# Wan 2.2 spicy 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 spicy Image-to-Video Lora** on Atlas Cloud,
a unified API platform for 400+ AI models across text, image, video, audio and 3D.

- **Model ID**: `alibaba/wan-2.2-spicy/image-to-video-lora`
- **Built by**: Alibaba
- **Modality**: Video
- **Model page**: https://www.atlascloud.ai/models/alibaba/wan-2.2-spicy/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: "alibaba/wan-2.2-spicy/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 alibaba/wan-2.2-spicy/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**: `alibaba/wan-2.2-spicy/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 image for generating the output.

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

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

- **`duration`** (`integer`, _optional_):
  The duration of the generated media in seconds.
  - Default: `5`
  - Options: 5, 8

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

- **`low_noise_loras`** (`array`, _optional_):
  List of low noise LoRAs to apply (max 3).
  - 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": "alibaba/wan-2.2-spicy/image-to-video-lora",
  "image": "",
  "prompt": ""
}
```


**Full Example**:

```json
{
  "model": "alibaba/wan-2.2-spicy/image-to-video-lora",
  "image": "",
  "prompt": "",
  "resolution": "480p",
  "duration": 5,
  "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 (e.g., “2023-04-01T12:34:56.789Z”).

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

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

- **`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": "",
  "has_nsfw_contents": [],
  "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": "alibaba/wan-2.2-spicy/image-to-video-lora",
  "image": "",
  "prompt": "",
  "resolution": "480p",
  "duration": 5,
  "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/alibaba/wan-2.2-spicy/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  |
| **Developed by** | Alibaba Tongyi Wanxiang Lab |
| **Release Date** | July 28, 2025 |
| **Model Type** | Video Generation |
| **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-T2V-A14B](https://huggingface.co/Wan-AI/Wan2.2-T2V-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. The primary goal of Wan 2.2 is to provide an open and advanced suite of tools for generating high-quality, cinematic videos from various inputs, including text, images, and audio. Its core contribution lies in making state-of-the-art video generation technology accessible to a broader community of researchers and creators through open-sourcing its models and code. The project emphasizes cinematic aesthetics, complex motion generation, and computational efficiency, introducing several key innovations to achieve these aims.

#### Key Features & Innovations

Wan 2.2 introduces several groundbreaking features that set it apart from previous models:

*   **Effective MoE Architecture:** Wan 2.2 is the first model to successfully integrate a Mixture-of-Experts (MoE) architecture into a video diffusion model. This design uses specialized expert models for different stages of the denoising process, which significantly increases the model's capacity without raising computational costs. The model has a total of 27B parameters, but only 14B are active during any given step.

*   **Cinematic-Level Aesthetics:** The model was trained on a meticulously curated dataset with detailed

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