| --- |
| license: openrail++ |
| library_name: ml-stable-diffusion |
| pipeline_tag: text-to-image |
| base_model: |
| - cyberdelia/CyberRealistic |
| - latent-consistency/lcm-lora-sdv1-5 |
| tags: |
| - stable-diffusion |
| - stable-diffusion-1.5 |
| - cyberrealistic |
| - lcm |
| - photorealistic |
| - coreml |
| - apple-neural-engine |
| - palettized |
| - ios |
| --- |
| |
| # CyberRealistic Final LCM · Core ML 6-bit |
|
|
| This is a reproducible Core ML conversion prepared for the LocalMuse iOS app. |
| It starts from CyberRealistic Final FP16, fuses the official SD 1.5 LCM-LoRA, |
| then applies Apple's 6-bit k-means weight palettization to the UNet and CLIP |
| text encoder. The VAE decoder and VAE encoder remain FP16. |
|
|
| ## Pinned sources |
|
|
| - Base: `cyberdelia/CyberRealistic` |
| - revision: `99827f96edd717dacb28c68560680c201c55df05` |
| - file: `CyberRealistic_FINAL_FP16.safetensors` |
| - bytes: `2,132,651,162` |
| - SHA-256: `2209c07b331a06cb28cf7c830ec758ae5b49eb97fab21f5de6b18c7be8b41554` |
| - Adapter: `latent-consistency/lcm-lora-sdv1-5` |
| - revision: `cf2fced511dbe7e26c8d1d397e728fbab875db4b` |
| - file: `pytorch_lora_weights.safetensors` |
| - bytes: `134,621,556` |
| - SHA-256: `8f90d840e075ff588a58e22c6586e2ae9a6f7922996ee6649a7f01072333afe4` |
| - SD 1.5 component configuration and tokenizer: |
| `stable-diffusion-v1-5/stable-diffusion-v1-5` at |
| `451f4fe16113bff5a5d2269ed5ad43b0592e9a14`. |
|
|
| All source files are authenticated before the pipeline is loaded or traced. |
| The LoRA is fused at scale 1.0 and unloaded before Core ML conversion. |
|
|
| ## Core ML layout |
|
|
| - Fixed resolution: 512 × 512 |
| - Deployment target: iOS 17+ |
| - Compute policy used for conversion: CPU and GPU |
| - Attention: `SPLIT_EINSUM_V2` |
| - `Unet.mlmodelc`: mixed FP16 / 6-bit palettized, 282 LUT operations |
| - `TextEncoder.mlmodelc`: mixed FP16 / 6-bit palettized, 74 LUT operations |
| - `VAEDecoder.mlmodelc`: FP16 |
| - `VAEEncoder.mlmodelc`: FP16, included for image-to-image and Face Detail |
| - No safety checker is embedded |
| - Production payload: 957,838,366 bytes |
|
|
| The Core ML converter is pinned to Apple `ml-stable-diffusion` revision |
| `e12202c1f6405b83918b58a5d097cd61e3e1f702` with Core ML Tools 8.3.0. |
| The conversion performed component-level PyTorch/Core ML parity checks before |
| palettization. The final palettized package was then tested end-to-end with the |
| LocalMuse LCM scheduler, including VAE-encoder image-to-image refinement. |
|
|
| ## Recommended settings |
|
|
| - Scheduler: LCM (required) |
| - Steps: 4–10 |
| - Default: 8 steps |
| - CFG: 1.5; keep guidance in the 1.0–2.0 range |
| - Resolution: 512 × 512 |
|
|
| Four, six, eight and ten steps were tested with the same prompt and seed. |
| Eight steps gave the best quality/speed balance; ten remained stable. This |
| tested 4–10 application range is intentionally a little wider than the |
| adapter model card's usual 2–8 recommendation. |
|
|
| ## License and attribution |
|
|
| CyberRealistic and Stable Diffusion 1.5 remain subject to the CreativeML Open |
| RAIL-M terms in `LICENSE`. The LCM-LoRA repository declares OpenRAIL++ and its |
| terms are included in `LCM_LORA_LICENSE.md`. The use-based restrictions and |
| attribution requirements continue to apply. This repository adds no new |
| restrictions and does not claim authorship of the original model or adapter. |
|
|
| - CyberRealistic creator: Cyberdelia |
| - LCM-LoRA authors: Simian Luo, Yiqin Tan, Suraj Patil, Daniel Gu et al. |
| - Format conversion: LocalMuseAI |
|
|
