Seedance 2.5 (Image-to-Video) generates Hollywood-grade cinematic videos from reference images and text prompts with native audio-visual synchronization, director-level camera and lighting control, and exceptional motion stability. Built on Seed's unified multimodal architecture, it preserves the input image's subject and composition while adding expressive, physically accurate motion.
Idle
$0.81per run·~12 / $10
5-second continuous POV hidden camera shot. The camera is buried inside a messy pile of random household objects, looking upward through gaps between clothes, boxes, books, and clutter. The scene takes place in a realistic messy home environment. A furious person is aggressively searching through the mess. Their hands move quickly, digging through objects, throwing things aside, and pushing items away in frustration. Their sleeves, hands, and random objects constantly pass in front of the lens, blocking the view. Objects hit the camera, causing realistic camera shake, vibration, motion blur, and autofocus shifts. While searching, the person angrily complains and shouts: " Who moved my stuff?! This place is a complete mess! I can't find anything!" The person's frustration keeps building. They breathe heavily, move faster, toss objects around, and lose patience. The environment feels chaotic and real, with natural physics and messy object movement. Raw documentary style, realistic home environment, handheld hidden camera POV, immersive first-person perspective, cinematic lighting, realistic human movement, natural facial expressions, realistic physics, ultra realistic, 5-second continuous shot.
Seedance 2.5 Image-to-Video generates cinematic videos from a reference image and text prompt. It preserves the input image's subject, composition, and style while adding expressive motion, camera movement, scene progression, and synchronized audio.
Image-to-video generation
Animate a reference image into a cinematic video with prompt-guided motion.
Image-faithful results
Preserve the input image's subject identity, composition, lighting, and style while generating motion.
Optional last-frame guidance
Use last_image to guide the final frame or ending direction of the generated video.
Native audio generation
Generate synchronized audio together with the video output.
Director-style prompt control
Control camera movement, lighting, mood, subject action, and scene progression through natural-language prompts.
High-resolution output
Generate videos in 480p, 720p, 1080p, or 4k.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Detailed description of the cinematic scene, action, camera movement, lighting, and mood. |
| image | Yes | Start image URL to guide the video generation. |
| last_image | No | Last-frame image URL for video continuation or ending-frame guidance. |
| duration | No | Video length in seconds. Range: 4–30. Default: 5. |
| resolution | No | Output resolution: 480p, 720p, 1080p, or 4k. Default: 720p. |
| generate_audio | No | Generate synchronized audio for the output video. Default: true. |
The output aspect ratio follows the input image automatically.
last_image when you want to guide the final frame or ending direction.4 to 30 seconds.480p, 720p, 1080p, or 4k.generate_audio enabled when synchronized audio is needed.The input image sets the first frame — your prompt directs what happens from there. The clearer the motion and camera direction, the better the result.
Describe the action that begins from your image: what the subject does, where it goes, how the camera moves. Don't re-describe what's already visible in the image — build on it.
The woman in the photo turns from the window and walks toward the camera; slow push-in, warm interior light.
For longer clips, break the motion into timed segments (1-second granularity), continuous, no gaps.
0-2s: She turns from the window, a faint smile forming. Camera holds. 2-5s: She walks toward the lens and stops mid-frame; slow push-in, shallow focus. 5-8s: She glances off-screen as light shifts from warm to cool. Handheld drift.
Lock the look for the whole clip: camera style, lighting, palette, atmosphere, audio.
Write these directly; the model understands them:
For a niche term, add a plain-language gloss: "rack focus: the sharp foreground softens as the background comes into focus."
When you attach images, videos, or audio, bind each one explicitly in the prompt by its upload order — @image1, @video1, @audio1 — and say what it's for. Don't rely on labels drawn inside the image itself.
When a reference is already accurate, just point to it — no need to re-describe it in detail.
Positive descriptions work best, but you can suppress subtitles and audio:
| prompt | |
|---|---|
| weak | make it move |
| strong | The parked car in the image pulls out and drives down the wet street. 0-3s: headlights flick on, the car eases forward, reflections rippling on the asphalt. 3-6s: it accelerates past the camera, which pans to follow. Low angle, neon reflections, light rain, cinematic night mood. Engine and rain sounds, no music. |
Pricing is based on selected resolution and duration.
| Resolution | Per 5s | Per second |
|---|---|---|
| 480p | $0.90 | $0.18 |
| 720p | $1.80 | $0.36 |
| 1080p | $4.50 | $0.90 |
| 4k | $9.00 | $1.80 |
| Resolution | 5s | 10s | 15s | 30s |
|---|---|---|---|---|
| 480p | $0.90 | $1.80 | $2.70 | $5.40 |
| 720p | $1.80 | $3.60 | $5.40 | $10.80 |
| 1080p | $4.50 | $9.00 | $13.50 | $27.00 |
| 4k | $9.00 | $18.00 | $27.00 | $54.00 |
image and last_image to guide both the beginning and ending of the video.last_image when the ending frame or final visual direction matters.480p or 720p for testing and 1080p or 4k for higher-resolution output.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/bytedance/seedance-2.5/image-to-video with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Seedance 2.5 Image To Video below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"resolution": "720p",
"duration": 5,
"generate_audio": true
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/bytedance/seedance-2.5/image-to-video" \
-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/bytedance/seedance-2.5/image-to-video";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"resolution": "720p",
"duration": 5,
"generate_audio": true
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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 = {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"resolution": "720p",
"duration": 5,
"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-2.5/image-to-video", 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)Seedance 2.5 Image To Video is a ByteDance model for video generation from images, exposed as a REST API on WaveSpeedAI. Seedance 2.5 (Image-to-Video) generates Hollywood-grade cinematic videos from reference images and text prompts with native audio-visual synchronization, director-level camera and lighting control, and exceptional motion stability. Built on Seed's unified multimodal architecture, it preserves the input image's subject and composition while adding expressive, physically accurate motion. 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/bytedance/bytedance-seedance-2.5-image-to-video.
Seedance 2.5 Image To Video starts at $0.81 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`, `image`, `resolution`, `duration`, `generate_audio`, `last_image`. 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-2.5-image-to-video.
Reported generation time on WaveSpeedAI is around 193 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.
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.