---
license: openrail++
base_model: SG161222/RealVisXL_V5.0_Lightning
library_name: coreml
tags:
- coreml
- stable-diffusion-xl
- text-to-image
- image-to-image
- ios
- localmuse
---

# RealVisXL V5.0 Lightning — 6-bit split Core ML

Self-contained, compiled SDXL package prepared for on-device use by the LocalMuse iOS app. The repository stays private until the artifact manifest and anonymous downloads are verified.

## Provenance

- Source UNet: [`SG161222/RealVisXL_V5.0_Lightning`](https://huggingface.co/SG161222/RealVisXL_V5.0_Lightning)
- Pinned source revision: `f4454158cedaab9f0688c199561d6c92525f3a85`
- Source fp16 UNet LFS SHA-256: `1143cd2aaf65d24af34b5699d090aed724f6c0978c2ec5a5f56821ccb36260ce`
- License: CreativeML Open RAIL++-M (`LICENSE.md`), inherited from the source model and SDXL base.

The RealVisXL UNet was converted from the pinned source above. The text encoders, VAE encoder/decoder, tokenizer vocabulary, and merges are standard SDXL components reused byte-for-byte from [`LocalMuseAI/coreml-dreamshaper-xl-lightning-6bit`](https://huggingface.co/LocalMuseAI/coreml-dreamshaper-xl-lightning-6bit) revision `736408cecf59b3b81a8b2a900cdd5dbf94c34c2a`. Only the UNet contains RealVisXL-specific weights.

## Conversion

- Apple converter: [`apple/ml-stable-diffusion`](https://github.com/apple/ml-stable-diffusion) revision `e12202c1f6405b83918b58a5d097cd61e3e1f702`
- PyTorch `2.4.0`
- coremltools `8.0`
- Diffusers `0.30.2`
- Transformers `4.44.2`
- huggingface_hub `0.24.6`
- NumPy `1.23.5`
- Resolution: fixed `1024 × 1024` (`128 × 128` latent)
- Classifier-free-guidance batch: `2`
- Attention implementation: `SPLIT_EINSUM`
- UNet storage: mixed Float16 and 6-bit palettized weights
- Minimum Core ML deployment target: iOS 17
- UNet split into two independently compiled chunks below 1 GiB each

The source model's fp16 UNet was loaded directly without downloading unrelated full-precision weights, converted through Apple's Stable Diffusion implementation, palettized before chunking, bisected with coremltools, and compiled with `xcrun coremlc`.

## Runtime settings

The source model card recommends 5 inference steps, CFG 1–2, and a DPM++ SDE Karras scheduler. LocalMuse uses:

- Steps: `5`
- CFG: `1.5`
- Scheduler: second-order DPM-Solver++ multistep
- Timestep spacing: Karras
- Batch size: `1` image (the UNet internally uses batch 2 for CFG)
- UNet: Neural Engine preferred
- VAE: GPU preferred
- Minimum device memory exposed by the app: 8 GiB

## Verification

Both chunks compile successfully with Xcode `coremlc`. Their named input/output feature sets and tensor shapes match the SDXL split-UNet contract already used by LocalMuse. The model is distributed as direct authenticated files; the app pins every URL to a repository commit and verifies the exact SHA-256 and byte count before installation.

Real-device image quality and thermal behavior should still be validated before shipping a production App Store build.