Kling Omni Video O1 Reference-to-Video generates creative videos using character, prop, or scene references from multiple viewpoints. Extracts subject features and creates new video content while maintaining identity consistency across frames. Ready-to-use REST API, best performance, no cold starts, affordable pricing.
Boşta
$0.56çalıştırma başına·~17 / $10
Use reference image 1 as the female character and reference image 2 as the male character. Blend their appearances into the same semi-realistic anime / 3D animated style so they look like they belong in one world, keeping their facial features clearly recognizable. Create a cozy 10-second Christmas scene in a warm living room at night: a large decorated Christmas tree with glowing fairy lights and red ornaments stands in the background, soft yellow light from the tree and a fireplace. The two characters are sitting together on a rug in front of the tree, facing each other slightly, holding mugs of hot chocolate, chatting and laughing gently as if sharing Christmas stories. The camera starts with a medium two-shot of both of them, then slowly dollies in and slightly arcs around them, with shallow depth of field and bokeh from the Christmas lights. Atmosphere: warm, festive, romantic, soft film look, subtle lens glow from the lights, no text on screen.
The robot is dancing with the teddy bear
The girl in Picture 1 is skateboarding in the environment of Picture 2
A women in ornate dresses, wearing a necklace and a handbag, is walking on the street.
The banana cat plays games by the Christmas tree.
Kling Omni Video O1 is Kuaishou's groundbreaking unified multi-modal video model. The Reference-to-Video mode creates new video content based on subject references — maintaining character, prop, and scene identity while generating entirely new creative scenarios.
Build subjects from multiple reference viewpoints:
Advanced feature extraction ensures:
Generate entirely new content while preserving identity:
Upload Reference Images Provide one or more images of your subject (character, object, or scene).
Describe the Scenario Write a prompt for the new video content.
Example: "The character walking through a futuristic city at night, neon lights reflecting on wet streets"
Set Parameters Choose duration, resolution, and output format.
Generate Receive video with your subject in the new scenario.
| Reference Type | Price per Second |
|---|---|
| Image Reference | $0.112 |
| Video Reference | $0.168 |
$0.112/s for image reference only; $0.168/s when using video reference.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/kwaivgi/kling-video-o1/reference-to-video with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Kling Video O1 Reference To Video below.
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/kwaivgi/kling-video-o1/reference-to-video" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d '{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"video": "https://example.com/your-input.mp4",
"keep_original_sound": true,
"aspect_ratio": "16:9",
"duration": 5
}'
# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
-H "Authorization: Bearer $WAVESPEED_API_KEY"
# When status is "completed", read the output from data.outputs[0].// npm install wavespeed
const WaveSpeed = require('wavespeed');
const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env
const result = await client.run("kwaivgi/kling-video-o1/reference-to-video", {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"video": "https://example.com/your-input.mp4",
"keep_original_sound": true,
"aspect_ratio": "16:9",
"duration": 5
});
console.log(result.outputs[0]); // → URL of the generated output# pip install wavespeed
import wavespeed
output = wavespeed.run(
"kwaivgi/kling-video-o1/reference-to-video",
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"video": "https://example.com/your-input.mp4",
"keep_original_sound": true,
"aspect_ratio": "16:9",
"duration": 5
}
)
print(output["outputs"][0]) # → URL of the generated outputKling Video O1 Reference To Video is a Kuaishou model for video generation from images, exposed as a REST API on WaveSpeedAI. Kling Omni Video O1 Reference-to-Video generates creative videos using character, prop, or scene references from multiple viewpoints. Extracts subject features and creates new video content while maintaining identity consistency across frames. Ready-to-use REST API, best performance, no cold starts, 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 prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/kwaivgi/kwaivgi-kling-video-o1-reference-to-video.
Kling Video O1 Reference To Video starts at $0.56 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`, `images`, `video`, `aspect_ratio`, `duration`, `keep_original_sound`. 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/kwaivgi/kwaivgi-kling-video-o1-reference-to-video.
Average end-to-end generation time on WaveSpeedAI is around 261 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 (Kuaishou). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.