bytedance/seedance-v1-pro-i2v-480p

이미지를 비디오로

PRO

Seedance v1 Pro i2v 480p API by ByteDance

bytedance/seedance-v1-pro-i2v-480p

Seedance-v1-pro-i2v-480p

Seedance Pro’s image-to-video mode transforms still visuals into cinematic motion, maintaining visual consistency and expressive animation across frames.

입력

매개변수 구성 로드 중...

출력

대기

생성된 비디오가 여기에 표시됩니다

설정을 구성하고 실행을 클릭하여 시작하세요

요청당 $0.022가 소요됩니다. $10로 이 모델을 약 454번 실행할 수 있습니다.

다음으로 할 수 있는 작업:

Seedance 2.0 Kling v3 Vidu Wan2.7

파라미터

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-pro-i2v-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 requests

인증

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/generateVideo

요청 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-pro-i2v-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/uploadMedia

업로드 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-pro-i2v-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.

Format: date-time

completed_atstring

ISO 8601 timestamp when the prediction was completed.

Format: date-time

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-skills

API 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.

더 알아보기

github.com/AtlasCloudAI/atlas-cloud-skills

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-mcp

설정

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.

더 알아보기

github.com/AtlasCloudAI/mcp-server

API 스키마

스키마를 사용할 수 없음

요청 기록을 보려면 로그인하세요

모델 요청 기록에 액세스하려면 로그인해야 합니다.

로그인

ByteDance Seedance Pro I2V 480p

ByteDance Seedance Pro I2V 480p is a revolutionary AI image-to-video generation model developed by ByteDance, now exclusively available on WaveSpeedAI as a global premiere launch. This cutting-edge model transforms static images into dynamic 5-second videos at 480p resolution with lightning-fast processing speed, offering high-quality visual outputs with enhanced motion and semantic understanding. Part of the Dreamina model family, this SOTA-level model delivers unprecedented performance in image-to-video synthesis.

Key Features

Ultra-Fast Video Generation: Lightning-speed processing creates 5-second videos at 480p resolution with vivid details and smooth motion in seconds, not minutes.
SOTA Motion Rendering: State-of-the-art dynamic rendering techniques create natural and realistic movements that bring images to life.
Advanced Semantic Understanding: Industry-leading AI excels in interpreting complex user prompts to generate coherent and dynamic scenes with professional quality.
Realistic Physical Simulation: SOTA physics engine simulates realistic physical properties and movements for lifelike video generation.
Blazing-Fast Processing: Optimized for maximum speed efficiency, allowing instant creation of high-quality videos for real-time workflows.
Flexible Parameter Control: Customizable settings offer duration and style adjustments for complete creative control.
Professional Quality Output: SOTA image-to-video technology ensures broadcast-ready results with smooth temporal consistency.
Instant Creative Enhancement: Transform ordinary photos into engaging, dynamic content perfect for social media marketing in seconds.

Global Premiere

Exclusive Launch: WaveSpeedAI is the first platform globally to offer ByteDance's latest I2V technology from the Dreamina series.
Cutting-Edge Access: Be among the first worldwide to experience SOTA image-to-video generation capabilities.
Premium Integration: Seamless API access to ByteDance's most advanced video synthesis technology.
Pioneer Advantage: Early access to breakthrough AI technology before wider market availability.

Technical Excellence & Speed Optimization

SOTA Architecture: Built on breakthrough research delivering state-of-the-art image-to-video generation performance.
Optimized Processing: Native ByteDance technology delivers superior generation speed compared to standard I2V solutions.
Real-Time Processing: Ultra-fast video synthesis enables immediate creative workflows and instant content creation.
High-Performance Computing: Enterprise-grade infrastructure supports rapid, high-volume video generation at scale.

Perfect for Fast-Paced Creative Work

Content Creators: Instantly transform static photography into engaging video content for social platforms with lightning-fast speed.
Marketing Professionals: Create dynamic promotional materials from product images in seconds, not hours.
Social Media Managers: Convert photos into shareable, dynamic content that captures attention with lightning-fast turnaround.
E-commerce Teams: Bring product photos to life with subtle motion effects using SOTA I2V technology.
Digital Agencies: Deliver client projects faster with instant video generation capabilities from the Dreamina model family.

Performance & Speed Advantages

Instant Results: Generate professional videos in seconds with SOTA processing speed.
Real-Time Workflow: Advanced AI enables immediate creative iteration and rapid content production.
Scalable Speed: Handle multiple image-to-video conversions simultaneously without performance degradation.
Optimized Efficiency: Advanced algorithms maximize speed while maintaining SOTA quality standards.

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 SOTA models.
Input Sensitivity: The quality and consistency of generated videos depend significantly on the quality of the input image; subtle variations may lead to output variability.
Resolution Limitation: This model is optimized for 480p video generation and does not support higher resolutions.
Speed vs Quality Trade-off: While optimized for speed, extremely complex scenes may require additional processing time.

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.