bytedance/seedance-2.0/reference-to-video

image-to-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

Loading parameter configuration...

OUTPUT

Idle

Your generated videos will appear here

Configure your settings and click Run to get started

For every second of 720p video you generated, you will be charged $0.2419/second. Your request will cost $0.0112 per 1000 tokens. The number of tokens is given by (height of output video × width of output video × (input duration + output duration) × 24) / 1024. If video inputs are provided the rate drops to $0.00688 per 1000 tokens. With video inputs and 720p resolution the price is $0.1486 per second.

Here's what you can do next:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parameters

Code Example
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()

Install

Install the required package for your language.

bash

pip install requests

Authentication

All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.

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}"
}

Keep your API key secure

Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.

Submit a request

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())

Submit a Request

Submit an asynchronous generation request. The API returns a prediction ID that you can use to check the status and retrieve the result.

POST/api/v1/model/generateVideo

Request Body

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']}")

Response

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

Check Status

Poll the prediction endpoint to check the current status of your request.

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

Polling Example

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)

Status Values

processingThe request is still being processed.

completedGeneration is complete. Outputs are available.

succeededGeneration succeeded. Outputs are available.

failedGeneration failed. Check the error field.

Completed Response

{
  "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"
  }
}

Upload Files

Upload files to Atlas Cloud storage and get a URL you can use in your API requests. Use multipart/form-data to upload.

POST/api/v1/model/uploadMedia

Upload Example

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}")

Response

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

Input Schema

The following parameters are accepted in the request body.

Total: 0Required: 0Optional: 0

No parameters available.

Example Request Body

json

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

Output Schema

The API returns a prediction response with the generated output URLs.

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

Example Response

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 integrates 300+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.

Supported Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ supported clients

Install

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

Setup API Key

Get your API key from the Atlas Cloud dashboard and set it as an environment variable.

bash

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

Capabilities

Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.

Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.

Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.

LLM ChatChat with Qwen, DeepSeek, and other large language models.

Media UploadUpload local files for image editing and image-to-video workflows.

Learn more

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server connects your IDE with 300+ AI models via the Model Context Protocol. Works with any MCP-compatible client.

Supported Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ supported clients

Install

bash

npx -y atlascloud-mcp

Configuration

Add the following configuration to your IDE's MCP settings file.

json

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

Available Tools

atlas_generate_imageGenerate images from text prompts.

atlas_generate_videoCreate videos from text or images.

atlas_chatChat with large language models.

atlas_list_modelsBrowse 300+ available AI models.

atlas_quick_generateOne-step content creation with auto model selection.

atlas_upload_mediaUpload local files for API workflows.

Learn more

github.com/AtlasCloudAI/mcp-server

API Schema

Schema not available

No examples available

Please log in to view request history

You need to be logged in to access your model request history.

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.