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2.2 KiB
2.2 KiB
description
| description |
|---|
| Remove unwanted objects from images with AI inpainting (LaMa), guided by a mask of the region to erase. |
Object Eraser
Remove unwanted objects from images using AI inpainting (LaMa model). Accepts an image and a mask indicating the region to erase.
API Endpoint
POST /api/v1/tools/image/erase-object
Processing: Asynchronous (returns 202, poll /api/v1/jobs/{jobId}/progress for status via SSE)
Model bundle: object-eraser-colorize (1-2 GB)
Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| file | file | Yes | - | Source image file (multipart) |
| mask | file | Yes | - | Mask image (white = area to erase, black = keep). Must be uploaded with fieldname mask |
| format | string | No | "auto" |
Output format: auto, png, jpg, jpeg, webp, tiff, gif, avif, heic, heif, jxl |
| quality | integer | No | 95 |
Output quality (1-100) |
Example Request
curl -X POST http://localhost:1349/api/v1/tools/image/erase-object \
-F "file=@photo.jpg" \
-F "mask=@mask.png" \
-F "format=png" \
-F "quality=95"
Response
Initial Response (202 Accepted)
{
"jobId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
"async": true
}
Progress (SSE at /api/v1/jobs/{jobId}/progress)
event: progress
data: {"phase":"processing","stage":"Inpainting...","percent":70}
Final Result (via SSE)
{
"phase": "complete",
"percent": 100,
"result": {
"jobId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
"downloadUrl": "/api/v1/download/{jobId}/photo_erased.png",
"previewUrl": "/api/v1/download/{jobId}/preview.webp",
"originalSize": 245000,
"processedSize": 230000
}
}
Notes
- Requires the
object-eraser-colorizemodel bundle to be installed (1-2 GB). - The mask must be the same dimensions as the source image. White pixels indicate areas to erase; the AI fills them with plausible content.
- Uses LaMa (Large Mask Inpainting) for high-quality object removal.
- For non-browser-previewable output formats, a WebP preview is generated alongside the main output.
- Supports HEIC/HEIF, RAW, TGA, PSD, EXR, and HDR input formats via automatic decoding.