
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。
免費試用 Vidu Q3 參考素材生成影片 API,支援最多四張參考圖片、1080p 輸出、選用音效、彈性長度和種子控制。適合彈性影片創作與測試。
// 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: 'viduq3-reference-to-video',
prompt: 'Keep the character from image one and the jacket details from image two while walking through a rainy city',
resolution: '1080p',
duration: 8,
aspect_ratio: '9:16',
images: [
'https://example.com/character-reference.jpg',
'https://example.com/outfit-reference.jpg'
],
sound: true,
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: 'viduq3-reference-to-video',
prompt: 'Keep the character from <<<image_1>>> wearing the coat from <<<image_2>>> while entering the rainy city shown in <<<image_3>>>, then return to a close-up of <<<image_1>>>',
resolution: '1080p',
duration: 8,
aspect_ratio: '9:16',
images: [
'https://example.com/character-reference.jpg',
'https://example.com/coat-reference.jpg',
'https://example.com/rainy-city-reference.jpg'
],
sound: true,
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': 'viduq3-reference-to-video',
'prompt': 'Keep the character from image one and the jacket details from image two while walking through a rainy city',
'resolution': '1080p',
'duration': 8,
'aspect_ratio': '9:16',
'images': [
'https://example.com/character-reference.jpg',
'https://example.com/outfit-reference.jpg'
],
'sound': True,
'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': 'viduq3-reference-to-video',
'prompt': 'Keep the character from <<<image_1>>> wearing the coat from <<<image_2>>> while entering the rainy city shown in <<<image_3>>>, then return to a close-up of <<<image_1>>>',
'resolution': '1080p',
'duration': 8,
'aspect_ratio': '9:16',
'images': [
'https://example.com/character-reference.jpg',
'https://example.com/coat-reference.jpg',
'https://example.com/rainy-city-reference.jpg'
],
'sound': True,
'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": "viduq3-reference-to-video",
"prompt": "Keep the character from image one and the jacket details from image two while walking through a rainy city",
"resolution": "1080p",
"duration": 8,
"aspect_ratio": "9:16",
"images": [
"https://example.com/character-reference.jpg",
"https://example.com/outfit-reference.jpg"
],
"sound": true,
"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": "viduq3-reference-to-video",
"prompt": "Keep the character from <<<image_1>>> wearing the coat from <<<image_2>>> while entering the rainy city shown in <<<image_3>>>, then return to a close-up of <<<image_1>>>",
"resolution": "1080p",
"duration": 8,
"aspect_ratio": "9:16",
"images": [
"https://example.com/character-reference.jpg",
"https://example.com/coat-reference.jpg",
"https://example.com/rainy-city-reference.jpg"
],
"sound": true,
"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"
| 參數 | 價格 | 原價 | 折扣 |
|---|
Vidu Q3 Reference-to-Video API 為需要一致角色、物件和場景的開發者與創意團隊 提供生產就緒的 AI 影片生成。這項 Reference-to-Video API 整合使用一張或多張參考圖片 錨定主體身分,同時生成帶有同步原生音訊的連貫多鏡頭影片。它基於 Vidu 新一代 Q3 模型建構,結合智慧鏡頭切換、強大的跨鏡頭一致性和彈性的輸出 控制,為 Flaq AI 上可擴展的敘事工作流程提供支援。
注意 請確保提示詞和參考圖片符合 Vidu 的內容安全準則。如果發生錯誤, 請檢查輸入中是否含有受限內容,進行調整後重試。
Vidu Q3 Reference-to-Video 與 Vidu Q3 Image-to-Video 比較 Vidu Q3 Image-to-Video 可讓起始圖片動起來。Vidu Q3 Reference-to-Video 使用多個主體參考素材,在內容更豐富的多鏡頭 敘事生成中保持身分和連續性。
Vidu Q3 Reference-to-Video 與 Vidu Q3 Turbo Reference-to-Video 比較 Vidu Q3 Turbo 優先追求極致速度和成本 效率。Vidu Q3 著重在不同機位間實現更強的一致性,並為敘事、品牌和 角色工作流程提供均衡的製作品質。
Vidu Q3 Reference-to-Video 與 Seedance V2.0 Reference-to-Video 比較 Seedance V2.0 支援廣泛的混合模態 參考輸入。Vidu Q3 透過多圖片主體錨定、智慧鏡頭切換,以及 緊密同步的原生對話、音效和音樂展現差異化優勢。
Vidu Q3 Reference-to-Video 與 Wan 2.7 Reference-to-Video 比較 Wan 2.7 支援混合圖片、影片和可選聲音 參考素材。Vidu Q3 著重高效的多圖片一致性、原生音訊與影片輸出,以及跨鏡頭變化的連貫 敘事。
Vidu Q3 Reference-to-Video 與 Kling Video O3 Reference-to-Video 比較 Kling Video O3 提供強大的參考素材引導 動作與場景推理。Vidu Q3 提供多圖片主體一致性、原生多語言音訊和智慧 鏡頭切換,用於創作完整的視聽故事。
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。