# Vidu Q3 Reference-to-Video — Atlas Cloud API

> Vidu Q3 Reference-to-Video generates videos from 1-4 reference images with consistent subjects. Features intelligent camera switching with better consistency across multiple camera positions, audio support, and resolutions up to 1080p.

This is the machine-readable API reference for **Vidu Q3 Reference-to-Video** on Atlas Cloud,
a unified API platform for 400+ AI models across text, image, video, audio and 3D.

- **Model ID**: `vidu/q3/reference-to-video`
- **Built by**: Vidu
- **Modality**: Video
- **Model page**: https://www.atlascloud.ai/models/vidu/q3/reference-to-video
- **API key**: https://www.atlascloud.ai/console/api-keys
- **Docs**: https://www.atlascloud.ai/docs

## Pricing on Atlas Cloud

- $0.042 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/q3/reference-to-video"`.
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/q3/reference-to-video -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/q3/reference-to-video`


## 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/q3/reference-to-video"`

- **`images`** (`array[string]`, _required_):
  Reference images for generating video with consistent subjects. Accepts 1 to 4 images as URLs or Base64 encoded strings. Supported codecs: PNG, JPEG, JPG, WebP. Dimensions must be at least 128x128 pixels, aspect ratio less than 1:4 or 4:1, and size limited to 50MB.
  - Default: `["https://static.atlascloud.ai/media/images/1745492230971199265_J3J5tO7p.jpg","https://static.atlascloud.ai/media/images/1745492247387641003_Z3jDAxur.jpg"]`
  - Min items: 1
  - Max items: 4

- **`prompt`** (`string`, _required_):
  A textual description for video generation.
  - Default: `"Santa Claus and the bear hug by the lakeside."`

- **`duration`** (`number`, _optional_):
  The duration of the generated video in seconds.
  - Default: `5`
  - Min: 3
  - Max: 16

- **`resolution`** (`string`, _optional_):
  The resolution of the generated media. Native 540p, 720p, and 1080p use the original Vidu route when available. 1080p-sr generates a native 720p source video and applies FlashVSR super-resolution. 1440p-sr generates a native 1080p source video and applies FlashVSR super-resolution.
  - Default: `"720p"`
  - Options: "540p", "720p", "1080p", "1080p-sr", "1440p-sr"

- **`generate_audio`** (`boolean`, _optional_):
  Whether to generate audio for the video.
  - Default: `true`

- **`aspect_ratio`** (`string`, _optional_):
  The aspect ratio of the output video.
  - Default: `"16:9"`
  - Options: "16:9", "9:16", "3:4", "4:3", "1:1"

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

- **`seed`** (`integer`, _optional_):
  The random seed to use for the generation. Set -1 for random.
  - Default: `0`



**Required Parameters Example**:

```json
{
  "model": "vidu/q3/reference-to-video",
  "prompt": "Santa Claus and the bear hug by the lakeside.",
  "images": [
    "https://static.atlascloud.ai/media/images/1745492230971199265_J3J5tO7p.jpg",
    "https://static.atlascloud.ai/media/images/1745492247387641003_Z3jDAxur.jpg"
  ]
}
```


**Full Example**:

```json
{
  "model": "vidu/q3/reference-to-video",
  "images": [
    "https://static.atlascloud.ai/media/images/1745492230971199265_J3J5tO7p.jpg",
    "https://static.atlascloud.ai/media/images/1745492247387641003_Z3jDAxur.jpg"
  ],
  "prompt": "Santa Claus and the bear hug by the lakeside.",
  "duration": 5,
  "resolution": "720p",
  "generate_audio": true,
  "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.

- **`has_nsfw_contents`** (`array[boolean]`, _optional_):
  Array of boolean values indicating NSFW detection for each output.



**Example Response**:

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


## 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/q3/reference-to-video",
  "images": [
    "https://static.atlascloud.ai/media/images/1745492230971199265_J3J5tO7p.jpg",
    "https://static.atlascloud.ai/media/images/1745492247387641003_Z3jDAxur.jpg"
  ],
  "prompt": "Santa Claus and the bear hug by the lakeside.",
  "duration": 5,
  "resolution": "720p",
  "generate_audio": true,
  "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/q3/reference-to-video)

## About this model

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

#### 1. Introduction

**Vidu Q3** is an advanced AI video generation model developed by Shengshu Technology (生数科技) in collaboration with Tsinghua University. Released on January 30, 2026, Vidu Q3 is designed to produce high-fidelity, synchronized audio-visual content with industry-leading continuous video length and native support for integrated audio generation.

The model represents a significant advancement in automated video synthesis by unifying multiple complex video generation tasks—such as lip-synced dialogue, dynamic camera movements, and multi-shot storytelling—into a single-pass framework. Leveraging a novel Transformer-based diffusion architecture, Vidu Q3 sets a new standard for cinematic and marketing video content creation with its combination of spatial-temporal coherence, multimodal input flexibility, and real-time directorial control.

---

#### 2. Key Features & Innovations

- **Native Audio-Video Synchronization**: Vidu Q3 generates lip-synced dialogue, sound effects, and background music simultaneously within a single pass, ensuring precise temporal alignment between audio tracks and visual lip movements without requiring post-processing.

- **Extended High-Definition Video Generation**: Supports up to 16 seconds of continuous video at 1080p resolution and 24 frames per second—the longest continuous generation duration among leading competitors—enabling more complex storytelling sequences.

- **Smart Cuts for Scene Detection**: Integrates automatic scene boundary detection and multi-shot narrative transitions, which facilitate the smooth generation of dynamic video scenes without manual intervention.

- **Native Camera Control**: Allows frame-level directorial commands such as pans, push-ins, and tracking shots within the generation pipeline, granting users granular cinematic control over the resulting video composition.

_(Description truncated. Full text on the model page.)_

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Atlas Cloud — one API for 400+ AI models. Model page: https://www.atlascloud.ai/models/vidu/q3/reference-to-video · Docs: https://www.atlascloud.ai/docs