Bria FIBO 2.0 Image Edit edits one to four reference images with natural-language or structured instructions, supporting masked single-image edits, visual refinements, product visuals, marketing assets, creative content, and production workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
/filters:quality(85)/examples/feacd872c938ecac57657c15f68ac38d/333dc549-44c0-4b07-a4c4-42e2294ac7a9-5863c04bfd98.webp)
$0.04per run·~25 / $1
/filters:quality(82)/examples/feacd872c938ecac57657c15f68ac38d/333dc549-44c0-4b07-a4c4-42e2294ac7a9-5863c04bfd98.webp)
Transform the gown into an avant-garde couture piece combining matte black silk, polished silver structural panels, translucent smoked-glass sections, tiny ruby beadwork, fine gold embroidery, and layered organza. Preserve the designer, mannequin, pose, studio layout, camera angle, and original gown silhouette. Keep the garment elegant and wearable, with realistic textile folds, metal reflections, transparent material behavior, and intricate stitching. Premium fashion editorial, highly detailed.
Bria FIBO 2.0 Edit transforms existing images with natural-language instructions. Edit a single image, combine multiple references, or use a mask to focus changes on a specific area.
4 source or reference images in one request.21 supported aspect ratios or keep the first image's ratio.| Parameter | Required | Description |
|---|---|---|
| images | Yes | Between 1 and 4 source or reference images. Supports JPEG, JPG, PNG, and WEBP. |
| prompt | Conditional | Text instructions describing the desired edit. Required unless structured_instruction is provided. Do not use both. |
| mask_image | No | Black-and-white mask for single-image editing. White areas are edited and black areas are preserved. |
| structured_instruction | No | Advanced edit instructions as a JSON string, used instead of prompt. |
| aspect_ratio | No | Output aspect ratio: 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 16:10, 10:16, 2:1, 1:2, 21:9, 9:21, 3:1, 1:3, 4:1, 1:4, 5:1, or 1:5. Defaults to the first image's ratio when omitted. |
| output_format | No | Output format: png or jpeg. Default: png. |
| seed | No | Optional seed. Omit for random generation. |
1 and 4 source or reference images.prompt or provide a structured_instruction.png or jpeg.$0.04 per request.
images is required and supports 1–4 images.prompt or structured_instruction, not both.mask_image requires exactly one input image.aspect_ratio is omitted, the first image's ratio is used.aspect_ratio is specified. Large aspect-ratio changes may also reduce quality.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/bria/fibo-2.0/edit with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Fibo 2.0 Edit below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"aspect_ratio": "1:1",
"output_format": "png",
"prompt": "A cinematic shot of a city at sunset, soft golden light"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/bria/fibo-2.0/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="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(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' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout|deleted) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
*) sleep 2 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/bria/fibo-2.0/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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"aspect_ratio": "1:1",
"output_format": "png",
"prompt": "A cinematic shot of a city at sunset, soft golden light"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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", "deleted"].includes(result.status)) throw new Error(JSON.stringify(result));
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 = {
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"aspect_ratio": "1:1",
"output_format": "png",
"prompt": "A cinematic shot of a city at sunset, soft golden light"
}
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/bria/fibo-2.0/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 = 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", "deleted"}:
raise RuntimeError(result)
time.sleep(2)Fibo 2.0 Edit is a Bria model for image editing, exposed as a REST API on WaveSpeedAI. Bria FIBO 2.0 Image Edit edits one to four reference images with natural-language or structured instructions, supporting masked single-image edits, visual refinements, product visuals, marketing assets, creative content, and production workflows. Ready-to-use REST inference API, best performance, no coldstarts, 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 Python, JavaScript, and cURL examples for submitting requests and polling results. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/bria/bria-fibo-2.0-edit.
Fibo 2.0 Edit starts at $0.04 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`, `aspect_ratio`, `seed`, `mask_image`, `output_format`. 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/bria/bria-fibo-2.0-edit.
Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.
Commercial usage rights depend on the model's license, set by its provider (Bria). Check the provider's applicable terms and WaveSpeedAI's Terms of Service before commercial use.