CyberRealistic V8 Inpaint Compact — Core ML 6-bit for LocalMuse
This repository contains a compact Core ML conversion of
CyberRealistic v8.0-Inpainting by Cyberdelia
for the dedicated Inpaint workflow in the LocalMuse iOS app.
The UNet and CLIP text encoder use Apple's 6-bit k-means palettization. Both
VAE directions remain FP16 to preserve image encoding, decoding and mask-edge
quality. This is a storage-optimized build for nominal 6 GB devices; the
separate FP16 build remains the highest-quality option for 8 GB devices.
Provenance
- Official source: CyberRealistic
v8.0-Inpainting, Civitai model version
1464918
- Authenticated checkpoint mirror:
vclansience/SD_models
- Pinned mirror revision:
a461e57d5b2d1975670b27580cc03b6d460caf2c
- Source file:
cyberrealistic_v80Inpainting.safetensors
- Source size:
2132679962 bytes
- Source SHA-256:
0ee757321f40a95029c4ff7e4ae78352ed9ee67a3ddaa8d14b6b1a48161901f2
- Architecture: Stable Diffusion 1.5 Inpaint
- UNet input/output:
[2, 9, 64, 64] → [2, 4, 64, 64]
- Fixed image resolution: 512×512
- UNet and text encoder storage: 6-bit palettized
- VAE encoder and decoder storage: FP16
- UNet attention graph:
SPLIT_EINSUM_V2
- Core ML deployment target: iOS 17
- Core ML execution policy in LocalMuse: CPU + GPU
- Conversion tooling: Apple
ml-stable-diffusion commit
e12202c1f6405b83918b58a5d097cd61e3e1f702, Core ML Tools 8.3.0
The Hugging Face mirror is used because Civitai requires an interactive
account for the original download. Its LFS SHA-256 exactly matches the
official Civitai API fingerprint. Pipeline configuration is pinned separately
to the official Stable Diffusion 1.5 Inpaint repository at revision
8a4288a76071f7280aedbdb3253bdb9e9d5d84bb.
The package includes a monolithic sub-1 GB UNet, the matching CLIP text
encoder, VAE decoder, VAE encoder and tokenizer. PROVENANCE.json records the
authenticated source, conversion dependencies and SHA-256 of every compiled
runtime artifact.
This package is only compatible with a true nine-channel Inpaint runtime. It
must not replace a normal four-channel SD 1.5 model.
Validation
- UNet conversion parity: 74.2 dB PSNR
- CLIP text encoder conversion parity: 82.8 dB PSNR
- VAE decoder conversion parity: 61.5 dB PSNR
- VAE encoder conversion parity: 80.7 dB PSNR
- Compiled UNet input:
[2, 9, 64, 64]
- Compiled UNet output:
[2, 4, 64, 64]
- All four compiled components completed finite-output prediction tests
- End-to-end 30-step DPM-Solver++ Inpaint smoke test completed successfully
Recommended settings
- Scheduler: DPM-Solver++ with Karras timestep spacing
- Steps: 30 (recommended range: 10–50)
- Guidance scale: 5.0
- Resolution: 512×512
- Batch size: 1 on iOS
- Device memory class: nominal 6 GB minimum in LocalMuse
Do not attach the generic lcm-lora-sdv1-5 adapter to this checkpoint.
Reference Diffusers and Core ML end-to-end tests both produce a black masked
region with that combination. A true LCM variant requires an adapter distilled
specifically for the nine-channel Inpaint UNet.
License and attribution
CyberRealistic is authored by Cyberdelia. The original Civitai model permits
commercial use and derivative distribution. The underlying SD 1.5 terms and
use-based restrictions are retained in the included
CreativeML Open RAIL-M license.
The files here are modified by conversion to compiled Core ML format and by
6-bit palettization of the UNet and text encoder. No weights were retrained or
merged, and no additional restrictions are imposed. This repository does not
imply endorsement by Cyberdelia, Stability AI, CompVis, Runway, Apple,
Hugging Face or Civitai.
