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darkmaniac7/TokForge-CyberRealistic-V9-CoreML-6bit
TokForge-CyberRealistic-V9-CoreML-6bit is a text-to-image model from darkmaniac7. Use it when you need an image from a text prompt. It is set up for ml-stable-diffusion. The card lists the license as creativeml-openrail-m.
- 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
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Updated Oct 5, 2026
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.bin953 MB · 51%
From the Hugging Face model README
Runs on-device in the TokForge app.
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.
| 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.
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)
CyberRealistic_V9_FP16.safetensors from cyberdelia/CyberRealistic via diffusers
StableDiffusionPipeline.from_single_file and re-exported to SD-1.5 diffusers format.ml-stable-diffusion python_coreml_stable_diffusion.torch2coreml,
--attention-implementation SPLIT_EINSUM_V2.--quantize-nbits 6).--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.
ml-stable-diffusion —
https://github.com/apple/ml-stable-diffusion (6-bit palettization, SPLIT_EINSUM_V2 attention).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.