Remove Objects Advanced uses cutting-edge AI technology to intelligently remove unwanted objects such as text and people based on a mask image, delivering fast and precise results for image enhancement.
https://www.ailabapi.com/api/image/editing/remove-objects-advancedPOSTmultipart/form-dataJPEG JPG PNG| Field | Required | Type | Description |
|---|---|---|---|
ailabapi-api-key | YES | string | Application API KEY. Get API KEY |
| Field | Required | Type | Scope | Default | Description |
|---|---|---|---|---|---|
image | YES | file | Original image. | ||
mask | YES | file | Mask image. | ||
steps | NO | integer | [1, +] | 30 | Sampling steps determine the level of detail in the generated image. A higher value may result in better quality, but it will significantly increase the processing time. |
strength | NO | float | [0.1, 1.0] | 0.8 | The smaller the value, the closer it is to the original image. |
scale | NO | float | [1, 20] | 7 | The degree to which the text description influences the output. |
seed | NO | integer | [-1, +] | 0 | Random seed, used as the basis for determining the initial state of the diffusion process. It must be a non-negative number (-1 represents a random seed). If the random seed is the same positive integer and all other parameters are identical, the generated image will most likely be consistent. |
dilate_size | NO | integer | [1, +] | 15 | Mask Dilation Radius. The mask used for object removal should fully encompass the target object. When users manually draw the mask, it often extends beyond the object’s boundary. However, if the mask is generated by a segmentation algorithm, it typically adheres closely to the object’s edges, which might leave parts of the object uncovered. This can lead to incomplete removal or unexpected artifacts during generation. To avoid such issues, it’s recommended to appropriately increase the dilate_size parameter to ensure the mask fully covers the object. |
quality | NO | string | H, M, L | M | H`: High quality — best output quality, but slightly slower processing.`, M: Medium quality — balanced in both quality and speed., “L: Low quality — fastest processing, suitable for scenarios where speed is prioritized. |
| Field | Type | Description |
|---|---|---|
data | object | The content of the result data returned. |
+binary_data_base64 | array of string | Output the processed image as a Base64 array (single image). |
API Key for authentication
Original image.
Mask image.
Sampling steps determine the level of detail in the generated image. A higher value may result in better quality, but it will significantly increase the processing time.
"30"
The smaller the value, the closer it is to the original image.
"0.8"
The degree to which the text description influences the output.
"7"
Random seed, used as the basis for determining the initial state of the diffusion process. It must be a non-negative number (-1 represents a random seed). If the random seed is the same positive integer and all other parameters are identical, the generated image will most likely be consistent.
"0"
Mask Dilation Radius. The mask used for object removal should fully encompass the target object. When users manually draw the mask, it often extends beyond the object's boundary. However, if the mask is generated by a segmentation algorithm, it typically adheres closely to the object's edges, which might leave parts of the object uncovered. This can lead to incomplete removal or unexpected artifacts during generation. To avoid such issues, it's recommended to appropriately increase the dilate_size parameter to ensure the mask fully covers the object.
"15"
Quality Parameter.
H: High quality — best output quality, but slightly slower processing.M: Medium quality — balanced in both quality and speed.L: Low quality — fastest processing, suitable for scenarios where speed is prioritized."M"
Success
The response is of type object.