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