
文本转图像
从文本提示词创建 AI 图像
通过 Seedance 2.0 参考图生视频 API 实现高质量视频生成,支持写实人物生成,并具备内置声音生成能力。它可融合参考信息并保持稳定输出,适合大规模自动化视频生产与高一致性批量创作,显著降低成本同时提升内容水准。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-reference-to-video',
prompt: 'Use image one for the subject, video one for the movement, and audio one for the atmosphere',
resolution: '1080p',
duration: 8,
aspect_ratio: '16:9',
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-reference-to-video',
prompt: 'Place the explorer from <<<image_1>>> in the environment from <<<image_2>>>, following the camera movement in <<<video_1>>> and speaking with the reference voice from <<<audio_1>>>',
resolution: '1080p',
duration: 10,
aspect_ratio: '16:9',
sound: true,
images: [
'https://example.com/explorer-reference.jpg',
'https://example.com/environment-reference.jpg'
],
videos: ['https://example.com/camera-movement-reference.mp4'],
audios: ['https://example.com/voice-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-reference-to-video',
'prompt': 'Use image one for the subject, video one for the movement, and audio one for the atmosphere',
'resolution': '1080p',
'duration': 8,
'aspect_ratio': '16:9',
'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-reference-to-video',
'prompt': 'Place the explorer from <<<image_1>>> in the environment from <<<image_2>>>, following the camera movement in <<<video_1>>> and speaking with the reference voice from <<<audio_1>>>',
'resolution': '1080p',
'duration': 10,
'aspect_ratio': '16:9',
'sound': True,
'images': [
'https://example.com/explorer-reference.jpg',
'https://example.com/environment-reference.jpg'
],
'videos': ['https://example.com/camera-movement-reference.mp4'],
'audios': ['https://example.com/voice-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-reference-to-video",
"prompt": "Use image one for the subject, video one for the movement, and audio one for the atmosphere",
"resolution": "1080p",
"duration": 8,
"aspect_ratio": "16:9",
"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-reference-to-video",
"prompt": "Place the explorer from <<<image_1>>> in the environment from <<<image_2>>>, following the camera movement in <<<video_1>>> and speaking with the reference voice from <<<audio_1>>>",
"resolution": "1080p",
"duration": 10,
"aspect_ratio": "16:9",
"sound": true,
"images": [
"https://example.com/explorer-reference.jpg",
"https://example.com/environment-reference.jpg"
],
"videos": ["https://example.com/camera-movement-reference.mp4"],
"audios": ["https://example.com/voice-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 参考生视频 API 在 Flaq AI 上为开发者和创意 团队提供参考引导的视频生成。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。它支持可配置的时长、多种宽高比、 多个分辨率档位,以及适用于可控视频工作流的可选生成声音设置。
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 参考生视频与 Seedance V2.0 文生视频 Seedance V2.0 文生视频根据文本 提示词运行。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 参考生视频与 Seedance V2.0 Fast 参考生视频 两个版本提供相同的 参考媒体类型和 Flaq AI 核心控制项。标准版本增加了 1080p 和 4K 分辨率选项,而 Fast 版本专注于 480p 和 720p 工作流。
Seedance V2.0 参考生视频与 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 支持多个可选的音频 参考和生成声音开关。
Seedance V2.0 参考生视频与 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 还接受参考视频和可选的参考音频。
Seedance V2.0 参考生视频与 Runway 视频工具 Runway 提供更广泛的交互式创作套件。 Seedance V2.0 参考生视频围绕提示词、受支持的参考素材上传、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。
MiniMax H3 API 为视频团队提供了一种实用的组合:图生视频生成、768p 和 2K 输出选项、最长 15 秒的片段,以及比多个可比的 Seedance 2.0 设置更低的标示成本。MiniMax 还已在 Hugging Face 上发布了 H3-Base 权重,为研究、自定义流水线以及在其许可证允许范围内的自托管实验带来了新的选择。
比较 Higgsfield MCP 和 CLI 与 Flaq AI 的 Seedance 2.0 REST 端点、定价、任务轮询、模型变体、测试方式以及实际生产用例。
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。