# Stable Diffusion 1.5 LCM Inpainting — CoreML (SHxTLxST)

CoreML `.mlpackage` versions of [SimianLuo/LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7) configured for inpainting, palettized per Apple's LCM recommendation (TextEncoder/VAE at 6-bit, UNet at 8-bit).

Used by [SHxTLxST](https://github.com/byteReactr/SHxTLxST) iOS app for lifestyle image generation from masked product photos.

## Submodels

| Submodel | nbits | Size | Purpose |
|---|---|---|---|
| `TextEncoder.mlpackage` | 6-bit | 88 MB | CLIP text encoding |
| `Unet.mlpackage` | 8-bit | 821 MB | Latent diffusion (inpainting) |
| `VAEEncoder.mlpackage` | 6-bit | 25 MB | Image -> latent |
| `VAEDecoder.mlpackage` | 6-bit | 36 MB | Latent -> image |

**Total:** 970 MB (well within iOS app size budgets)

## Usage (iOS, Swift)

```swift
import CoreML

let config = MLModelConfiguration()
config.computeUnits = .all  // ANE + GPU + CPU

let textEncoder = try TextEncoder(configuration: config)
let unet = try Unet(configuration: config)
let vaeEncoder = try VAEEncoder(configuration: config)
let vaeDecoder = try VAEDecoder(configuration: config)
```

For inference, see [apple/ml-stable-diffusion](https://github.com/apple/ml-stable-diffusion) — this package is drop-in compatible, with the UNet modified to accept a 9-channel input (4 latent + 4 masked image latent + 1 mask) per the inpainting convention.

## Files

- `Resources/` — the four `.mlpackage` submodels
- `tokenizer/` — CLIP tokenizer (vocab.json + merges.txt)
- `scheduler_config.json` — LCM scheduler config
- `manifest.json` — per-submodel quantization metadata

## Source

- Original PyTorch model: [SimianLuo/LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7)
- Base: runwayml/stable-diffusion-v1-5
- Conversion script: [convert_sd_inpaint_apple.py](https://github.com/byteReactr/SHxTLxST/scripts/convert_sd_inpaint_apple.py)
- Conversion pipeline: trace on Linux+GPU -> convert on macOS (coremltools `BlobWriter` is macOS-only)

## License

Original SD 1.5: CreativeML Open RAIL-M. The CoreML conversion is a derivative work; this repo inherits the Open RAIL-M license.