# Wan 2.2 Turbo Spicy Infinite Image-to-Video — Atlas Cloud API

> Image-to-video model for segmented prompt video generation with stable motion and 30fps workflow post-processing.

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

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

## Pricing on Atlas Cloud

- $0.02 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-turbo-spicy/infinite-image-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 atlascloud/wan-2.2-turbo-spicy/infinite-image-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**: `atlascloud/wan-2.2-turbo-spicy/infinite-image-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: `"atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video"`

- **`prompt`** (`array[string]`, _required_):
  Ordered prompt list. Each segment generates duration seconds.
  - Default: `["The kitten is wandering on the road."]`
  - Min items: 1
  - Max items: 6

- **`image`** (`string`, _required_):
  First-frame image URL or Base64 image.

- **`duration`** (`integer`, _optional_):
  Seconds generated for each prompt segment.
  - Default: `5`
  - Min: 5
  - Max: 5

- **`resolution`** (`string`, _optional_):
  Target output resolution after 30fps post-processing.
  - Default: `"720p"`
  - Options: "480p", "720p", "1080p"

- **`seed`** (`integer`, _optional_):
  - Default: `-1`



**Required Parameters Example**:

```json
{
  "model": "atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video",
  "prompt": [
    "The kitten is wandering on the road."
  ],
  "image": ""
}
```


**Full Example**:

```json
{
  "model": "atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video",
  "prompt": [
    "The kitten is wandering on the road."
  ],
  "image": "",
  "duration": 5,
  "resolution": "720p",
  "seed": -1
}
```


### Output Schema

The API returns the following output format:


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

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

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

- **`status`** (`string`, _optional_):
  Status: processing, completed, failed, or timeout.

- **`outputs`** (`array[string]`, _optional_):
  URLs to generated video outputs.

- **`created_at`** (`string`, _optional_):
  ISO timestamp of creation.

- **`has_nsfw_contents`** (`array[boolean]`, _optional_):
  NSFW detection per output.

- **`error`** (`string`, _optional_):
  Error message when generation fails.



**Example Response**:

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


## 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-turbo-spicy/infinite-image-to-video",
  "prompt": [
    "The kitten is wandering on the road."
  ],
  "image": "",
  "duration": 5,
  "resolution": "720p",
  "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-turbo-spicy/infinite-image-to-video)

## 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 Turbo Spicy Infinite Image-to-Video

#### Model Overview

| Field | Description |
| :--- | :--- |
| **Model Name** | `atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video` |
| **Model Type** | Advanced Image-to-Video Generation |
| **Core Architecture** | Mixture-of-Experts (MoE) |
| **Active Parameters** | 14B |
| **Variant** | Base |
| **Tuning** | Spicy-tuned post-processing pipeline (adult-oriented) |

**Wan 2.2 Turbo Spicy Infinite Image-to-Video** is an enhanced image-to-video model built on the Wan 2.2 foundation. Inheriting the **Mixture-of-Experts (MoE)** architecture and cinematic-level aesthetics of the original Wan series, this variant introduces two breakthroughs — **inference acceleration** and **infinite-length generation** — and ships with a spicy-tuned post-processing pipeline for adult-oriented creative work.

---

#### Key Features & Innovations

##### 1. Ultra-Fast Inference: 4-Step Distillation with RCM
To address the high latency typical of large-scale models, we apply specialized sampling optimization and knowledge distillation:

* **RCM (Refined Consistency Model) Sampler** — a more efficient ODE solver that significantly improves single-step sampling quality.
* **4-Step Distillation** — denoising steps are compressed to **4 steps** through multi-stage distillation, enabling cinematic-grade generation at a fraction of the original cost and unlocking low-latency interaction.

##### 2. Infinite-Length Generation: Anchor-Frame Autoregressive Architecture
A targeted retraining gives the model an advanced temporal extension mechanism that breaks the duration limits of traditional video models:

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

---

Atlas Cloud — one API for 400+ AI models. Model page: https://www.atlascloud.ai/models/atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video · Docs: https://www.atlascloud.ai/docs