
AI 文本转图像生成器
使用领先 AI 图像模型、灵活设置和快速浏览器工作流,从提示词创建精致图像。
通过 Alibaba Happy Horse 1.1 API 创建参考引导视频,具备可控运动、稳定性能和可扩展创意生产工作流。开放权重快乐马AI 适合角色动作、品牌素材、参考驱动短片、视觉探索和灵活参考转视频生成,便于围绕参考图像持续迭代创意并保持主体风格一致。
// 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;
// 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));
}
# 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']
# 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)
# 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
}'
# 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"
| 参数 | 价格 | 原价 | 折扣 |
|---|
Happy Horse 1.1 参考图生视频 API 为需要在生成片段中保持角色、产品、场景和品牌资产一致性的团队提供升级版阿里巴巴视频生成能力。这个专业级参考图生视频 API 集成以参考图片作为视觉锚点,再生成精致的视频输出,相比 Happy Horse 1.0,具备更强的主体一致性、更好的提示词遵循能力和更丰富的视觉质感。Happy Horse 1.1 参考图生视频面向 Flaq AI 上可扩展的创意生产而构建,非常适合广告、短剧、电商、角色工作流和多资产内容流水线。
注意 请确保你的提示词和参考图片符合阿里巴巴的安全准则。如果发生错误, 请检查输入是否包含受限内容,调整后再试。
Happy Horse 1.1 Reference-to-Video vs. Happy Horse 1.0
Happy Horse 1.0 支持实用的文本和图片视频生成。Happy Horse 1.1 参考图生视频通过更强的参考锚定、更好的主体一致性、更准确的提示词遵循和更丰富的视觉质感推进工作流。
Happy Horse 1.1 Reference-to-Video vs. Seedance 2.0 Reference-to-Video
Seedance 2.0 Reference-to-Video 提供字节跳动参考引导视频创作。Happy Horse 1.1 参考图生视频强调阿里巴巴升级版多参考理解和主体一致性,适用于商业、角色和营销活动工作流。
Happy Horse 1.1 Reference-to-Video vs. Kling Reference-to-Video
Kling 参考工作流在富有表现力的角色运动方面很强。Happy Horse 1.1 专注于一致的视觉锚定、更好的提示词遵循,以及面向需要可重复输出团队的生产级 API 集成。
Happy Horse 1.1 Reference-to-Video vs. Runway Video Tools
Runway 提供面向创作者的广泛视频控制。Happy Horse 1.1 参考图生视频 API 更适合程序化生成、多参考创意自动化,以及应用内部可扩展的生产。
Happy Horse 1.1 Reference-to-Video vs. Pika
Pika 为创作者提供易用的视频生成。Happy Horse 1.1 参考图生视频则提供更偏生产的 API 路径,用于一致的参考引导片段、品牌安全的视觉复用和自动化创意系统。
在浏览器中探索多种 AI 创作工具,用于快速图像和视频工作流,然后通过 Flaq AI 可用于生产的模型 API 扩展成功创意。Flaq AI 为所有模型提供统一 API 层,让你的工作流更容易使用和扩展。