OpenVideo Image to Video LoRA is a fast AI image-to-video generation model that creates short cinematic clips with native audio from a single reference image, with optional preset control and per-LoRA strength settings for style, motion, and look-and-feel. Supports 480p, 720p, and 1080p output and 3–20 second duration tiers. Ready-to-use REST inference API for cinematic clips, character-consistent videos, stylized motion, product videos, social media content, advertising creatives, and professional LoRA-based image-to-video workflows with simple integration, no coldstarts, and affordable pricing.
निष्क्रिय
$0.15प्रति रन·~66 / $10
The traveler looks down at the map, then raises his head toward the distant horizon. Wind blows sand across the frame, his jacket moves naturally, and the camera slowly circles around him with an epic cinematic feeling.
OpenVideo — Unlimited Image-to-Video (LoRA tier) uses the same audio-video generation pipeline as the base image-to-video endpoint, while adding a curated LoRA stack for stronger style, motion, and visual control. You can use the recommended preset or override individual LoRA strengths per request.
Same base image-to-video workflow
Uses the same core audio-video pipeline as the base image-to-video endpoint.
Preset-based LoRA control
Choose between original for lighter styling or tuned for the recommended LoRA stack.
Per-LoRA override support Override individual LoRA strengths on top of the selected preset for more flexible control.
Style, motion, and look control Fine-tune visual style, motion behavior, and overall output feel with a curated LoRA set.
Unlimited creative flexibility Designed for users who want more direct control over the generation result.
This endpoint supports all inputs from the base image-to-video endpoint, plus the following extra fields:
| Parameter | Required | Description |
|---|---|---|
preset | No | LoRA preset. original applies lighter styling. tuned applies the recommended LoRA stack. Default: tuned. |
loras | No | Per-LoRA strength overrides merged on top of the selected preset. Example: {"omninft": 0.5, "better_motion": 0.3}. Unknown keys are ignored. Default: {}. |
sulphur, sulphur_v1, vbvr, dreamly, synth, plora, singularity, omninft, omninft_bf16, better_motion, physics_v2, hardcut, transition
image-to-video endpoint.tuned for the recommended LoRA stack or original for a lighter effect.loras object to adjust individual LoRA strengths.| Resolution | Per 5s | Per second | Max length |
|---|---|---|---|
| 480p | $0.15 | $0.03 / s | 20 s |
| 720p | $0.25 | $0.05 / s | 20 s |
| 1080p | $0.35 | $0.07 / s | 20 s |
720p costs 5/3× the 480p price.1080p costs 7/3× the 480p price.max(5, min(duration, 20)).better_motion or physics_v2 to guide output behavior.preset = tuned for the recommended default behavior.preset = original when you want results closer to the base input style.loras object to override only the LoRAs you want to adjust.0 if you want to disable that LoRA while keeping the rest of the preset unchanged.image-to-video inputs and adds preset and loras.preset is tuned.loras value is {}.loras: {"omninft": 0} to fully disable that LoRA from the selected preset without changing the rest.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/open-video/image-to-video-lora with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Open Video Image To Video Lora below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"preset": "tuned",
"resolution": "480p",
"duration": 5,
"seed": -1
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/open-video/image-to-video-lora" \
-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/open-video/image-to-video-lora";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"preset": "tuned",
"resolution": "480p",
"duration": 5,
"seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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 = {
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"preset": "tuned",
"resolution": "480p",
"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/open-video/image-to-video-lora", 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)Open Video Image To Video Lora is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. OpenVideo Image to Video LoRA is a fast AI image-to-video generation model that creates short cinematic clips with native audio from a single reference image, with optional preset control and per-LoRA strength settings for style, motion, and look-and-feel. Supports 480p, 720p, and 1080p output and 3–20 second duration tiers. Ready-to-use REST inference API for cinematic clips, character-consistent videos, stylized motion, product videos, social media content, advertising creatives, and professional LoRA-based image-to-video workflows with simple integration, no coldstarts, and 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/open-video-image-to-video-lora.
Open Video Image To Video Lora starts at $0.15 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`, `seed`, `loras`. 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/open-video-image-to-video-lora.
Median end-to-end generation time on WaveSpeedAI is around 54 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.