Edit images using Flux models
June 3, 2025
Table of contents
This endpoint edits existing images using FLUX.1 Kontext model based on text prompts and reference images.
https://api.useapi.net/v1/ltxstudio/images/flux-edit
Request Headers
Authorization: Bearer {API token}
Content-Type: application/json
# Alternatively you can use multipart/form-data
# Content-Type: multipart/form-data
API token
is required, see Setup useapi.net for details.
Request Body
{
"prompt": "Add golden sunset lighting to the landscape",
"referenceAssetId": "asset:3b18…-type:image/png",
"aspectRatio": "16:9",
"seed": 123456
}
email
is optional when only one account configured.
However, if you have multiple accounts configured, this parameter becomes required.prompt
is required, text description for image editing (max 2000 characters)referenceAssetId
is required, asset ID for the source image to edit (use fieldfileId
from POST /assets/?type=reference-image)aspectRatio
is optional, output image aspect ratio.
Supported values:16:9
,9:16
,1:1
.
Default is16:9
.seed
is optional, random seed for reproducible results.
Default is random.pollForResult
is optional, number of polling attempts to wait for completion (each attempt waits 3 seconds, up to 60 seconds total max) before sending job to the scheduler.
For example,pollForResult: 5
will poll every 3 seconds for up to 15 seconds total.
Supported values: 1-20.
Default is immediate return with jobId.maxJobs
is optional, override the default maximum number of concurrent jobs.replyUrl
is optional, webhook URL for job completion notifications.
See GET assets/jobId
for response model.replyRef
is optional, custom reference for webhook identification.
Responses
-
{ "jobId": "email:[email protected]:7a34b821-9fd0-205e-d21b-4abc6f7839e7-type:image", "generationId": "gen_abc123def456" }
-
200 OK (pollForResult completed)
{ "status": { "type": "completed", "progress": 100, "artifact": { "assetUrl": "https://storage.googleapis.com/lt-infinity-prd/artifacts/vertex-ai/…", "expirationDateString": "1748919477350", "asset": { "type": "artifact", "fileId": "asset:3b18…-type:image/png", "mimeType": "image/png", "artifactSubtype": "vertex-ai" } } }, "jobId": "email:[email protected]:7a34b821-9fd0-205e-d21b-4abc6f7839e7-type:image", "replyRef": "custom-reference-123", "replyUrl": "https://webhook.example.com/ltx-callback", "code": 200 }
-
{ "error": "Error message", "code": 400 }
-
{ "error": "Unauthorized", "code": 401 }
-
{ "error": "Insufficient credits", "code": 402 }
Model
Use GET assets/jobId
to retrieve job status and results if they were not provided with the response.
{ // TypeScript, all fields are optional
status: {
type: 'active' | 'completed' | 'failed'
progress?: number
message?: string
artifact?: {
assetUrl: string
expirationDateString: string
asset: {
type: string
fileId: string
mimeType: string
artifactSubtype: string
}
}
}
jobId?: string
generationId?: string
replyRef?: string
replyUrl?: string
code?: number
}
Examples
-
curl "https://api.useapi.net/v1/ltxstudio/images/flux-edit" \ -H "Authorization: Bearer …" \ -H "Content-Type: application/json" \ -d '{ "prompt": "Add golden sunset lighting to the landscape", "referenceAssetId": "asset:3b18…-type:image/png", "aspectRatio": "16:9", "pollForResult": 10 }'
-
const token = "API token"; const apiUrl = "https://api.useapi.net/v1/ltxstudio/images/flux-edit"; const response = await fetch(apiUrl, { method: "POST", headers: { "Authorization": `Bearer ${token}`, "Content-Type": "application/json", }, body: JSON.stringify({ "prompt": "Add golden sunset lighting to the landscape", "referenceAssetId": "asset:3b18…-type:image/png", "aspectRatio": "16:9", "pollForResult": 10 }) }); const result = await response.json(); console.log("response", {response, result});
-
import requests token = "API token" apiUrl = "https://api.useapi.net/v1/ltxstudio/images/flux-edit" headers = { "Authorization" : f"Bearer {token}", "Content-Type": "application/json" } data = { "prompt": "Add golden sunset lighting to the landscape", "referenceAssetId": "asset:3b18…-type:image/png", "aspectRatio": "16:9", "pollForResult": 10 } response = requests.post(apiUrl, headers=headers, json=data) print(response, response.json())