FLUX 2 flash Edit from Black Forest Labs delivers ultra-fast, production-grade image-to-image editing. Use plain-English prompts and precise HEX color control to restyle scenes, replace backgrounds, inpaint/outpaint, and retouch with brand-consistent palettes. Low-latency REST API, no cold starts, affordable pricing.
Idle

$0.013per run·~76 / $1

The woman wearing the cap in image 2 and sunglasses in image 3, same happy smile, same curly hair visible under the cap, same background and lighting, natural and realistic

Transform the elderly man into a young man in his late 20s, same person but younger with smooth youthful skin, no wrinkles, full brown hair instead of grey, shorter well-groomed brown beard, same facial structure and features, same contemplative expression gazing into distance, wearing the exact same brown tweed jacket and dark corduroy pants, holding the same wooden walking cane, sitting on the same stone wall, identical autumn countryside background with golden trees and green fields, same warm afternoon lighting, same composition and pose, only age changed

Transform the image's style into a realistic one.
FLUX.2 Flash Edit is a fast, cost-efficient image-to-image editing model for prompt-driven transformations. Give it one or more input images plus a clear instruction, and it will apply targeted edits while keeping the overall composition stable—great for high-throughput creative pipelines.
It’s best suited for quick style shifts, background tweaks, light retouching, and generating multiple variations for iteration or A/B testing.
Ultra-fast prompt-driven editing Apply natural-language edit instructions with low latency for rapid iteration.
Composition-stable transformations Keeps layout and key identity cues stable while applying targeted changes.
Multi-image editing (up to 4 inputs) Accepts multiple input image URLs to guide the edit and/or provide references.
Production-friendly controls
Deterministic results with seed, plus API-only options for synchronous responses and base64 outputs.
Size control for common creative formats Generate outputs sized for banners, posters, social crops, and product imagery.
width*height pixels (each dimension 256–1536).-1 for random; fixed value for repeatability).true, returns the output as a base64 string instead of a URL (API-only).true, waits for generation to finish and returns the result in the same response (API-only).Write prompts like an editor’s brief: what to change, what to keep, and what “done” looks like.
Tips that work well for FLUX.2 Flash Edit:
images must be 1–4 URLs pointing to your input images.download_url values here.size
Use width*height (for example 1024*1024, 832*1216, 1216*832).
Each dimension must be 256–1536.
seed
-1 (default behavior) for a new variation each run
A fixed integer (for example 12345) for repeatable results and controlled iteration
enable_sync_mode (API-only)
false (default): submit a task, then query the prediction result
true: wait and return the finished output in the same response (simpler integration, but the request takes longer)
enable_base64_output (API-only)
false (default): outputs are URLs
true: outputs are base64 strings (useful when you can’t fetch remote URLs)
After you finish configuring the parameters, click Run, preview the result, and iterate if needed.
$0.013 per run
size and seed fixed and vary only the prompt.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-2-flash/edit with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Flux 2 Flash Edit below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"seed": -1
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-2-flash/edit" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
-H "Authorization: Bearer $WAVESPEED_API_KEY")
RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$STATUS" in
completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
created|processing) sleep 2 ;;
*) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-2-flash/edit";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
await new Promise(resolve => setTimeout(resolve, 2000));
}import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"seed": -1
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-2-flash/edit", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout"}:
raise RuntimeError(result)
if status not in {"created", "processing"}:
raise RuntimeError(f"Unexpected status: {status}")
time.sleep(2)Flux 2 Flash Edit is a WaveSpeedAI model for image editing, exposed as a REST API on WaveSpeedAI. FLUX 2 flash Edit from Black Forest Labs delivers ultra-fast, production-grade image-to-image editing. Use plain-English prompts and precise HEX color control to restyle scenes, replace backgrounds, inpaint/outpaint, and retouch with brand-consistent palettes. Low-latency REST API, no cold starts, affordable pricing. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/flux-2-flash-edit.
Flux 2 Flash Edit starts at $0.013 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `prompt`, `images`, `seed`, `enable_base64_output`, `enable_sync_mode`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/flux-2-flash-edit.
Median end-to-end generation time on WaveSpeedAI is around 7 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.