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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 ANE-fast beautiful
default replacing the FP16 finetune that hit a >11-minute ANE graph compile.| File | Size | Contents |
|---|---|---|
DreamShaper-8_palettized_split_einsum_v2_compiled.zip | ~874 MB | The compiled Swift-CLI resource bundle (a single ZIP of Resources/) |
Resources/ | ~913 MB | The unzipped tree: TextEncoder.mlmodelc / Unet.mlmodelc / VAEDecoder.mlmodelc / VAEEncoder.mlmodelc + vocab.json + merges.txt |
Resources/) holding 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: 20 (8 = fast floor, 40 = extra refinement)
cfg-scale: 7.5
resolution: 512x512 (SD-1.5 native; baked into the compiled model)Lykon/dreamshaper-8 (SD-1.5 diffusers format).ml-stable-diffusion python_coreml_stable_diffusion.torch2coreml,
--attention-implementation SPLIT_EINSUM_V2 (ANE-shaped attention).--quantize-nbits 6) — the iOS-17 ANE runtime feature
that makes the graph compile fast on the Neural Engine.--bundle-resources-for-swift-cli).--chunk-unet needed); runs on Apple silicon, iOS 17+
(6-bit palettized weights require the iOS-17 runtime).ml-stable-diffusion —
https://github.com/apple/ml-stable-diffusion (6-bit palettization, SPLIT_EINSUM_V2 attention).