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darkmaniac7/TokForge-epiCRealism-CoreML-6bit
TokForge-epiCRealism-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.
Published by TokForge for on-device inference.
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Updated Oct 12, 2026
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From the Hugging Face model README
Published by TokForge for on-device inference.
Runs on-device in the TokForge app.
A 6-bit palettized Apple CoreML conversion of epiCRealism
(emilianJR/epiCRealism, an SD-1.5 cinematic
photoreal finetune with 1M+ downloads), 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 Apple's StableDiffusionPipeline (and the TokForge installer)
loads.
attention: split_einsum_v2 (Apple Neural Engine)
compute: .cpuAndNeuralEngine (palettized -> fast ANE compile)
steps: 20-30 (epiCRealism cinematic-portrait range)
cfg-scale: 7.0
resolution: 512x512 (SD-1.5 native; baked into the compiled model)
emilianJR/epiCRealism (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 ~9.8 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 epinikion's epiCRealism. 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.