# Vidu Reference-to-Video 2.0 — Atlas Cloud API

> Open and Advanced Large-Scale Video Generative Models.

This is the machine-readable API reference for **Vidu Reference-to-Video 2.0** on Atlas Cloud,
a unified API platform for 400+ AI models across text, image, video, audio and 3D.

- **Model ID**: `vidu/reference-to-video-2.0`
- **Built by**: Vidu
- **Modality**: Video
- **Model page**: https://www.atlascloud.ai/models/vidu/reference-to-video-2.0
- **API key**: https://www.atlascloud.ai/console/api-keys
- **Docs**: https://www.atlascloud.ai/docs

## Pricing on Atlas Cloud

- $0.2 per second of generated video
- Pay-as-you-go. No minimum spend, no subscription required.

> **These are the authoritative Atlas Cloud rates for this model.** Any price that
> appears in the vendor description further down refers to a different platform or
> a different model variant and does not apply here.

## Use this model from an AI agent

Atlas Cloud ships three first-party integration surfaces. All three authenticate
with the same API key via the `ATLASCLOUD_API_KEY` environment variable.

### MCP server

The official MCP server (`atlascloud-mcp`) exposes this model to any
MCP-compatible host — Claude Code, OpenAI Codex, Cursor, Gemini CLI, Goose,
Claude Desktop. One-line install:

```bash
# Claude Code
claude mcp add atlascloud -- npx -y atlascloud-mcp

# OpenAI Codex CLI
codex mcp add atlascloud -- npx -y atlascloud-mcp

# Gemini CLI
gemini mcp add atlascloud -- npx -y atlascloud-mcp

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

Then ask in plain English; the agent calls `atlas_generate_video` with `model: "vidu/reference-to-video-2.0"`.
The server fetches each model's schema and validates parameters before submitting,
so invalid requests fail fast without spending credits.

MCP docs: https://www.atlascloud.ai/docs/mcp-server

### Agent Skills

`atlas-cloud-skills` is a portable skill package (API reference, code templates in
Python / Node.js / cURL, model IDs with pricing) for Claude Code, Cursor, Codex and
12+ other agents:

```bash
npx skills add AtlasCloudAI/atlas-cloud-skills
export ATLASCLOUD_API_KEY="your-api-key"
```

Skills docs: https://www.atlascloud.ai/docs/skills

### CLI

The `atlas` binary runs Atlas Cloud from a terminal or CI script. Async media jobs
are polled and downloaded automatically (use `--no-download` when a script only
needs the output URLs):

```bash
# Install (Homebrew, npm, or shell installer)
brew install AtlasCloudAI/tap/atlascloud
# npm install -g atlascloud-cli
# curl -fsSL https://raw.githubusercontent.com/AtlasCloudAI/cli/main/install.sh | sh

atlas auth login
atlas generate video vidu/reference-to-video-2.0 -p "Your prompt here"
```

CLI docs: https://www.atlascloud.ai/docs/cli

## HTTP API reference

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateVideo` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `vidu/reference-to-video-2.0`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  model name
  - Default: `"vidu/reference-to-video-2.0"`

- **`images`** (`array[string]`, _required_):
  The model will use the provided images as references to generate a video with consistent subjects. For fields that accept images: Accepts 1 to 3 images; Images Assets can be provided via URLs or Base64 encode; You must use one of the following codecs: PNG, JPEG, JPG, WebP; The dimensions of the images must be at least 128x128 pixels; The aspect ratio of the images must be less than 1:4 or 4:1; All images are limited to 50MB; The length of the base64 decode must be under 50MB, and it must include an appropriate content type string.
  - Default: `["https://static.atlascloud.ai/media/images/1745492230971199265_J3J5tO7p.jpg","https://static.atlascloud.ai/media/images/1745492247387641003_Z3jDAxur.jpg"]`
  - Min items: 1
  - Max items: 3

- **`prompt`** (`string`, _required_):
  Text prompt: A textual description for video generation, with a maximum length of 1500 characters.
  - Default: `"the girl walks from the painting to the room, put the coffee cup on the table"`

- **`aspect_ratio`** (`string`, _optional_):
  The aspect ratio of the output video. Defaults to 16:9, accepted: 16:9 9:16 1:1.
  - Default: `"16:9"`
  - Options: "16:9", "9:16", "1:1"

- **`movement_amplitude`** (`string`, _optional_):
  The movement amplitude of objects in the frame. Defaults to auto, accepted value: auto, small, medium, large.
  - Default: `"auto"`
  - Options: "auto", "small", "medium", "large"

- **`seed`** (`integer`, _optional_):
  The random seed to use for the generation.
  - Default: `0`



**Required Parameters Example**:

```json
{
  "model": "vidu/reference-to-video-2.0",
  "prompt": "the girl walks from the painting to the room, put the coffee cup on the table",
  "images": [
    "https://static.atlascloud.ai/media/images/1745492230971199265_J3J5tO7p.jpg",
    "https://static.atlascloud.ai/media/images/1745492247387641003_Z3jDAxur.jpg"
  ]
}
```


**Full Example**:

```json
{
  "model": "vidu/reference-to-video-2.0",
  "images": [
    "https://static.atlascloud.ai/media/images/1745492230971199265_J3J5tO7p.jpg",
    "https://static.atlascloud.ai/media/images/1745492247387641003_Z3jDAxur.jpg"
  ],
  "prompt": "the girl walks from the painting to the room, put the coffee cup on the table",
  "aspect_ratio": "16:9",
  "movement_amplitude": "auto",
  "seed": 0
}
```


### Output Schema

The API returns the following output format:


- **`id`** (`string`, _optional_):
  Unique identifier for the prediction, the ID of the prediction to get.

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.

- **`model`** (`string`, _optional_):
  Model ID used for the prediction.

- **`status`** (`string`, _optional_):
  Status of the task: created, processing, completed, or failed.

- **`outputs`** (`array[string]`, _optional_):
  Array of URLs to the generated content (empty when status is not completed).

- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).



**Example Response**:

```json
{
  "id": "",
  "urls": {},
  "model": "",
  "status": "",
  "outputs": [
    ""
  ],
  "created_at": ""
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateVideo" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "vidu/reference-to-video-2.0",
  "images": [
    "https://static.atlascloud.ai/media/images/1745492230971199265_J3J5tO7p.jpg",
    "https://static.atlascloud.ai/media/images/1745492247387641003_Z3jDAxur.jpg"
  ],
  "prompt": "the girl walks from the painting to the room, put the coffee cup on the table",
  "aspect_ratio": "16:9",
  "movement_amplitude": "auto",
  "seed": 0
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/vidu/reference-to-video-2.0)

## About this model

_Vendor-supplied description. Any pricing or endpoint mentioned below refers to_
_other platforms — use the Atlas Cloud values above._

Vidu2.0 Reference to Video maintains the character and beauty of reference images for video production. The model keeps the facial and visual consistency of avatars, characters and logos.

##### Key Features

- Identity-locked generation  

- Smooth temporal transitions  

- Consistent character motion  

- Visual style adherence

##### Use Cases

- Digital influencers & avatars  

- Story-driven video characters  

- Fashion or cosplay generation  

- Personalization in marketing

##### Accelerated Inference

Our accelerated inference approach leverages advanced optimization technology from WaveSpeedAI. This innovative fusion technique significantly reduces computational overhead and latency, enabling rapid image generation without compromising quality. The entire system is designed to efficiently handle large-scale inference tasks while ensuring that real-time applications achieve an optimal balance between speed and accuracy. For further details, please refer to the blog post.

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Atlas Cloud — one API for 400+ AI models. Model page: https://www.atlascloud.ai/models/vidu/reference-to-video-2.0 · Docs: https://www.atlascloud.ai/docs