Kling Omni Video O1 Image-to-Video
kwaivgi/kling-video-o1/image-to-video
Kling Omni Video O1 Image-to-Video transforms static images into dynamic cinematic videos using MVL (Multi-modal Visual Language) technology. Maintains subject consistency while adding natural motion, physics simulation, and seamless scene dynamics. Ready-to-use REST API, best performance, no coldstarts, affordable pricing.
Examples
Parameters
| Name | Type | Default | Constraints | Description |
|---|---|---|---|---|
| image *Image | image_upload | — | 0–1 items | The first frame image URL. |
| last_imageEnd Image | image_upload | — | 0–1 items | The last frame image URL. |
| promptPrompt | textarea | — | ≤ 5000 chars | The positive prompt for the generation. |
| resolutionGeneration Mode | select | 1080P | 720P | 1080P | |
| durationDuration | select | 5 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | The duration of the generated media in seconds (3-10). |
| soundSound | boolean | false | — | Whether to generate audio for the video. |
| multi_shotMulti Shot | boolean | — | — | Whether to generate multi-shot video When true: the prompt parameter is invalid. When false: the shot_type and multi_prompt parameters are invalid |
| shot_typeShot Type | select | — | customize | intelligence | Shot type for the generation. |
| multi_promptMulti Prompt | array<object> | — | — | List of multi-prompt elements for the generation. |
| ↳durationDuration | number | 5 | — | The duration of this shot in seconds. |
| ↳promptPrompt | text | — | — | The prompt for this shot. |
API
Call this model through one unified REST API. Get a key on the API Keys page.
cURL
# 1) Submit — returns { "task_uuid": "..." }
curl -X POST "https://www.namifusion.com/api/v1/marketplace/run/kwaivgi/kling-video-o1/image-to-video" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"input": {
"image": [
"https://assets-public.namifusion.com/uploads/images/2026-04-14/5895345cd939.png"
],
"last_image": [
"https://assets-public.namifusion.com/uploads/images/2026-04-14/c98e7d7d7c2e.png"
],
"prompt": "Generate a dynamic video of a tranquil sunset scene transitioning into a starry night over the mountains, with realistic lighting and smooth motions.",
"resolution": "1080P",
"duration": 5,
"sound": false,
"shot_type": "intelligence"
}
}'
# 2) Poll until status is "completed", then read the output URLs
curl "https://www.namifusion.com/api/v1/marketplace/run/tasks/TASK_UUID" \
-H "Authorization: Bearer YOUR_API_KEY"Python
import time, requests
API_KEY = "YOUR_API_KEY"
HEADERS = {"Authorization": f"Bearer {API_KEY}"}
# 1) Submit
resp = requests.post(
"https://www.namifusion.com/api/v1/marketplace/run/kwaivgi/kling-video-o1/image-to-video",
headers=HEADERS,
json={
"input": {
"image": [
"https://assets-public.namifusion.com/uploads/images/2026-04-14/5895345cd939.png"
],
"last_image": [
"https://assets-public.namifusion.com/uploads/images/2026-04-14/c98e7d7d7c2e.png"
],
"prompt": "Generate a dynamic video of a tranquil sunset scene transitioning into a starry night over the mountains, with realistic lighting and smooth motions.",
"resolution": "1080P",
"duration": 5,
"sound": False,
"shot_type": "intelligence"
}
},
)
resp.raise_for_status() # 401/402/429/5xx stop here instead of polling a bad task
task = resp.json()
# 2) Poll until a terminal state (completed / failed / cancelled).
# This model is allowed up to 600s server-side.
deadline = time.time() + 660
while task.get("status") not in ("completed", "failed", "cancelled"):
if time.time() > deadline:
raise TimeoutError(f"still {task.get('status')} — keep the task_uuid and poll later")
time.sleep(3)
poll = requests.get(f"https://www.namifusion.com/api/v1/marketplace/run/tasks/{task['task_uuid']}", headers=HEADERS)
poll.raise_for_status()
task = poll.json()
print(task["status"], task.get("output"))JavaScript
const API_KEY = "YOUR_API_KEY";
const HEADERS = { Authorization: `Bearer ${API_KEY}` };
// 1) Submit
const resp = await fetch("https://www.namifusion.com/api/v1/marketplace/run/kwaivgi/kling-video-o1/image-to-video", {
method: "POST",
headers: { ...HEADERS, "Content-Type": "application/json" },
body: JSON.stringify({
"input": {
"image": [
"https://assets-public.namifusion.com/uploads/images/2026-04-14/5895345cd939.png"
],
"last_image": [
"https://assets-public.namifusion.com/uploads/images/2026-04-14/c98e7d7d7c2e.png"
],
"prompt": "Generate a dynamic video of a tranquil sunset scene transitioning into a starry night over the mountains, with realistic lighting and smooth motions.",
"resolution": "1080P",
"duration": 5,
"sound": false,
"shot_type": "intelligence"
}
}),
});
if (!resp.ok) throw new Error(`submit failed: ${resp.status} ${await resp.text()}`);
let task = await resp.json();
// 2) Poll until a terminal state (completed / failed / cancelled).
// This model is allowed up to 600s server-side.
const deadline = Date.now() + 660 * 1000;
while (!["completed", "failed", "cancelled"].includes(task.status)) {
if (Date.now() > deadline) throw new Error(`still ${task.status} — keep the task_uuid and poll later`);
await new Promise((r) => setTimeout(r, 3000));
const poll = await fetch(`https://www.namifusion.com/api/v1/marketplace/run/tasks/${task.task_uuid}`, { headers: HEADERS });
if (!poll.ok) throw new Error(`poll failed: ${poll.status}`);
task = await poll.json();
}
console.log(task.status, task.output);Documentation
Kling Omni Video O1 Image-To-Video
Transform static images into dynamic cinematic videos with advanced MVL technology
Kling Omni Video O1 Image-to-Video is a cutting-edge model designed to convert static images into dynamic, physics-aware cinematic videos. Powered by Multi-modal Visual Language (MVL) technology, this model ensures subject consistency while introducing natural motion, physics simulation, and seamless scene dynamics. With no cold starts and affordable pricing, it is optimized for developers seeking high-quality video generation.
🚀 Key Features
- Intelligent Image Animation: Adds natural, physics-based motion to static subjects while preserving original image details and composition.
- Subject Consistency: Ensures stable character identity, consistent props, and maintained color tones across all frames.
- Multi-modal Understanding: Combines input images with text prompts to control motion direction, camera angles, and scene atmosphere.
- Physics Simulation: Generates believable movement patterns for realistic video dynamics.
- Prompt Control: Allows users to guide animation with detailed text descriptions for precise results.
🛠️ Technical Specifications
| Parameter | Type | Default | Range | Description |
|---|---|---|---|---|
| image | string | - | - | First frame of the video (required). |
| prompt | string | - | - | Positive prompt for generation (required). |
| last_image | string | - | - | Last frame of the video (optional). |
| duration | integer | 5 | 3, 4, 5, 6, 7, 8, 9, 10 | Duration of the generated video. Only 5s or 10s supported without last_image. |
💰 Pricing
| Attribute | 720P | 1080P |
|---|---|---|
| No Reference Video | 0.0840 | 0.1120 |
| With Reference Video | 0.1260 | 0.1680 |
Pricing is billed per second of output video duration.
💡 Best Use Cases
- E-commerce: Create engaging product videos from static images to enhance online listings.
- Social Media: Generate dynamic content for platforms like Instagram, TikTok, and Facebook.
- Film Production: Prototype cinematic scenes with natural motion and physics-based dynamics.
- Advertising: Produce high-quality animated visuals for marketing campaigns.
🔗 Related Models
- Kwaivgi Kling V3.0 Text-To-Video: Text-based video generation model.
- Kwaivgi Kling Video O1 Reference-To-Video: Reference-based video generation for advanced use cases.
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