README.md

---
library_name: coreml
pipeline_tag: image-to-image
base_model: neonforestmist/Clover-Image-Tiny-Inpaint
license: creativeml-openrail-m
tags:
  - coreml
  - ios
  - inpainting
  - stable-diffusion
---

# Clover Image Tiny Inpaint — Core ML

The production Core ML conversion of
[`neonforestmist/Clover-Image-Tiny-Inpaint`](https://huggingface.co/neonforestmist/Clover-Image-Tiny-Inpaint)
for fully local 512 × 512 inpainting on iPhone and iPad. It uses the same
trained nine-channel inpainting checkpoint as the Diffusers release; Core ML
changes the execution format, not the learned weights.

Use this model with
[`neonforestmist/Clover-Image-Tiny-iOS`](https://github.com/neonforestmist/Clover-Image-Tiny-iOS).
The app downloads, verifies, and installs the resources automatically.

## Recommended release

The production entry point is [`manifest-pipeline.json`](manifest-pipeline.json).
It describes a **1.79 GB**, two-stage FP16 Core ML pipeline for iOS 18 or newer.
Core ML owns the handoff between the stages, which reduces the lifetime of
large intermediate tensors without changing the U-Net calculation.

| Resource | Purpose | Size |
|---|---|---:|
| `pipeline-v1/UnetPipeline.mlmodelc` | Full nine-channel FP16 inpainting U-Net | 1.72 GB |
| `VAEEncoder.mlmodelc` | Encodes the masked source image | 68.5 MB |
| `manifest-pipeline.json` | Pinned paths, sizes, and SHA-256 checksums | — |

The separate Clover installation is still required. Its approximately
**994.9 MB** runtime provides the tokenizer, text encoder, and VAE decoder, so
this repository does not download duplicate copies of those components.

The repository also retains compressed and stateful research artifacts for
comparison. The shipping iOS app uses `manifest-pipeline.json`, not the older
`manifest.json` entry point.

## Runtime contract

- Minimum OS: iOS 18
- Resolution: 512 × 512
- U-Net sample input: Float16 `[1, 9, 64, 64]`
- Timestep input: Float16 `[1]`
- Text input: Float16 `[1, 768, 1, 77]`
- Noise prediction output: Float32 `[1, 4, 64, 64]`
- Channels: noisy latent (4) + mask (1) + masked-image latent (4)
- Mask semantics: white regenerates, black preserves
- Recommended scheduler: DPM-Solver++ multistep
- Recommended settings: 20 steps, guidance scale 6.0

The selected source region is replaced by neutral gray before VAE encoding,
which becomes zero after normalization to `[-1, 1]`. For a small mask, the iOS
runtime uses a focused 512 × 512 crop with surrounding source context and then
composites the result through the exact user mask. Pixels outside the mask are
copied directly from the source image.

## LoRA compatibility

LoRAs made for the regular Clover Create model target its four-channel U-Net
and cannot be applied directly to this nine-channel inpainting U-Net. To ship
an inpainting style, train or adapt it for the inpainting checkpoint and fuse
it before Core ML conversion.

## Validation

The production bundle is checked in three ways:

- every file is verified against the byte count and SHA-256 in the schema-v3 manifest;
- the compiled pipeline passes a native macOS Core ML prediction smoke test with finite output and the expected tensor shape;
- the complete app flow was exercised on a physical iPhone 15 running iOS 26.6 using a 30-step masked edit.

The physical-device run completed successfully in about 73 seconds in the
XCTest/debug validation environment. That timing is a release smoke test, not
a formal performance benchmark; first-run Core ML compilation and device
thermal state can materially change latency.

## Citation

```bibtex
@software{lozadaperez2026cloverimagetinyinpaintcoreml,
  author = {Lukas Lozada Perez},
  title = {Clover Image Tiny Inpaint Core ML},
  year = {2026},
  url = {https://huggingface.co/neonforestmist/Clover-Image-Tiny-Inpaint-CoreML}
}
```

Designed and developed independently by Lukas Lozada Perez. Model weights are
available under the CreativeML Open RAIL-M license. Inference runs completely
on device after installation.