daVinci MagiHuman Image-to-Video API — a 15B parameter omni video generation model, the new open-source king on par with WAN 2.5. Generates high-quality AI videos from reference images with optional audio input. Supports digital humans, talking heads, and general video generation. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
$0.1per run·~10 / $1
A man sits at a modern, minimalist podcast studio desk, calmly recording a show. He gestures gently with his right hand while speaking, saying, “Welcome to my show — truly, thank you so much for joining me on WaveSpeed.” The camera captures a medium shot from eye level, focusing on his face and upper body, with a shallow depth of field blurring the background slightly. Natural, warm tones and cinematic lighting style.
DaVinci MagiHuman Image-to-Video animates a reference image into a cinematic, human-centered video clip. Upload a photo, describe the motion and scene, and optionally provide an audio track to synchronize — the model generates natural human movement, expressive storytelling, and a cinematic atmosphere across multiple resolution and duration options.
Image-grounded generation Start from a reference photo for precise visual control over character appearance, environment, and composition.
Human-focused motion Optimized for realistic human motion, expressions, and interactions — ideal for portrait-style and character-driven video content.
Audio input support Upload an audio track to guide the rhythm, mood, and pacing of the generated video for synchronized results.
Multiple resolution tiers Generate at 256p, 720p, or 1080p to match your delivery requirements and budget.
Flexible aspect ratio Supports 16:9 landscape and 9:16 portrait orientations for both cinematic and social media formats.
Adjustable duration Generate clips from 5 to 10 seconds with per-second granularity.
Reproducible results Use the seed parameter to lock in a specific output for exact reproduction.
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Reference image to animate (URL or file upload). |
| prompt | Yes | Text description of the motion, camera style, and scene atmosphere. |
| audio | No | Optional audio track to synchronize with the generated video. |
| aspect_ratio | No | Output aspect ratio: 16:9 (default) or 9:16. |
| resolution | No | Output resolution: 256p, 720p (default), or 1080p. |
| duration | No | Clip length in seconds. Options: 5, 6, 7, 8, 9, 10. Default: 5. |
| seed | No | Random seed for reproducible results. Use -1 for a random seed. |
| Duration | 256p | 720p | 1080p |
|---|---|---|---|
| 5s | $0.10 | $0.20 | $0.30 |
| 6s | $0.12 | $0.24 | $0.36 |
| 7s | $0.14 | $0.28 | $0.42 |
| 8s | $0.16 | $0.32 | $0.48 |
| 9s | $0.18 | $0.36 | $0.54 |
| 10s | $0.20 | $0.40 | $0.60 |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/davinci-magihuman/image-to-video with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Davinci Magihuman Image To Video below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"aspect_ratio": "16:9",
"resolution": "720p",
"duration": 5,
"seed": -1
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/davinci-magihuman/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=$(printf '%s' "$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/wavespeed-ai/davinci-magihuman/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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"aspect_ratio": "16:9",
"resolution": "720p",
"duration": 5,
"seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"aspect_ratio": "16:9",
"resolution": "720p",
"duration": 5,
"seed": -1
}
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/wavespeed-ai/davinci-magihuman/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 = 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)Davinci Magihuman Image To Video is a WaveSpeedAI model for video generation from images, exposed as a REST API on WaveSpeedAI. daVinci MagiHuman Image-to-Video API — a 15B parameter omni video generation model, the new open-source king on par with WAN 2.5. Generates high-quality AI videos from reference images with optional audio input. Supports digital humans, talking heads, and general video generation. 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/wavespeed-ai/davinci-magihuman-image-to-video.
Davinci Magihuman Image To Video starts at $0.10 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`, `audio`, `aspect_ratio`, `resolution`, `duration`. 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/wavespeed-ai/davinci-magihuman-image-to-video.
Median end-to-end generation time on WaveSpeedAI is around 219 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 (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.