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Alibaba/happyhorse-1.1-reference-to-video
happyhorse-1.1-reference-to-video
Create stunning reference-guided videos with Happy Horse 1.1 API for reference-to-video generation. Stable and affordable for professional video creation workflows.
Reference to Video
Related Happyhorse 1.1 Models
API Examples
Submit Example
// Step 1: Submit generation request
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: 'happyhorse-1.1-reference-to-video',
prompt: 'Subject moves naturally while preserving reference style and appearance',
duration: 5,
resolution: '1080p',
aspect_ratio: '16:9',
images: [
'https://example.com/ref-1.jpg',
'https://example.com/ref-2.jpg'
],
seed: 42
})
});
const { data } = await response.json();
const taskId = data.task_id;
// Use the @ (AT) reference feature in prompt through <<<...>>> placeholders.
// Placeholder numbering is 1-based and follows the images array order:
// <<<image_1>>> = images[0], <<<image_2>>> = images[1], <<<image_3>>> = images[2]
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: 'happyhorse-1.1-reference-to-video',
prompt: 'Show the person from <<<image_1>>> walking beside the bicycle from <<<image_2>>> through the street in <<<image_3>>>, preserving all reference details',
duration: 5,
resolution: '1080p',
aspect_ratio: '16:9',
images: [
'https://example.com/person-reference.jpg',
'https://example.com/bicycle-reference.jpg',
'https://example.com/street-reference.jpg'
],
seed: 42
})
});
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
# Step 1: Submit generation request
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': 'happyhorse-1.1-reference-to-video',
'prompt': 'Subject moves naturally while preserving reference style and appearance',
'duration': 5,
'resolution': '1080p',
'aspect_ratio': '16:9',
'images': [
'https://example.com/ref-1.jpg',
'https://example.com/ref-2.jpg'
],
'seed': 42
}
)
result = response.json()
task_id = result['data']['task_id']
# Use the @ (AT) reference feature in prompt through <<<...>>> placeholders.
# Placeholder numbering is 1-based and follows the images array order:
# <<<image_1>>> = images[0], <<<image_2>>> = images[1], <<<image_3>>> = images[2]
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': 'happyhorse-1.1-reference-to-video',
'prompt': 'Show the person from <<<image_1>>> walking beside the bicycle from <<<image_2>>> through the street in <<<image_3>>>, preserving all reference details',
'duration': 5,
'resolution': '1080p',
'aspect_ratio': '16:9',
'images': [
'https://example.com/person-reference.jpg',
'https://example.com/bicycle-reference.jpg',
'https://example.com/street-reference.jpg'
],
'seed': 42
}
)
media_reference_result = media_reference_response.json()
media_reference_task_id = media_reference_result['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
# Step 1: Submit generation request
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": "happyhorse-1.1-reference-to-video",
"prompt": "Subject moves naturally while preserving reference style and appearance",
"duration": 5,
"resolution": "1080p",
"aspect_ratio": "16:9",
"images": ["https://example.com/ref-1.jpg", "https://example.com/ref-2.jpg"],
"seed": 42
}'
# Use the @ (AT) reference feature in prompt through <<<...>>> placeholders.
# Placeholder numbering is 1-based and follows the images array order:
# <<<image_1>>> = images[0], <<<image_2>>> = images[1], <<<image_3>>> = images[2]
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": "happyhorse-1.1-reference-to-video",
"prompt": "Show the person from <<<image_1>>> walking beside the bicycle from <<<image_2>>> through the street in <<<image_3>>>, preserving all reference details",
"duration": 5,
"resolution": "1080p",
"aspect_ratio": "16:9",
"images": [
"https://example.com/person-reference.jpg",
"https://example.com/bicycle-reference.jpg",
"https://example.com/street-reference.jpg"
],
"seed": 42
}'
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
Happy Horse 1.1 Reference to Video Pricing
| Parameters | Price | Original Price | Discount |
|---|
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Professional Happy Horse 1.1 Reference-to-Video API (Alibaba's Consistent Reference Video Generation)
Happy Horse 1.1 Reference-to-Video API delivers upgraded Alibaba video generation for teams that need consistent characters, products, scenes, and brand assets across generated clips. This professional reference-to-video API integration uses reference images as visual anchors, then creates polished video outputs with stronger subject consistency, improved prompt following, and richer visual texture compared with Happy Horse 1.0. Built for scalable creative production on Flaq AI, Happy Horse 1.1 Reference-to-Video is well suited for advertising, short drama, e-commerce, character workflows, and multi-asset content pipelines.
Key Features of Happy Horse 1.1 Reference-to-Video API
- Reference-Guided Video Generation: Generate videos from visual references while preserving key identity details, product attributes, character traits, and scene style.
- Upgraded from Happy Horse 1.0: Happy Horse 1.1 improves dynamic expression, subject consistency, instruction following, and visual quality for more reliable reference-based output.
- Multi-Reference Understanding: Use multiple visual inputs to guide complex creative needs such as consistent characters, product details, scene combinations, and brand assets.
- Improved Subject Consistency: Maintain stable identity and visual continuity across motion, camera changes, and prompt-guided scene evolution.
- Flexible Creative Direction: Guide camera movement, action, atmosphere, scene pacing, and narrative intent through natural-language prompts.
- Production-Ready API Integration: Build repeatable reference-to-video workflows for commerce tools, creative platforms, media pipelines, and marketing automation on Flaq AI.
How to Use Happy Horse 1.1 Reference-to-Video API for Consistent Video Generation on Flaq AI
- Input: At least one reference image plus natural-language prompts describing the desired scene, action, camera movement, and visual style.
- Output: Reference-guided video clips delivered through Happy Horse 1.1 Reference-to-Video API integration.
- Aspect Ratios: Supports multiple aspect ratios for social media, ads, short drama production, product videos, and brand content.
- Duration: Flexible short-form video generation for production previews, campaign assets, character clips, and automated creative workflows.
- Capabilities: Reference-to-video generation, multi-reference visual grounding, consistent character and product rendering, prompt-guided motion, and scalable Alibaba video API integration.
Best Use Cases for Happy Horse 1.1 Reference-to-Video API Integration
- Consistent Character Video Creation: Generate character-driven clips that preserve appearance, styling, and identity across different scenes and prompts.
- E-commerce Product Videos: Use product references to create dynamic demos, lifestyle clips, and campaign videos while keeping important product details recognizable.
- Brand & Advertising Workflows: Create reference-aligned videos for campaigns, brand assets, spokesperson content, and multi-scene creative concepts.
- Short Drama & Story Production: Combine reference visuals with narrative prompts to generate dialogue-style scenes, character moments, and story previews.
- Automated Creative Pipelines: Power scalable reference-based video generation in SaaS tools, media platforms, agency systems, and commerce applications.
Note Please ensure your prompts and reference images comply with Alibaba's safety guidelines. If an error occurs, review your input for restricted content, adjust it, and try again.
Happy Horse 1.1 Reference-to-Video vs Competitors: Comparative Analysis
-
Happy Horse 1.1 Reference-to-Video vs. Happy Horse 1.0
Happy Horse 1.0 supports practical text and image video generation. Happy Horse 1.1 Reference-to-Video advances the workflow with stronger reference grounding, improved subject consistency, better prompt following, and richer visual texture. -
Happy Horse 1.1 Reference-to-Video vs. Seedance 2.0 Reference-to-Video
Seedance 2.0 Reference-to-Video provides ByteDance reference-guided video creation. Happy Horse 1.1 Reference-to-Video emphasizes Alibaba's upgraded multi-reference understanding and subject consistency for commerce, character, and campaign workflows. -
Happy Horse 1.1 Reference-to-Video vs. Kling Reference-to-Video
Kling reference workflows are strong for expressive character motion. Happy Horse 1.1 focuses on consistent visual grounding, improved prompt adherence, and production-ready API integration for teams that need repeatable outputs. -
Happy Horse 1.1 Reference-to-Video vs. Runway Video Tools
Runway provides broad creator-facing video controls. Happy Horse 1.1 Reference-to-Video API is better suited for programmatic generation, multi-reference creative automation, and scalable production inside applications. -
Happy Horse 1.1 Reference-to-Video vs. Pika
Pika offers accessible video generation for creators. Happy Horse 1.1 Reference-to-Video provides a more production-oriented API path for consistent reference-guided clips, brand-safe visual reuse, and automated creative systems.
Explore multiple Creative Tools with Flaq AI
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.
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