darkmaniac7/TokForge-DreamShaper-8-LCM-CoreML-6bit

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](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 fast slot in the TokForge model set.

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

Files

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

1. 1.Loaded Lykon/dreamshaper-8-lcm (SD-1.5 diffusers format, LCM-finetuned UNet).
2. 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.
3. 3.Applied 6-bit palettization (--quantize-nbits 6).
4. 4.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.