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ml-stable-diffusion (torch2coreml) using
SPLIT_EINSUM_V2 attention and --quantize-nbits 6 — 6-bit palettized weights compile
fast on the Apple Neural Engine (the ANE-fast photoreal path, vs the FP16 finetunes >11-min
ANE graph compile).| File | Size | Contents |
|---|---|---|
RealisticVision-5.1_palettized_split_einsum_v2_compiled.zip | ~874 MB | The compiled Swift-CLI resource bundle (a single ZIP of Resources/) |
Resources/ | ~913 MB | TextEncoder.mlmodelc / Unet.mlmodelc / VAEDecoder.mlmodelc / VAEEncoder.mlmodelc + vocab.json + merges.txt |
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)SG161222/Realistic_Vision_V5.1_noVAE (SD-1.5 diffusers; the diffusers conversion
bundles a working VAE).ml-stable-diffusion python_coreml_stable_diffusion.torch2coreml,
--attention-implementation SPLIT_EINSUM_V2.--quantize-nbits 6) for the fast iOS-17 ANE compile.--bundle-resources-for-swift-cli).--chunk-unet needed); iOS 17+ (6-bit palettized).ml-stable-diffusion —
https://github.com/apple/ml-stable-diffusion (6-bit palettization, SPLIT_EINSUM_V2).