ideogram/object-removal
Object Removal by Ideogram - AI-powered image editing, style transfer, and transformation. Edit photos with natural language instructions, remove backgrounds, change styles, and enhance images effortlessly.
PNG, JPEG, WebP, or GIF · 20 MiB maximum
PNG, JPEG, WebP, or GIF · 20 MiB maximum
Example output — click Run to generate your own
Send your first request
OpenAI-compatible endpoint with unified authentication and usage tracking.
https://api.sandbase.ai/v1/runideogram/object-removalInput Schema
2 parameters · 1 required · 1 optional
| Parameter | Type | Required | Description |
|---|---|---|---|
image | string | Required | The source image containing the object to remove (maximum file size 10MB). |
mask | string | Optional | A black-and-white mask matching the source image dimensions. White pixels are removed and black pixels are preserved (maximum file size 10MB). |
Output Schema
| Field | Type | Description |
|---|---|---|
id | string | Unique identifier for the generation task |
status | string | Task status: pending, running, completed, failed, timeout |
model | string | Model used for the generation |
outputs | array | Array of output items |
outputs[].url | string | URL of the generated artifact |
outputs[].content_type | string | MIME type (e.g. image/png, video/mp4) |
error | object | null | Error details if failed, null on success |
error.type | string | Machine-readable error type code |
error.message | string | Human-readable error description |
Async Workflow
This model uses asynchronous execution. Submit a request and poll for the result.
- Submit — POST to /v1/run, receive an
id - Poll — GET /v1/run/{id} until status is
completed,failed, ortimeout - Retrieve — Read
outputsfrom the completed response
Code Examples
Ready-to-run snippets
# Step 1: Submit
curl -X POST https://api.sandbase.ai/v1/run \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "ideogram/object-removal",
"mask": "https://v3.fal.media/files/kangaroo/1dd3zEL5MXQ3Kb4-mRi9d_indir%20(20).png",
"image": "https://v3.fal.media/files/panda/-LC_gNNV3wUHaGMQT3klE_output.png",
"prompt": "a beautiful sunset over mountains"
}'
# Step 2: Poll result (replace <id>)
curl https://api.sandbase.ai/v1/run/<id> \
-H "Authorization: Bearer YOUR_API_KEY"API README
Ideogram Object Removal
object removal in the Ideogram lineup is built to remove a selected object and reconstruct the visual content that belonged behind it. The object removal configuration combines that transformation with the quality and motion profile represented by this exact model version, so teams can choose it deliberately among adjacent family variants. It is useful when the input already establishes part of the creative intent and the model must supply a polished result rather than a generic media conversion, with subject identity, scene logic, visual hierarchy, and the delivery goal kept explicit.
For production work with ideogram/object-removal, begin with the non-negotiable content, then describe the desired change, framing, action, atmosphere, and finishing cues in that order. Separate what must remain recognizable from what may vary, and prefer concrete nouns and observable actions over abstract praise. That structure makes outputs easier to compare across storyboard passes, campaign variants, catalog assets, and other repeatable creative pipelines.
Highlights
Semantic object removal. Erases an unwanted subject instead of hiding it with a flat patch or blur.
Contextual inpainting. Reconstructs hidden background from nearby texture, structure, perspective, and light.
Boundary blending. Resolves edges and shadows around the removed region so the repair belongs in the image.
object removal composition cleanup. Handles distracting props, people, marks, and scene elements while retaining the rest. This is the defining creative strength of the object removal configuration.
Pricing
| Billing unit | Price |
|---|---|
| Per request | $0.03 |
When to Use
| ✅ Good fit | ❌ Consider alternatives |
|---|---|
| The model's named workflow matches the source material and intended output | A different input modality or model route is required |
| A managed asynchronous result is suitable for the production pipeline | A synchronous, interactive editor is essential |
| The documented controls cover the required duration, framing, or format | The project needs controls outside this endpoint's schema |
| Creative iteration benefits from a repeatable request structure | Exact deterministic pixels, frames, geometry, or samples are mandatory |
| A finished downloadable media asset is the desired deliverable | Editable source layers or a native project file are required |
Prompt Guide
For image-conditioned generation, state the intended result first, then add the subject or source treatment, progression, style, and delivery constraints. Keep one creative variable per phrase, use the documented field names for controls, and change one setting at a time when comparing results.
{
"image": "https://v3.fal.media/files/panda/-LC_gNNV3wUHaGMQT3klE_output.png"
}
Technical Specs
| Spec | Value |
|---|---|
| Model ID | ideogram/object-removal |
| Inputs | image, mask |
| Required inputs | image |
| Output fields | content_type, url |
| Execution | Async (submit, then poll for result) |