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.
En attente

$0.08par exécution·~12 / $1

The girl stood by the sea watching the sunrise.

Turn it into pixel art

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

The couple stood in front of the Disney castle.

The girl stood among the flowers.

The woman is walking down the runway.
USO (Unified Style-Subject Optimized) is a model developed by UXO Team that unifies style-driven and subject-driven generation tasks.
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 URLs from data.outputs. Examples for Uso below.
# Submit the prediction
curl --fail-with-body --connect-timeout 10 --max-time 60 \
-X POST "https://api.wavespeed.ai/api/v3/bytedance/uso" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d '{
"sync_model": false
}'
# Wait at least 2 seconds, then poll. Safe GET requests may be retried.
curl --fail-with-body --connect-timeout 10 --max-time 30 \
--retry 4 --retry-all-errors --retry-delay 1 \
-X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
-H "Authorization: Bearer $WAVESPEED_API_KEY"
# Start at 2 seconds and increase the interval for long-running tasks.
# Stop on completed, failed, cancelled, or timeout.// npm install wavespeed
const { Client } = require('wavespeed');
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
const client = new Client(apiKey);
try {
const result = await client.run("bytedance/uso", {
"sync_model": false
}, {
timeout: 3600,
pollInterval: 2.0,
});
console.log(result.outputs);
} catch (error) {
console.error('Generation failed:', error);
process.exitCode = 1;
}# pip install wavespeed
import os
from wavespeed import Client
client = Client(api_key=os.environ["WAVESPEED_API_KEY"])
try:
output = client.run(
"bytedance/uso",
{
"sync_model": false
},
timeout=3600.0,
poll_interval=2.0,
)
print(output["outputs"])
except Exception as error:
raise SystemExit(f"Generation failed: {error}") from errorUso 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.
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/bytedance/bytedance-uso.
Uso starts at $0.080 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`, `seed`, `reference_images`, `sync_model`. 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.
Average end-to-end generation time on WaveSpeedAI is around 91 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (ByteDance). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.