bit palettization (--quantize-nbits 6).
Bundled the compiled resources for the Swift CLI (--bundle-resources-for-swift-cli).

Conversion peaked at ~9.9 GB RAM (no --chunk-unet needed). Runs on iOS 17+ (6-bit palettized weights require the iOS-17 ANE runtime); on iOS-16 the app falls back to an FP16 model.

License & attribution

License: CreativeML OpenRAIL-M, inherited from DreamShaper 8 LCM / Stable Diffusion 1.5. Use is subject to the OpenRAIL-M restrictions.
Base model: DreamShaper 8 LCM by Lykon — https://huggingface.co/Lykon/dreamshaper-8-lcm. 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 LCM. 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.