Bria Fibo Edit Genfill
Playground
Try it on WavespeedAI!BRIA FIBO Edit GenFill is a fast AI generative fill image editing model that fills masked regions in an image from a text prompt using BRIA’s licensed-data image editing API. Ready-to-use REST inference API for inpainting, object replacement, background repair, product image editing, creative retouching, marketing assets, and professional image editing workflows with simple integration, no coldstarts, and affordable pricing.
Features
Bria FIBO Edit GenFill
Bria FIBO Edit GenFill fills masked regions in an image using a natural-language prompt. Upload a source image, provide a mask, and describe what should appear in the masked area to generate a clean, context-aware edit.
Why Choose This?
-
Text-guided generative fill
Replace or insert content inside a masked region using a prompt. -
Precise mask-based control
Limit generation to exactly the area you want to edit. -
Clean editing workflow
Ideal for object replacement, scene cleanup, product retouching, and controlled inpainting. -
Licensed-data Bria pipeline
Built on Bria’s commercial-friendly image editing workflow. -
Simple input setup
Works with direct URLs or uploaded image assets.
Parameters
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Source image to edit. |
| mask_image | Yes | Mask image that marks the region to fill. |
| prompt | Yes | Text description of what to generate inside the masked area. |
How to Use
- Upload the source image — provide the image you want to edit.
- Upload the mask image — clearly mark the region that should be filled or replaced.
- Write your prompt — describe the desired content, style, material, color, lighting, or object.
- Submit — run the model and download the generated result.
Example Use Case
Fill an empty tabletop region with a realistic ceramic vase that matches the lighting and perspective of the original photo.
Pricing
| Output | Cost |
|---|---|
| Per image | $0.04 |
Billing Rules
- Each generated image costs $0.04
- Pricing is fixed per image
Best Use Cases
- Object insertion — Add products, props, or scene elements into a masked area.
- Object replacement — Swap one item for another while preserving the rest of the composition.
- Photo cleanup — Remove distractions and regenerate the background naturally.
- Product retouching — Refine catalog or marketing visuals with controlled edits.
- Creative compositing — Build prompt-driven image edits with precise spatial control.
Pro Tips
- Use a clean, accurate mask for better fill quality.
- Be specific in the prompt about object type, material, color, and lighting.
- Mention perspective or scene context when realism matters.
- Smaller, well-defined masked regions usually produce more stable results.
- Match the prompt to the style of the original image for more natural blending.
Notes
image,mask_image, andpromptare all required.- The mask should clearly define the area to be filled.
- Better prompts usually improve control over object appearance and scene consistency.
- Pricing is fixed per generated image.
Related Models
- Bria image editing workflows — Useful when you need other prompt-based edit or inpainting styles.
- Bria fill and replace workflows — Useful for more targeted commercial image editing tasks.
Authentication
For authentication details, please refer to the Authentication Guide.
API Endpoints
Submit Task & Query Result
# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/bria/fibo-edit/genfill" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"
Parameters
Task Submission Parameters
Request Parameters
| Parameter | Type | Required | Default | Range | Description |
|---|---|---|---|---|---|
| image | string | Yes | - | The source image to edit. | |
| mask_image | string | Yes | - | Mask image that marks the region to fill. | |
| prompt | string | Yes | - | Describe what should be generated inside the masked area. |
Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data.id | string | Unique identifier for the prediction, Task Id |
| data.model | string | Model ID used for the prediction |
| data.outputs | array | Array of URLs to the generated content (empty when status is not completed) |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |
Result Request Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| id | string | Yes | - | Task ID |
Result Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data | object | The prediction data object containing all details |
| data.id | string | Unique identifier for the prediction, the ID of the prediction to get |
| data.model | string | Model ID used for the prediction |
| data.outputs | string | Array of URLs to the generated content. |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |