darkmaniac7 / TokForge-CyberRealistic-V9-CoreML-6bit

TokForge — CyberRealistic V9 · CoreML 6-bit (Apple Neural Engine)

A 6-bit palettized Apple CoreML conversion of CyberRealistic V9 ( cyberdelia/CyberRealistic , the CyberRealistic_V9_FP16 checkpoint by cyberdelia — an SD-1.5 photorealistic finetune with best-in-class faces and an integrated VAE), 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 .

Part of the TokForge iOS · CoreML Image Models 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

attention:    split_einsum_v2 (Apple Neural Engine)
compute:      .cpuAndNeuralEngine  (palettized -> fast ANE compile)
steps:        25-30  (CyberRealistic photoreal sweet spot)
cfg-scale:    7.0
resolution:   512x512  (SD-1.5 native; baked into the compiled model)

How this was built

1. Loaded CyberRealistic_V9_FP16.safetensors from cyberdelia/CyberRealistic via diffusers StableDiffusionPipeline.from_single_file and re-exported to 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 .
3. Applied 6-bit palettization ( --quantize-nbits 6 ).
4. Bundled the compiled resources for the Swift CLI ( --bundle-resources-for-swift-cli ).

Conversion peaked at ~10.5 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 CyberRealistic / Stable Diffusion 1.5. Use is subject to the OpenRAIL-M restrictions.
• Base model: CyberRealistic V9 by cyberdelia — https://huggingface.co/cyberdelia/CyberRealistic . All credit for the model weights is cyberdelias.
• 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 cyberdelias CyberRealistic V9. 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.