bytedance/seedance-2.0/reference-to-video

gambar-ke-video

Seedance 2.0 Reference-to-Video API by ByteDance

bytedance/seedance-2.0/reference-to-video

Reference-to-video

Multimodal video generation from reference images, videos, and audio. Supports video editing and extension.

INPUT

Memuat konfigurasi parameter...

OUTPUT

Menunggu

Video yang dihasilkan akan muncul di sini

Konfigurasikan pengaturan Anda dan klik Jalankan untuk memulai

Setiap detik video 720p yang Anda hasilkan akan dikenakan tarif $0.2419/detik. Permintaan Anda berbiaya $0.0112 per 1000 token. Jumlah token dihitung dengan rumus (tinggi video output × lebar video output ×(durasi input + durasi output)× 24) / 1024. Jika input video disediakan, tarifnya turun menjadi $0.00688 per 1000 token. Dengan input video dan resolusi 720p, harganya $0.1486 per detik.

Berikut yang dapat Anda lakukan selanjutnya:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parameter

Contoh kode
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-2.0/reference-to-video",
    "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()

Instalasi

Instal paket yang diperlukan untuk bahasa pemrograman Anda.

bash

pip install requests

Autentikasi

Semua permintaan API memerlukan autentikasi melalui API key. Anda bisa mendapatkan API key dari dasbor Atlas Cloud.

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP Headers

python

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Jaga keamanan API key Anda

Jangan pernah mengekspos API key Anda di kode sisi klien atau repositori publik. Gunakan variabel lingkungan atau proxy backend sebagai gantinya.

Kirim permintaan

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())

Kirim Permintaan

Kirim permintaan pembuatan asinkron. API mengembalikan prediction ID yang dapat Anda gunakan untuk memeriksa status dan mengambil hasil.

POST/api/v1/model/generateVideo

Isi Permintaan

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-2.0/reference-to-video",
    "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']}")

Respons

{
  "id": "pred_abc123",
  "status": "processing",
  "model": "model-name",
  "created_at": "2025-01-01T00:00:00Z"
}

Periksa Status

Polling prediction endpoint untuk memeriksa status permintaan Anda saat ini.

GET/api/v1/model/prediction/{prediction_id}

Contoh Polling

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)

Nilai Status

processingPermintaan masih diproses.

completedPembuatan selesai. Output tersedia.

succeededPembuatan berhasil. Output tersedia.

failedPembuatan gagal. Periksa field error.

Respons Selesai

{
  "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"
  }
}

Unggah File

Unggah file ke penyimpanan Atlas Cloud dan dapatkan URL yang dapat Anda gunakan dalam permintaan API Anda. Gunakan multipart/form-data untuk mengunggah.

POST/api/v1/model/uploadMedia

Contoh Unggah

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}")

Respons

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

Parameter berikut diterima di isi permintaan.

Total: 0Wajib: 0Opsional: 0

Tidak ada parameter yang tersedia.

Contoh Isi Permintaan

json

{
  "model": "bytedance/seedance-2.0/reference-to-video"
}

Output Schema

API mengembalikan respons prediction dengan URL output yang dihasilkan.

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

Contoh Respons

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 Skills mengintegrasikan 300+ model AI langsung ke asisten pengkodean AI Anda. Satu perintah untuk menginstal, lalu gunakan bahasa alami untuk menghasilkan gambar, video, dan mengobrol dengan LLM.

Klien yang Didukung

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ klien yang didukung

Instalasi

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

Atur API Key

Dapatkan API key dari dasbor Atlas Cloud dan atur sebagai variabel lingkungan.

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

Kemampuan

Setelah diinstal, Anda dapat menggunakan bahasa alami di asisten AI Anda untuk mengakses semua model Atlas Cloud.

Pembuatan GambarBuat gambar dengan model seperti Nano Banana 2, Z-Image, dan lainnya.

Pembuatan VideoBuat video dari teks atau gambar dengan Kling, Vidu, Veo, dll.

Obrolan LLMMengobrol dengan Qwen, DeepSeek, dan model bahasa besar lainnya.

Unggah MediaUnggah file lokal untuk pengeditan gambar dan alur kerja gambar-ke-video.

Pelajari lebih lanjut

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server menghubungkan IDE Anda dengan 300+ model AI melalui Model Context Protocol. Berfungsi dengan klien apa pun yang kompatibel dengan MCP.

Klien yang Didukung

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ klien yang didukung

Instalasi

bash

npx -y atlascloud-mcp

Konfigurasi

Tambahkan konfigurasi berikut ke file pengaturan MCP di IDE Anda.

json

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Alat yang Tersedia

atlas_generate_imageBuat gambar dari prompt teks.

atlas_generate_videoBuat video dari teks atau gambar.

atlas_chatMengobrol dengan model bahasa besar.

atlas_list_modelsJelajahi 300+ model AI yang tersedia.

atlas_quick_generatePembuatan konten satu langkah dengan pemilihan model otomatis.

atlas_upload_mediaUnggah file lokal untuk alur kerja API.

Pelajari lebih lanjut

github.com/AtlasCloudAI/mcp-server

Schema API

Schema tidak tersedia

Tidak ada contoh yang tersedia

Silakan masuk untuk melihat riwayat permintaan

Anda perlu masuk untuk mengakses riwayat permintaan model Anda.

Masuk

1. Introduction

Seedance 2.0 is a state-of-the-art multimodal generative AI model designed for synchronized video and audio content creation. Developed by ByteDance and integrated into the CapCut/Dreamina platform as of March 2026, this model family advances the field of generative multimedia by combining sophisticated diffusion transformer architectures with physics-informed world modeling for realistic motion and spatial consistency.

Seedance 2.0’s significance lies in its Dual-Branch Diffusion Transformer (DB-DiT) architecture that jointly processes video and audio streams, enabling phoneme-level lip synchronization across multiple languages. Compared to previous iterations, it achieves substantially higher output usability rates and faster generation speeds. The two variants target different workloads: Seedance 2.0 delivers high-fidelity, cinematic-quality renders with enhanced lighting and texture detail, while Seedance 2.0 Fast provides a cost-effective, accelerated pipeline optimized for high throughput and rapid prototyping.

2. Key Features & Innovations

Dual-Branch Diffusion Transformer Architecture: Seedance 2.0 integrates separate yet synchronized diffusion branches for video and audio, enabling tight coupling between visual motion and sound generation. This architecture improves motion realism and audio-visual coherence beyond previous generative models.
World Model with Physics Simulation: The model incorporates a physics-based world modeling approach that simulates realistic object motion and spatial consistency over time. This leads to naturalistic dynamics and stable scene composition across generated video sequences.
Rich Multimodal Input Support: Seedance 2.0 accepts diverse input formats including text prompts, up to 9 images, and up to 3 video or audio clips of 15 seconds each. This flexibility allows nuanced content creation workflows combining static, dynamic, and auditory cues.
Phoneme-Level Lip Synchronization: The native audio generation pipeline supports lip-sync at the phoneme granularity in 8+ languages, ensuring high fidelity mouth movements closely match generated speech or singing.
High Usability and Efficiency: The model achieves an estimated 90% usable output rate compared to an industry average of approximately 20%, reducing post-processing overhead. Additionally, it delivers a 30% inference speed advantage over predecessor systems.
API Variants for Different Use Cases: The Seedance 2.0 endpoint is geared toward high fidelity and cinematic visual effects suitable for final production, while the Seedance 2.0 Fast variant offers roughly 3 times faster generation and approximately 91% cost savings at $0.022 per second of output, ideal for rapid iteration and volume workflows.

3. Model Architecture & Technical Details

Seedance 2.0 is built around the Dual-Branch Diffusion Transformer (DB-DiT), which separately processes video and audio streams via transformer-based denoising diffusion models while synchronizing generation steps to enforce audio-visual alignment. The system leverages a World Model that integrates physics simulation modules, enabling consistent spatial and temporal object behaviors within video sequences.

Training was conducted in multiple stages on large-scale, diverse datasets spanning images, videos, text captions, and audio recordings across multiple languages. Initial large-scale pre-training utilized resolutions spanning from 720p to 1080p, followed by supervised fine-tuning (SFT) to improve text and visual prompt conditioning fidelity. Reinforcement Learning with Human Feedback (RLHF) optimized multi-dimensional reward models that simultaneously assess aesthetics, motion coherence, and audio-visual synchronization quality.

The training pipeline supports multiple aspect ratios including 9:16, 16:9, 1:1, and 4:3, and target output lengths from 4 to 60 seconds. Specialized modules enable the @ reference system for fine-grained control of creative elements based on provided input assets.

4. Performance Highlights

Seedance 2.0 was benchmarked on the comprehensive SeedVideoBench-2.0 suite, which evaluates generative video models across over 50 image-based and 24 video-based benchmarks covering diverse content domains and multi-modal tasks.

Rank	Model	Developer	Score/Metric	Release Date
1	Kling 3.0	External	Competitive	2025
2	Sora 2	External	Competitive	2025
3	Seedance 2.0	ByteDance	High audiovisual sync, motion realism	2026
4	Veo 3.1	External	Strong baseline	2025

Seedance 2.0 matches or exceeds these contemporary models in synchronized video-audio generation, demonstrating especially strong performance in phoneme-level lip synchronization and motion naturalism thanks to the World Model component. Its 30% speed improvement and 90% output usability rate reflect notable efficiency advancements.

5. Intended Use & Applications

Social Media Content Creation: Efficiently generate engaging short videos with synchronized audio and visually rich effects, tailored for platforms like TikTok and Instagram.
E-commerce Product Videos: Automatically produce dynamic product showcases combining text, image, and video inputs with realistic motion and sound to enhance online shopping experiences.
Marketing Campaigns: Craft high-quality cinematic promotional content that integrates brand assets via the @ reference system for tailored storytelling and audience engagement.
Music Videos: Generate synchronized visuals with phoneme-accurate lip-syncing for multilingual vocal tracks to support artist and record label promotional needs.
Short Narrative Films: Create compelling narrative-driven video clips with coherent motion and spatial consistency, supporting indie filmmakers and content creators.
Fashion and Luxury Showcases: Produce visually detailed and aesthetic presentations incorporating texture and lighting refinements for high-end brand communications.