
AI 文字轉圖片生成器
使用領先 AI 圖片模型、彈性設定與快速瀏覽器工作流程,從提示詞建立精緻圖片。
試用 ByteDance Seedance 2.5 參考素材生成影片 API,需輸入影片參考,並可選擇參考圖片,支援 4–30 秒時長、聲音及 720p 輸出。還可組合最多 30 張圖片、10 段影片和 10 段音訊參考,並支援多種畫面比例,讓多種參考素材在同一工作流程中共同引導影片生成。
// 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-v2.5-reference-to-video',
prompt: 'Use image one for the subject, video one for the movement, and audio one for the atmosphere',
resolution: '720p',
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-v2.5-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: '720p',
duration: 10,
aspect_ratio: '16:9',
sound: true,
images: [
'https://example.com/explorer-reference.jpg',
'https://example.com/environment-reference.png'
],
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-v2.5-reference-to-video',
'prompt': 'Use image one for the subject, video one for the movement, and audio one for the atmosphere',
'resolution': '720p',
'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-v2.5-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': '720p',
'duration': 10,
'aspect_ratio': '16:9',
'sound': True,
'images': [
'https://example.com/explorer-reference.jpg',
'https://example.com/environment-reference.png'
],
'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-v2.5-reference-to-video",
"prompt": "Use image one for the subject, video one for the movement, and audio one for the atmosphere",
"resolution": "720p",
"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-v2.5-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": "720p",
"duration": 10,
"aspect_ratio": "16:9",
"sound": true,
"images": [
"https://example.com/explorer-reference.jpg",
"https://example.com/environment-reference.png"
],
"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"
| 參數 | 價格 | 原價 | 折扣 |
|---|
ByteDance Seedance 2.5 Reference-to-Video API 將必要的參考影片與選用的圖像、音訊和 其他影片素材結合,以引導在 Flaq AI 上生成新影片。開發者和創意團隊可以使用多模態 參考素材,比單純使用文字更精確地傳達主體外觀、產品、環境、動作、時序、聲音和視覺方向。 靈活的品質、時長、長寬比和生成聲音控制,使該 API 適用於 品牌內容、角色工作流程、產品敘事和可擴充的創意系統。
注意 至少需要一個參考影片。參考圖像為選用項目,提供影片時可以省略; 僅有音訊或圖像無法替代必要的影片輸入。請確保所有提示詞和媒體均符合 ByteDance 的內容安全準則。
Seedance 2.5 Reference-to-Video 與 Seedance 2.5 Text-to-Video
Text-to-Video 僅根據文字指示
生成影片。Reference-to-Video 增加了必要的影片指導,以及選用的圖像和音訊,適合需要更
具體控制動作、外觀、時序或風格的工作流程。
Seedance 2.5 Reference-to-Video 與 Seedance 2.5 Image-to-Video
Image-to-Video 為必要的首幀加入動畫,
並可使用選用的尾幀。Reference-to-Video 從必要的影片素材開始,還可將其與選用的
圖像和音訊素材結合,提供更全面的多模態指導。
Seedance 2.5 Reference-to-Video 與 Wan Reference-to-Video
兩種方式都使用上傳的媒體來引導新影片
生成。Seedance 2.5 提供必須上傳影片的工作流程,並支援選用的多圖像和音訊參考,以及透過 Flaq AI 提供的
靈活輸出控制。
Seedance 2.5 Reference-to-Video 與 Vidu Reference-to-Video
Vidu 提供參考素材驅動的影片創作,以保持視覺
一致性。Seedance 2.5 的差異在於必須使用影片進行指導,並能在單個請求中組合選用的圖像和
音訊參考。
Seedance 2.5 Reference-to-Video 與 Runway 影片工具
Runway 提供廣泛的互動式創作套件。
Seedance 2.5 Reference-to-Video API 提供專注的程式化工作流程,支援多模態媒體輸入、基於提示詞的
參考素材指導和可擴充生成。
在瀏覽器中探索多種 AI 創作工具,快速完成圖片與影片工作流程,然後透過 Flaq AI 的生產級模型 API 擴展成功想法。Flaq AI 為所有模型提供統一 API 層,讓你的工作流程更容易使用和擴展。