# Wan 2.2 Image-to-Video Lora — Atlas Cloud API

> Open and Advanced Large-Scale Video Generative Models.

This is the machine-readable API reference for **Wan 2.2 Image-to-Video Lora** on Atlas Cloud,
a unified API platform for 400+ AI models across text, image, video, audio and 3D.

- **Model ID**: `atlascloud/wan-2.2/image-to-video-lora`
- **Built by**: Alibaba
- **Modality**: Video
- **Model page**: https://www.atlascloud.ai/models/atlascloud/wan-2.2/image-to-video-lora
- **API key**: https://www.atlascloud.ai/console/api-keys
- **Docs**: https://www.atlascloud.ai/docs

## Pricing on Atlas Cloud

- $0.04 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: "atlascloud/wan-2.2/image-to-video-lora"`.
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 atlascloud/wan-2.2/image-to-video-lora -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**: `atlascloud/wan-2.2/image-to-video-lora`


## 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:

- **`image`** (`string`, _required_):
  The first-frame image URL for generating the video.

- **`prompt`** (`string`, _required_):
  The positive prompt for the generation.

- **`negative_prompt`** (`string`, _optional_):
  The negative prompt to avoid certain content in the generation.
  - Default: `""`

- **`resolution`** (`string`, _optional_):
  The resolution of the generated video.
  - Default: `"480p"`
  - Options: "480p", "720p"

- **`duration`** (`integer`, _optional_):
  The duration of the generated video in seconds.
  - Default: `5`
  - Min: 3
  - Max: 10

- **`loras`** (`array`, _optional_):
  List of LoRAs to apply (max 3). Module is auto-inferred from the safetensors filename.
  - Max items: 3

- **`high_noise_loras`** (`array`, _optional_):
  List of high noise LoRAs to apply (max 3). Loaded into the transformer (high noise stage).
  - Max items: 3

- **`low_noise_loras`** (`array`, _optional_):
  List of low noise LoRAs to apply (max 3). Loaded into transformer_2 (low noise stage).
  - Max items: 3

- **`seed`** (`integer`, _optional_):
  The random seed to use for the generation. -1 means a random seed will be used.
  - Default: `-1`



**Required Parameters Example**:

```json
{
  "model": "atlascloud/wan-2.2/image-to-video-lora",
  "image": "",
  "prompt": ""
}
```


**Full Example**:

```json
{
  "model": "atlascloud/wan-2.2/image-to-video-lora",
  "image": "",
  "prompt": "",
  "negative_prompt": "",
  "resolution": "480p",
  "duration": 5,
  "loras": [],
  "high_noise_loras": [],
  "low_noise_loras": [],
  "seed": -1
}
```


### Output Schema

The API returns the following output format:


- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created.

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

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

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

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

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



**Example Response**:

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


## 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": "atlascloud/wan-2.2/image-to-video-lora",
  "image": "",
  "prompt": "",
  "negative_prompt": "",
  "resolution": "480p",
  "duration": 5,
  "loras": [],
  "high_noise_loras": [],
  "low_noise_loras": [],
  "seed": -1
}'

# 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/atlascloud/wan-2.2/image-to-video-lora)

## About this model

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

### Wan 2.2: Open and Advanced Large-Scale Video Generative Model by Alibaba Wanxiang

#### Model Card Overview

| Field | Description |
| :--- | :--- |
| **Model Name** | Wan 2.2 Image-to-Video LoRA |
| **Developed by** | Alibaba Tongyi Wanxiang Lab |
| **Model Type** | Image-to-Video Generation with LoRA Support |
| **Resolution** | 480p, 720p (via VSR upscaling) |
| **Frame Rate** | 30 fps |
| **Duration** | 3–10 seconds |
| **Related Links** | **GitHub:** [https://github.com/Wan-Video/Wan2.2](https://github.com/Wan-Video/Wan2.2), **Hugging Face:** [https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B), **Paper (arXiv):** [https://arxiv.org/abs/2503.20314](https://arxiv.org/abs/2503.20314) |

#### Introduction

Wan 2.2 is a significant upgrade to the Wan series of foundational video models, designed to push the boundaries of generative AI in video creation. This image-to-video LoRA variant takes a reference image as the first frame and generates a high-quality video, with full support for custom LoRA weights to fine-tune the generation style, motion characteristics, or subject identity.

The model generates videos at 480p natively and supports 720p output via Video Super Resolution (VSR) upscaling, delivering smooth 30 fps playback at both resolutions.

#### Key Features & Innovations

*   **Effective MoE Architecture:** Wan 2.2 integrates a Mixture-of-Experts (MoE) architecture into the video diffusion model. Specialized expert models handle different stages of the denoising process, increasing model capacity without raising computational costs. The model has 27B total parameters with only 14B active during any given step.

*   **Cinematic-Level Aesthetics:** Trained on a meticulously curated dataset with detailed labels for cinematic properties like lighting, composition, and color tone. This allows generation of videos with precise and controllable artistic styles, achieving a professional, cinematic look.

_(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/atlascloud/wan-2.2/image-to-video-lora · Docs: https://www.atlascloud.ai/docs