Seedance 2.0 Fast (Text-to-Video Turbo) generates cinematic 720p/1080p videos from text prompts using speed-optimized inference —the fastest and most affordable Seedance option with native audio-visual synchronization and director-level control.
Inactivo
$0.6por ejecución·~16 / $10
A businesswoman in a perfectly pressed suit power-walks through a corporate lobby. Tracking shot at her pace. She doesn't notice a golden retriever in a tiny business suit walking the opposite direction, carrying a briefcase in its mouth. They pass each other. Camera does a comedic whip-pan back to the dog, who stops, adjusts its tie with one paw, and enters the elevator. The doors close. Wes Anderson symmetry, pastel color palette, deadpan framing.
Sweeping helicopter shot over an endless frozen battlefield at sunrise. Thousands of fallen warriors lie in the snow. Camera tilts down and tracks forward at ground level through the carnage. Stops on a single sword planted upright in the ice, a crown hanging from its hilt. A gauntleted hand reaches into frame and grips the sword. Camera racks focus to reveal a lone surviving knight, battered armor, breath visible in cold air. She pulls the sword free. Orchestral tension, Lord of the Rings scale, golden rim light.
Seedance 2.0 Fast Text-to-Video Turbo is the fastest and most affordable Seedance option — combining speed-optimized inference to deliver 720p and 1080p cinematic videos with native audio-visual synchronization.
Fastest Seedance option Speed-optimized inference for maximum throughput.
Affordable HD output Starting at $0.60 per 5 seconds at 720p.
Unified multimodal architecture Same Seedance 2.0 foundation handling text, image, audio, and video inputs.
Native audio-visual synchronization Generates video with synchronized audio in a single pass.
Director-level control Camera movement, lighting, shadows, and character performance controlled through prompts.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Detailed description of the cinematic scene |
| aspect_ratio | No | Output format: 16:9 (default), 9:16, 4:3, 3:4, 1:1, 21:9 |
| duration | No | Video length in seconds: 4-15 (default: 5) |
| resolution | No | Output resolution: 720p (default) or 1080p |
| enable_web_search | No | Enable web search for real-time information (default: false) |
| generate_audio | No | Generate synchronized audio for the output video (default: true) |
| reference_images | No | Reference image URLs to guide style, characters, or composition |
| reference_videos | No | Reference video URLs (total length must not exceed 15 seconds) |
| reference_audios | No | Reference audio URLs (total length must not exceed 15 seconds) |
| Resolution | Duration | Without Reference Videos | With Reference Videos |
|---|---|---|---|
| 720p | 5 s | $0.60 | $1.10 |
| 720p | 10 s | $1.20 | $2.20 |
| 720p | 15 s | $1.80 | $3.30 |
| 1080p | 5 s | $0.70 | $1.20 |
| 1080p | 10 s | $1.40 | $2.40 |
| 1080p | 15 s | $2.10 | $3.60 |
Notice: Use @image1, @image2, @audio1, etc. to reference your uploaded assets. The references will stay as plain text—don't worry.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/bytedance/seedance-2.0-fast/text-to-video-turbo with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Seedance 2.0 Fast Text To Video Turbo below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "16:9",
"resolution": "720p",
"duration": 5,
"enable_web_search": false,
"generate_audio": true
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/bytedance/seedance-2.0-fast/text-to-video-turbo" \
-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/bytedance/seedance-2.0-fast/text-to-video-turbo";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "16:9",
"resolution": "720p",
"duration": 5,
"enable_web_search": false,
"generate_audio": true
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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",
"aspect_ratio": "16:9",
"resolution": "720p",
"duration": 5,
"enable_web_search": False,
"generate_audio": True
}
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/seedance-2.0-fast/text-to-video-turbo", 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)Seedance 2.0 Fast Text To Video Turbo is a ByteDance model for video generation, exposed as a REST API on WaveSpeedAI. Seedance 2.0 Fast (Text-to-Video Turbo) generates cinematic 720p/1080p videos from text prompts using speed-optimized inference —the fastest and most affordable Seedance option with native audio-visual synchronization and director-level control. 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-seedance-2.0-fast-text-to-video-turbo.
Seedance 2.0 Fast Text To Video Turbo starts at $0.60 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`, `aspect_ratio`, `resolution`, `duration`, `reference_images`, `enable_web_search`. 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-seedance-2.0-fast-text-to-video-turbo.
Median end-to-end generation time on WaveSpeedAI is around 174 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 (ByteDance). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.