Seedance v1 Lite t2v 480p API by ByteDance
bytedance/seedance-v1-lite-t2v-480p
Seedance-v1-lite-t2v-480p
An efficient text-to-video model geared toward fast, cost-effective generation. Ideal for prototyping short narrative clips (5–10 s) with stylistic flexibility and prompt-faithful motion.
입력
매개변수 구성 로드 중...
출력
대기생성된 비디오가 여기에 표시됩니다
설정을 구성하고 실행을 클릭하여 시작하세요
요청당 $0.014가 소요됩니다. $10로 이 모델을 약 714번 실행할 수 있습니다.
다음으로 할 수 있는 작업:
코드 예시
import requests
import time
# Step 1: Start video generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
"model": "bytedance/seedance-v1-lite-t2v-480p",
"prompt": "A beautiful sunset over the ocean with gentle waves",
"width": 512,
"height": 512,
"duration": 3,
"fps": 24,
}
generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]
# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
def check_status():
while True:
response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
result = response.json()
if result["data"]["status"] in ["completed", "succeeded"]:
print("Generated video:", result["data"]["outputs"][0])
return result["data"]["outputs"][0]
elif result["data"]["status"] == "failed":
raise Exception(result["data"]["error"] or "Generation failed")
else:
# Still processing, wait 2 seconds
time.sleep(2)
video_url = check_status()설치
사용하는 언어에 필요한 패키지를 설치하세요.
bash
pip install requests인증
모든 API 요청에는 API 키를 통한 인증이 필요합니다. Atlas Cloud 대시보드에서 API 키를 받을 수 있습니다.
bash
export ATLASCLOUD_API_KEY="your-api-key-here"HTTP 헤더
python
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}API 키를 안전하게 보관하세요
클라이언트 측 코드나 공개 저장소에 API 키를 노출하지 마세요. 대신 환경 변수 또는 백엔드 프록시를 사용하세요.
요청 제출
import requests
url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
"model": "your-model",
"prompt": "A beautiful landscape"
}
response = requests.post(url, headers=headers, json=data)
print(response.json())요청 제출
비동기 생성 요청을 제출합니다. API는 상태 확인 및 결과 조회에 사용할 수 있는 예측 ID를 반환합니다.
POST
/api/v1/model/generateVideo요청 본문
import requests
url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
"model": "bytedance/seedance-v1-lite-t2v-480p",
"input": {
"prompt": "A beautiful sunset over the ocean with gentle waves"
}
}
response = requests.post(url, headers=headers, json=data)
result = response.json()
print(f"Prediction ID: {result['id']}")
print(f"Status: {result['status']}")응답
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}상태 확인
예측 엔드포인트를 폴링하여 요청의 현재 상태를 확인합니다.
GET
/api/v1/model/prediction/{prediction_id}폴링 예시
import requests
import time
prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }
while True:
response = requests.get(url, headers=headers)
result = response.json()
status = result["data"]["status"]
print(f"Status: {status}")
if status in ["completed", "succeeded"]:
output_url = result["data"]["outputs"][0]
print(f"Output URL: {output_url}")
break
elif status == "failed":
print(f"Error: {result['data'].get('error', 'Unknown')}")
break
time.sleep(3)상태 값
processing요청이 아직 처리 중입니다.completed생성이 완료되었습니다. 출력을 사용할 수 있습니다.succeeded생성이 성공했습니다. 출력을 사용할 수 있습니다.failed생성에 실패했습니다. 오류 필드를 확인하세요.완료 응답
{
"data": {
"id": "pred_abc123",
"status": "completed",
"outputs": [
"https://storage.atlascloud.ai/outputs/result.mp4"
],
"metrics": {
"predict_time": 45.2
},
"created_at": "2025-01-01T00:00:00Z",
"completed_at": "2025-01-01T00:00:10Z"
}
}파일 업로드
Atlas Cloud 스토리지에 파일을 업로드하고 API 요청에 사용할 수 있는 URL을 받습니다. multipart/form-data를 사용하여 업로드합니다.
POST
/api/v1/model/uploadMedia업로드 예시
import requests
url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }
with open("image.png", "rb") as f:
files = {"file": ("image.png", f, "image/png")}
response = requests.post(url, headers=headers, files=files)
result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")응답
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}입력 Schema
다음 매개변수가 요청 본문에서 사용 가능합니다.
전체: 0필수: 0선택: 0
사용 가능한 매개변수가 없습니다.
요청 본문 예시
json
{
"model": "bytedance/seedance-v1-lite-t2v-480p"
}출력 Schema
API는 생성된 출력 URL이 포함된 예측 응답을 반환합니다.
idstringrequired
Unique identifier for the prediction.
statusstringrequired
Current status of the prediction.
processingcompletedsucceededfailed
modelstringrequired
The model used for generation.
outputsarray[string]
Array of output URLs. Available when status is "completed".
errorstring
Error message if status is "failed".
metricsobject
Performance metrics.
predict_timenumber
Time taken for video generation in seconds.
created_atstringrequired
ISO 8601 timestamp when the prediction was created.
completed_atstring
ISO 8601 timestamp when the prediction was completed.
응답 예시
json
{
"id": "pred_abc123",
"status": "completed",
"model": "model-name",
"outputs": [
"https://storage.atlascloud.ai/outputs/result.mp4"
],
"metrics": {
"predict_time": 45.2
},
"created_at": "2025-01-01T00:00:00Z",
"completed_at": "2025-01-01T00:00:10Z"
}Atlas Cloud Skills
Atlas Cloud Skills는 300개 이상의 AI 모델을 AI 코딩 어시스턴트에 직접 통합합니다. 한 번의 명령으로 설치하고 자연어로 이미지, 동영상 생성 및 LLM과 대화할 수 있습니다.
지원 클라이언트
Claude Code
OpenAI Codex
Gemini CLI
Cursor
Windsurf
VS Code
Trae
GitHub Copilot
Cline
Roo Code
Amp
Goose
Replit
40+ 지원 클라이언트
설치
bash
npx skills add AtlasCloudAI/atlas-cloud-skillsAPI 키 설정
Atlas Cloud 대시보드에서 API 키를 받아 환경 변수로 설정하세요.
bash
export ATLASCLOUD_API_KEY="your-api-key-here"기능
설치 후 AI 어시스턴트에서 자연어를 사용하여 모든 Atlas Cloud 모델에 접근할 수 있습니다.
이미지 생성Nano Banana 2, Z-Image 등의 모델로 이미지를 생성합니다.
동영상 제작Kling, Vidu, Veo 등으로 텍스트나 이미지에서 동영상을 만듭니다.
LLM 채팅Qwen, DeepSeek 등 대규모 언어 모델과 대화합니다.
미디어 업로드이미지 편집 및 이미지-동영상 변환 워크플로우를 위해 로컬 파일을 업로드합니다.
MCP Server
Atlas Cloud MCP Server는 Model Context Protocol을 통해 IDE와 300개 이상의 AI 모델을 연결합니다. MCP 호환 클라이언트에서 사용할 수 있습니다.
지원 클라이언트
Cursor
VS Code
Windsurf
Claude Code
OpenAI Codex
Gemini CLI
Cline
Roo Code
100+ 지원 클라이언트
설치
bash
npx -y atlascloud-mcp설정
다음 설정을 IDE의 MCP 설정 파일에 추가하세요.
json
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}사용 가능한 도구
atlas_generate_image텍스트 프롬프트로 이미지를 생성합니다.
atlas_generate_video텍스트나 이미지로 동영상을 만듭니다.
atlas_chat대규모 언어 모델과 대화합니다.
atlas_list_models300개 이상의 사용 가능한 AI 모델을 탐색합니다.
atlas_quick_generate자동 모델 선택으로 원스텝 콘텐츠 생성.
atlas_upload_mediaAPI 워크플로우를 위해 로컬 파일을 업로드합니다.
API 스키마
스키마를 사용할 수 없음ByteDance Seedance Lite T2V 480p
ByteDance Seedance Lite T2V 480p is an optimized AI text-to-video generation model developed by ByteDance, now available on WaveSpeedAI. This cost-effective model transforms text prompts into dynamic 5-10 second videos at 480p resolution with efficient processing speed, offering quality visual outputs with enhanced motion and semantic understanding. Part of the Seedance Lite model family, this model delivers excellent performance in text-to-video synthesis at an affordable price point.
Key Features
- Fast Video Generation: Efficient processing creates 5-10 second videos at 480p resolution with vivid details and smooth motion.
- Quality Motion Rendering: Advanced dynamic rendering techniques create natural and realistic movements that bring text descriptions to life.
- Enhanced Semantic Understanding: AI excels in interpreting text prompts to generate coherent and dynamic scenes with professional quality.
- Realistic Physical Simulation: Physics engine simulates realistic physical properties and movements for lifelike video generation.
- Optimized Processing: Balanced for speed and quality, allowing efficient creation of high-quality videos.
- Flexible Parameter Control: Customizable settings offer duration (5-10s), aspect ratio and style adjustments for creative control.
- Professional Quality Output: Lite technology ensures quality results with smooth temporal consistency.
- Cost-Effective Creation: Transform text descriptions into engaging, dynamic content at an affordable price.
Technical Excellence & Optimization
- Lite Architecture: Built on optimized research delivering excellent text-to-video generation performance at reduced cost.
- Efficient Processing: ByteDance technology delivers superior generation efficiency compared to standard T2V solutions.
- Balanced Performance: Optimized video synthesis enables efficient creative workflows and content creation.
- High-Performance Computing: Optimized infrastructure supports efficient, high-volume video generation.
Perfect for Budget-Conscious Creative Work
- Content Creators: Transform text ideas into engaging video content for social platforms at affordable rates.
- Marketing Professionals: Create dynamic promotional videos from text descriptions cost-effectively.
- Social Media Managers: Convert text concepts into shareable, dynamic content that captures attention without breaking budgets.
- E-commerce Teams: Generate product demonstration videos from text descriptions using cost-effective T2V technology.
- Small Businesses: Deliver professional video content with budget-friendly video generation capabilities.
Performance & Efficiency Advantages
- Quality Results: Generate professional videos efficiently with optimized processing speed.
- Cost-Effective Workflow: Advanced AI enables creative iteration and content production at reduced costs.
- Scalable Efficiency: Handle multiple text-to-video conversions efficiently without performance degradation.
- Optimized Value: Advanced algorithms maximize quality while maintaining cost-effectiveness.
Limitations
- Creative Focus: Designed primarily for creative video synthesis; not intended for generating factually accurate content.
- Inherent Biases: Outputs may reflect biases present in the training data, typical of current models.
- Input Sensitivity: The quality and consistency of generated videos depend significantly on the quality of the input text prompt; subtle variations may lead to output variability.
- Resolution Limitation: This model is optimized for 480p video generation and does not support higher resolutions.
- Lite Performance: While cost-effective, may have slightly reduced capabilities compared to Pro versions.
Out-of-Scope Use
The model and its derivatives may not be used in any way that violates applicable national, federal, state, local, or international law or regulation, including but not limited to:
- Exploiting, harming, or attempting to exploit or harm minors, including solicitation, creation, acquisition, or dissemination of child exploitative content.
- Generating or disseminating verifiably false information with the intent to harm others.
- Creating or distributing personal identifiable information that could be used to harm an individual.
- Harassing, abusing, threatening, stalking, or bullying individuals or groups.
- Producing non-consensual nudity or illegal pornographic content.
- Making fully automated decisions that adversely affect an individual's legal rights or create binding obligations.
- Facilitating large-scale disinformation campaigns.
