Text to Image
Create AI images from text prompts
Kuaishou/kling-video-o3-std-reference-to-video
kling-video-o3-std-reference-to-video
Reference video generation by Kling O3 Std API with strong consistency. Stable performance and affordable pricing for identity-preserving video workflows. Stable and affordable for professional video production workflows.
Reference to Video
Related Kling Video O3 Std Models
API Examples
Submit Example
const response = await fetch('https://api.flaq.ai/api/v1/video/task', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': 'Bearer YOUR_API_KEY'
},
body: JSON.stringify({
model_name: 'kling-video-o3-std-reference-to-video',
prompt: 'Match motion to reference subjects, natural pacing',
video_url: 'https://example.com/source.mp4',
images: ['https://example.com/ref1.jpg', 'https://example.com/ref2.jpg'],
duration: 5
})
});
const { data } = await response.json();
const taskId = data.task_id;
// Use the @ (AT) reference feature in prompt through <<<...>>> placeholders.
// Placeholder numbering is 1-based for each media array:
// <<<image_1>>> = images[0], <<<image_2>>> = images[1], <<<video_1>>> = videos[0]
const mediaReferenceResponse = await fetch('https://api.flaq.ai/api/v1/video/task', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': 'Bearer YOUR_API_KEY'
},
body: JSON.stringify({
model_name: 'kling-video-o3-std-reference-to-video',
prompt: 'Make the athlete from <<<image_1>>> follow the motion in <<<video_1>>> while preserving the uniform details from <<<image_2>>>',
images: [
'https://example.com/athlete-reference.jpg',
'https://example.com/uniform-reference.jpg'
],
videos: ['https://example.com/motion-reference.mp4'],
aspect_ratio: '16:9',
duration: 5,
sound: false
})
});
const { data: mediaReferenceData } = await mediaReferenceResponse.json();
const mediaReferenceTaskId = mediaReferenceData.task_id;
Polling Example
// Step 2: Poll for results
const taskId = data.task_id;
const pollResult = async (taskId) => {
const res = await fetch(`https://api.flaq.ai/api/v1/video/${taskId}`, {
headers: { 'Authorization': 'Bearer YOUR_API_KEY' }
});
return res.json();
};
while (true) {
const pollResultData = await pollResult(taskId);
const status = pollResultData.data.task_status;
if (status === 'succeed') {
console.log(pollResultData.data.task_result.videos[0].url);
break;
}
if (status === 'failed') {
console.error(pollResultData.data.task_status_msg);
break;
}
await new Promise(resolve => setTimeout(resolve, 10000));
}
Submit Example
import requests
response = requests.post(
'https://api.flaq.ai/api/v1/video/task',
headers={
'Content-Type': 'application/json',
'Authorization': 'Bearer YOUR_API_KEY'
},
json={
'model_name': 'kling-video-o3-std-reference-to-video',
'prompt': 'Match motion to reference subjects, natural pacing',
'video_url': 'https://example.com/source.mp4',
'images': ['https://example.com/ref1.jpg', 'https://example.com/ref2.jpg'],
'duration': 5
}
)
task_id = response.json()['data']['task_id']
# Use the @ (AT) reference feature in prompt through <<<...>>> placeholders.
# Placeholder numbering is 1-based for each media array:
# <<<image_1>>> = images[0], <<<image_2>>> = images[1], <<<video_1>>> = videos[0]
media_reference_response = requests.post(
'https://api.flaq.ai/api/v1/video/task',
headers={
'Content-Type': 'application/json',
'Authorization': 'Bearer YOUR_API_KEY'
},
json={
'model_name': 'kling-video-o3-std-reference-to-video',
'prompt': 'Make the athlete from <<<image_1>>> follow the motion in <<<video_1>>> while preserving the uniform details from <<<image_2>>>',
'images': [
'https://example.com/athlete-reference.jpg',
'https://example.com/uniform-reference.jpg'
],
'videos': ['https://example.com/motion-reference.mp4'],
'aspect_ratio': '16:9',
'duration': 5,
'sound': False
}
)
media_reference_task_id = media_reference_response.json()['data']['task_id']
Polling Example
# Step 2: Poll for results
task_id = response.json()['data']['task_id']
poll_url = f"https://api.flaq.ai/api/v1/video/{task_id}"
while True:
poll_result = requests.get(poll_url, headers={'Authorization': 'Bearer YOUR_API_KEY'}).json()
status = poll_result['data']['task_status']
if status == 'succeed':
print(poll_result['data']['task_result']['videos'][0]['url'])
break
if status == 'failed':
print(poll_result['data']['task_status_msg'])
break
time.sleep(10)
Submit Example
curl -X POST https://api.flaq.ai/api/v1/video/task \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model_name": "kling-video-o3-std-reference-to-video",
"prompt": "Match motion to reference subjects, natural pacing",
"video_url": "https://example.com/source.mp4",
"images": ["https://example.com/ref1.jpg", "https://example.com/ref2.jpg"],
"duration": 5
}'
# Use the @ (AT) reference feature in prompt through <<<...>>> placeholders.
# Placeholder numbering is 1-based for each media array:
# <<<image_1>>> = images[0], <<<image_2>>> = images[1], <<<video_1>>> = videos[0]
curl -X POST https://api.flaq.ai/api/v1/video/task \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model_name": "kling-video-o3-std-reference-to-video",
"prompt": "Make the athlete from <<<image_1>>> follow the motion in <<<video_1>>> while preserving the uniform details from <<<image_2>>>",
"images": [
"https://example.com/athlete-reference.jpg",
"https://example.com/uniform-reference.jpg"
],
"videos": ["https://example.com/motion-reference.mp4"],
"aspect_ratio": "16:9",
"duration": 5,
"sound": false
}'
Polling Example
# Step 2: Poll for results
# Replace {task_id} with the task_id returned from the submit response
curl -X GET "https://api.flaq.ai/api/v1/video/{task_id}" \
-H "Authorization: Bearer YOUR_API_KEY"
Try the AI Video Generator now
Kling O3 Std Reference to Video Pricing
| Parameters | Price | Original Price | Discount |
|---|
Related Models
kling-v3.0-std-text-to-videotext-to-videokling-v3.0-std-image-to-videoimage-to-videokling-v3.0-pro-text-to-videotext-to-videokling-v3.0-pro-image-to-videoimage-to-videokling-3.0-turbo-text-to-videotext-to-videokling-3.0-turbo-image-to-videoimage-to-videokling-video-o3-std-text-to-videotext-to-videokling-video-o3-std-image-to-videoimage-to-videokling-video-o3-std-reference-to-videoreference-to-videokling-video-o3-std-video-editvideo-editkling-video-o3-pro-text-to-videotext-to-videokling-video-o3-pro-image-to-videoimage-to-videokling-video-o3-pro-reference-to-videoreference-to-videokling-video-o3-pro-video-editvideo-editkling-4-0-text-to-videotext-to-videokling-4-0-image-to-videoimage-to-videoREADME
Fast & Affordable Kling Video O3 Standard Reference-to-Video API (Kuaishou's MVL-Powered Reference Video Generation)
Kuaishou Kling Video O3 Standard Reference-to-Video API delivers cost-effective, production-grade AI video generation for developers and creative teams. This MVL-powered reference-to-video API integration enables you to generate professional video clips guided by reference videos across 3–15 second durations with optional audio effects. Built on Kuaishou's Multimodal Visual Language (MVL) architecture, the Kling Video O3 Standard model uses reference video inputs to guide motion style, camera behavior, and scene evolution for scalable production workflows on Flaq AI.
Key Features of Kling Video O3 Standard Reference-to-Video API
- Reference Video-Guided Generation: Generate new video clips guided by reference video inputs using Kuaishou's MVL technology—enabling precise control over motion style, camera behavior, and scene dynamics for consistent, directed output.
- MVL-Powered Scene Reasoning: The Kling Video O3 Standard model reasons about scene composition, spatial relationships, and motion patterns from reference videos in a unified pass for coherent, style-consistent output.
- Flexible Duration Control: Generate reference-guided videos from 3 to 15 seconds with precise duration settings, providing full control over clip length for diverse content requirements.
- Optional Audio Effects Integration: Add synchronized sound effects to generated videos for immersive output directly through the API—no post-production audio work required.
- Cost-Effective API Pricing: Uses per-second billing with strong cost-efficiency for reference-guided production workflows.
- Flexible Aspect Ratio Support: API supports 16:9 landscape, 9:16 vertical, and 1:1 square formats, optimized for social media content and digital marketing campaigns.
- Context-Aware Motion Transfer: Transfer motion patterns, camera movements, and scene dynamics from reference videos while generating new visual content through the MVL architecture.
How to Use Kling Video O3 Standard Reference-to-Video API for Video Generation on Flaq AI
- Input: Reference video(s) plus natural-language prompts (describing desired scene content, modifications, and generation direction)
- Output: Reference-guided videos (MP4 format) delivered via secure CDN URLs through Kling Video O3 Standard Reference-to-Video API integration
- Duration: 3 to 15 seconds, configurable per request
- Aspect Ratios: 3 supported formats - 16:9 (landscape), 9:16 (vertical/portrait), and 1:1 (square)
- Audio: Optional audio effects integration
- Capabilities: MVL-powered reference-guided video generation, motion style transfer, cinematic camera movement replication, and optional audio integration through Kling Video O3 Standard API.
Best Use Cases for Kling Video O3 Standard Reference-to-Video API Integration
- Consistent Brand Video Production: Generate new video content that maintains consistent motion style, camera behavior, and visual dynamics from reference brand videos through cost-effective API integration.
- Social Media Content Series: Produce platform-optimized video series with consistent motion patterns and optional audio for Instagram Reels, TikTok, and YouTube Shorts at scale.
- Creative Direction Control: Guide video generation with reference videos to achieve specific cinematographic styles, motion patterns, and scene dynamics for precise creative control.
- Rapid Variation Generation: Quickly generate multiple video variations guided by a single reference video, enabling efficient A/B testing and content diversification workflows.
- Automated Content Pipelines: Power automated reference-guided video generation workflows with flexible duration controls and optional audio effects for high-throughput production systems.
Note Please ensure your prompts comply with Kuaishou's content safety guidelines. If an error occurs, review your prompt for restricted content, adjust it, and try again.
Kling Video O3 Standard Reference-to-Video vs Competitors: Comparative Analysis
-
Kling Video O3 Standard vs. Kling Video O3 Pro Reference-to-Video Kling Video O3 Pro delivers enhanced motion fidelity and premium rendering quality at a higher price tier. Kling Video O3 Standard provides strong MVL-powered reference-guided generation with better cost-efficiency, making it well suited to high-volume workflows.
-
Kling Video O3 Standard vs. Kling Video O3 Standard Image-to-Video Kling Video O3 Standard Image-to-Video animates static images into video clips. Kling Video O3 Standard Reference-to-Video uses existing video clips as motion and style guides—ideal for applications requiring precise control over motion patterns and camera behavior from reference footage.
-
Kling Video O3 Standard vs. Runway Gen-3 Reference Generation Runway Gen-3 offers strong creative controls and artistic flexibility. Kling Video O3 Standard Reference-to-Video API differentiates through flexible 3–15 second duration control, optional integrated audio effects, and MVL-powered motion reasoning—making it accessible for budget-conscious developers.
-
Kling Video O3 Standard vs. Pika Reference-to-Video Pika excels at stylized animations and user-friendly interface. Kling Video O3 Standard provides programmatic API access, extended duration up to 15 seconds, optional audio effects, and strong cost-efficiency—ideal for developers building scalable reference-guided video pipelines.
-
Kling Video O3 Standard vs. Vidu Q3 (Vidu) Vidu Q3 excels at Smart Cuts multi-shot storytelling and flexible sound controls. Kling Video O3 Standard Reference-to-Video API differentiates through reference video-guided generation, MVL-powered motion transfer, and stable per-second billing—making it a strong choice for applications requiring precise motion style control.
Use the Kling O3 Std Reference to Video Model Directly in Powerful AI Tools
Explore several AI creation tools for quick image and video workflows in your browser, then scale successful ideas with Flaq AI's production-ready model APIs. Flaq AI provides a unified API layer for all models, making it easy to use and scale your workflows.
