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bytedance/uso

USO (Unified Style-Subject Optimized) by unifies style-driven and subject-driven generation to produce consistent outputs that blend artistic style with subject fidelity. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Image Editing
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Inactivo

The girl stood by the sea watching the sunrise.

$0.5por ejecución·~20 / $10

Siguiente:

EjemplosVer todo

The girl stood by the sea watching the sunrise.

The girl stood by the sea watching the sunrise.

Turn it into pixel art

Turn it into pixel art

A woman stands in the desert, gazing up at the starry sky.

A woman stands in the desert, gazing up at the starry sky.

The couple stood in front of the Disney castle.

The couple stood in front of the Disney castle.

The girl stood among the flowers.

The girl stood among the flowers.

The woman is walking down the runway.

The woman is walking down the runway.

Modelos relacionados

README

USO (Unified Style-Subject Optimized)

USO combines subject and style references to generate images that follow your prompt while preserving visual qualities from the supplied references.

Why Choose This?

  • Subject and style guidance
    Use reference images to guide the subject, identity, appearance, or visual style of the generated image.

  • Multi-reference input
    Provide up to four reference images in a single request.

  • Prompt-guided generation
    Add a prompt to describe the desired image, or leave it empty for reference-driven generation.

  • Reproducible results
    Set a seed to make generations easier to reproduce.

Parameters

ParameterRequiredDescription
reference_imagesYesOne to four reference images. The first image is used as the content reference; following images provide style or additional visual references.
promptNoText instructions for the generated image. May be left empty for reference-driven generation.
seedNoRandom seed for reproducible generation. Use -1 for a random seed.
sync_modelNoSync the model to the latest version. Default: false.

How to Use

  1. Add one to four images to reference_images.
  2. Put the main content reference first, followed by any style or additional references.
  3. Add a prompt to describe the desired result, or leave it empty for reference-driven generation.
  4. Set a seed for reproducible results, or use -1 for a random seed.
  5. Submit the request and retrieve the generated image URL from the result.

Pricing

Pricing is $0.50 per generated image, based on an output limit of up to 4 MP.

Output sizePrice
Up to 4 MP$0.50

Best Use Cases

  • Consistent subject generation — Guide the result with one or more subject references.
  • Style transfer — Apply a visual style from reference images to a new composition.
  • Creative exploration — Combine visual references with a prompt to explore different directions.
  • Marketing and concept art — Create images that follow a consistent subject or visual identity.

Pro Tips

  • Put the most important content or subject image first.
  • Use clear reference images with minimal compression artifacts.
  • Add a concise prompt when you need to specify how the references should be combined.
  • Use a fixed seed when comparing prompt changes.

Notes

  • Provide between one and four reference images.
  • The output-size limit for this pricing is 4 MP.
  • seed and sync_model do not add separate charges.
Nota:Este sitio web utiliza modelos de IA proporcionados por terceros. Los precios de la documentación son orientativos y pueden estar desactualizados. El botón Generate muestra una estimación; prevalece el cargo final de la tarea.

Uso API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/bytedance/uso 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 Uso below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "reference_images": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ]
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/bytedance/uso" \
  -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
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/bytedance/uso";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "reference_images": [
                "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
        ]
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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));
}
Python example
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 = {
    "reference_images": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ]
}

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/bytedance/uso", 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)

Uso API — Frequently asked questions

What is the Uso API?

Uso is a ByteDance model for image editing, exposed as a REST API on WaveSpeedAI. USO (Unified Style-Subject Optimized) by unifies style-driven and subject-driven generation to produce consistent outputs that blend artistic style with subject fidelity. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Uso API?

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/bytedance/bytedance-uso.

How much does Uso cost per run?

Uso starts at $0.5 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.

What inputs does Uso accept?

Key inputs: `prompt`, `seed`, `reference_images`. 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/bytedance/bytedance-uso.

How long does Uso take to generate?

Reported generation time on WaveSpeedAI is around 147 seconds per request. This is an estimate, not a latency guarantee; queue time and input settings can change the total wait. live status is visible in the prediction record.

Can I use Uso outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (ByteDance). Check the provider's applicable terms and WaveSpeedAI's Terms of Service before commercial use.

llms.txt — bytedance/uso for AI agents and LLMs