Views
No views yet
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.| File | Size | Contents |
|---|---|---|
Resources/ | ~913 MB | TextEncoder.mlmodelc / Unet.mlmodelc / VAEDecoder.mlmodelc / VAEEncoder.mlmodelc + vocab.json + merges.txt |
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).--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).