Image Inpaint Region

Image Inpaint Region (LaMa)

Local circular region inpainting with IOPaint LaMa. Builds a circular mask from --region cx,cy,r, then runs the same inference path as iopaint run / the IOPaint API.

Both --image and --region cx,cy,r are required.

Rules

When this skill applies, read and follow skill-dependency-manager — run scripts as documented, install missing tools into .dependency/.

• Run inpaint.py through the iopaint manifest entry (.dependency/iopaint/.venv/). Never use host python, py, python3, or any interpreter outside .dependency/.
• Do not hand-write equivalent inpaint commands — use the bundled script.
• Single file only. Pass one image with --image; directories are not supported.
• Require region first. If --region cx,cy,r is missing, inspect the image (or ask the user) before running.
• Use IOPaint defaults for inference config unless the user explicitly asks otherwise — do not invent custom mask blur, compositing, or hd_strategy overrides in the script.
• Pass the input path as-is. Output goes to <image-dir>/image-inpaint-region/ by default — no path rewriting.
• Never overwrite source files. Output lands in image-inpaint-region/ or --output.

Setup (first run)

IOPaint needs Python 3.11 and PyTorch. From project root:

.dependency/python-3.11/python.exe -m venv .dependency/iopaint/.venv
.dependency/iopaint/.venv/Scripts/python.exe -m pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
.dependency/iopaint/.venv/Scripts/python.exe -m pip install iopaint

CPU-only: skip the CUDA torch line and pip install iopaint (pulls CPU torch). Use bin/python on Unix.

Register in .dependency/manifest.json:

"iopaint": {
  "populated": true,
  "bin": ".dependency/iopaint/.venv/Scripts/python.exe"
}

LaMa weights (big-lama.pt) download on first run.

Quick Start

Both --image and --region are required:

# image/foo.png → image/image-inpaint-region/foo.png
# center (148, 248), radius 48
.dependency/iopaint/.venv/Scripts/python.exe .ai/image-inpaint-region/inpaint.py --image image/foo.png --region 148,248,48

GPU inference:

.dependency/iopaint/.venv/Scripts/python.exe .ai/image-inpaint-region/inpaint.py --image image/foo.png --region 148,248,48 --device cuda

Custom output path:

.dependency/iopaint/.venv/Scripts/python.exe .ai/image-inpaint-region/inpaint.py --image image/foo.png --region 148,248,48 -o image/foo_inpainted

How it works

1. Open the image as RGB (same as iopaint batch processing).
2. Draw a filled circle mask from --region cx,cy,r.
3. Binarize mask at 127 (same as iopaint.batch_processing / API).
4. Call ModelManager with InpaintRequest() defaults.
5. Write PNG via pil_to_bytes(..., "png", 100, ...).

IOPaint defaults used

These come from InpaintRequest() and iopaint run — the script does not override them:

The skill only adds --region cx,cy,r so you do not need a separate mask file.

Options

Supported inputs: .png, .jpg, .jpeg, .webp, .gif, .bmp, .tif, .tiff, .avif, .ico.

Agent Workflow

1. Confirm source — user path only; do not copy into image/ or use chat attachment cache.
2. Confirm region — inspect the image and determine circle center (cx, cy) and radius r before running.
3. One file per run — process one image, verify the result, then repeat for additional files if needed.
4. Inspect — check the inpainted area; leftover content → larger r; nearby art eaten → smaller r or move center.
5. Revert — delete the output file or git restore; sources are never modified.

Agent Notes

1. Use the bundled script, not hand-written IOPaint CLI with separate mask files unless the user already has a mask image.
2. Missing iopaint venv → populate .dependency/ per skill-dependency-manager, retry same command.
3. Do not copy, move, or replace the source with inpainted output — tell the user where the output file is.
4. For full IOPaint UI / brush mask editing, use the upstream iopaint start workflow instead.

Troubleshooting

CLI

Copy-paste commands: cli/image-inpaint-region.md

Related

• Engine: Sanster/IOPaint (LaMa)
• Upstream batch CLI: iopaint run --model lama --device cpu --image ... --mask ... --output ...