
文本转图像
从文本提示词创建 AI 图像
Seedance 2.0 Fast 参考图生视频 API 支持写实人物生成,并内置声音合成能力,专注低延迟与高稳定性优化。它适合快速响应、大规模任务和多样化批量定制流程,是追求高效与低成本编辑产出的实用工具。适合开发者和团队接入图片、视频或多模态生成流程。你可以先体验核心效果,再用于创意设计、营销素材、批量内容生产和自动化工作流。
// 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: 'seedance-v2.0-fast-reference-to-video',
prompt: 'Use image one for the subject, follow the movement from video one, and use audio one for the atmosphere',
resolution: '720p',
duration: 8,
aspect_ratio: '9:16',
sound: true,
images: ['https://example.com/subject-reference.jpg'],
videos: ['https://example.com/motion-reference.mp4'],
audios: ['https://example.com/atmosphere-reference.mp3']
})
});
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], <<<audio_1>>> = audios[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: 'seedance-v2.0-fast-reference-to-video',
prompt: 'Have the dancer from <<<image_1>>> perform the movement from <<<video_1>>> on the stage in <<<image_2>>>, using the rhythm from <<<audio_1>>>',
resolution: '720p',
duration: 10,
aspect_ratio: '9:16',
sound: true,
images: [
'https://example.com/dancer-reference.jpg',
'https://example.com/stage-reference.jpg'
],
videos: ['https://example.com/dance-movement-reference.mp4'],
audios: ['https://example.com/rhythm-reference.mp3']
})
});
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': 'seedance-v2.0-fast-reference-to-video',
'prompt': 'Use image one for the subject, follow the movement from video one, and use audio one for the atmosphere',
'resolution': '720p',
'duration': 8,
'aspect_ratio': '9:16',
'sound': True,
'images': ['https://example.com/subject-reference.jpg'],
'videos': ['https://example.com/motion-reference.mp4'],
'audios': ['https://example.com/atmosphere-reference.mp3']
}
)
result = response.json()
task_id = result['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], <<<audio_1>>> = audios[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': 'seedance-v2.0-fast-reference-to-video',
'prompt': 'Have the dancer from <<<image_1>>> perform the movement from <<<video_1>>> on the stage in <<<image_2>>>, using the rhythm from <<<audio_1>>>',
'resolution': '720p',
'duration': 10,
'aspect_ratio': '9:16',
'sound': True,
'images': [
'https://example.com/dancer-reference.jpg',
'https://example.com/stage-reference.jpg'
],
'videos': ['https://example.com/dance-movement-reference.mp4'],
'audios': ['https://example.com/rhythm-reference.mp3']
}
)
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": "seedance-v2.0-fast-reference-to-video",
"prompt": "Use image one for the subject, follow the movement from video one, and use audio one for the atmosphere",
"resolution": "720p",
"duration": 8,
"aspect_ratio": "9:16",
"sound": true,
"images": ["https://example.com/subject-reference.jpg"],
"videos": ["https://example.com/motion-reference.mp4"],
"audios": ["https://example.com/atmosphere-reference.mp3"]
}'
# 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], <<<audio_1>>> = audios[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": "seedance-v2.0-fast-reference-to-video",
"prompt": "Have the dancer from <<<image_1>>> perform the movement from <<<video_1>>> on the stage in <<<image_2>>>, using the rhythm from <<<audio_1>>>",
"resolution": "720p",
"duration": 10,
"aspect_ratio": "9:16",
"sound": true,
"images": [
"https://example.com/dancer-reference.jpg",
"https://example.com/stage-reference.jpg"
],
"videos": ["https://example.com/dance-movement-reference.mp4"],
"audios": ["https://example.com/rhythm-reference.mp3"]
}'
# 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 |
|---|
ByteDance Seedance V2.0 Fast 参考生视频 API 在 Flaq AI 上提供经济高效的参考引导视频生成,适用于快速 创意工作流。当前 API 集成接受文本提示词和至少一张参考图像或一个参考视频,还可选择添加其他图像、 视频和音频参考。它支持 480p 和 720p 输出、可配置的 时长、多种宽高比,以及可选的生成声音设置。
注意 至少需要一张参考图像或一个参考视频;不能将音频单独用作唯一的参考输入。 请确保您的提示词和参考媒体符合 ByteDance 的内容安全准则。
Seedance V2.0 Fast 与 Seedance V2.0 Standard 参考生视频 两个版本在 Flaq AI 上提供相同的参考媒体 类型和核心控制项。Fast 版本使用 480p 和 720p 输出档位,而标准版本还 提供 1080p 和 4K 选项。
Seedance V2.0 Fast 与 Seedance V2.0 Fast 文生视频 Fast 文生视频根据文本提示词运行。Fast 参考生视频增加了受支持的图像、视频和可选音频输入,以及在提示词中提及媒体的功能。
Seedance V2.0 Fast 与 Wan 2.7 参考生视频 两种 API 都接受图像和视频参考。Wan 2.7 还提供负面提示词和种子控制,而 Seedance V2.0 Fast 支持多个可选的音频 参考和生成声音开关。
Seedance V2.0 Fast 与 Vidu Q3 参考生视频 在当前 Flaq AI 配置中,Vidu Q3 参考生视频使用图像参考。Seedance V2.0 Fast 还接受参考视频和可选的参考音频。
Seedance V2.0 Fast 与 Runway 视频工具 Runway 提供更广泛的交互式创作套件。Seedance V2.0 Fast 参考生视频围绕提示词、受支持的参考素材上传、高效的输出 设置和生成声音,提供专注的 API 工作流。
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。
用于电影级视频的 Seedance 2.5 API 现已在 Flaq AI 上线,使创作者和开发者能够直接使用基于提示词的视频生成功能,并可选择添加声音、灵活设置宽高比、输出 480p 或 720p,以及生成 4 到 30 秒的片段。
Seedance 2.0 Mini API 正在进入发布观察阶段。了解开发者应核查哪些事项、它可能如何与 Seedance 2.0 对比,以及为什么 Flaq AI 能帮助 API 团队制定规划。
探索 Seedance 2.5 的发布前景、可能的 API 升级、与 Seedance 2.0 的对比要点、提示词测试,以及面向视频构建者的 Flaq AI 工作流规划。