atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video

image-to-video

TURBO

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

atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video

Infinite-image-to-video

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

INPUT

Loading parameter configuration...

OUTPUT

Idle

Your generated videos will appear here

Configure your settings and click Run to get started

Your request will cost $0.02 per run. For $10 you can run this model approximately 500 times.

Here's what you can do next:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parameters

Code Example
import requests
import time

# Step 1: Start video generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video",
    "prompt": "A beautiful sunset over the ocean with gentle waves",
    "width": 512,
    "height": 512,
    "duration": 3,
    "fps": 24,
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] in ["completed", "succeeded"]:
            print("Generated video:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

video_url = check_status()

Install

Install the required package for your language.

bash

pip install requests

Authentication

All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.

bash

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

HTTP Headers

python

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Keep your API key secure

Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.

Submit a request

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

Submit a Request

Submit an asynchronous generation request. The API returns a prediction ID that you can use to check the status and retrieve the result.

POST/api/v1/model/generateVideo

Request Body

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video",
    "input": {
        "prompt": "A beautiful sunset over the ocean with gentle waves"
    }
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['id']}")
print(f"Status: {result['status']}")

Response

{
  "id": "pred_abc123",
  "status": "processing",
  "model": "model-name",
  "created_at": "2025-01-01T00:00:00Z"
}

Check Status

Poll the prediction endpoint to check the current status of your request.

GET/api/v1/model/prediction/{prediction_id}

Polling Example

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

Status Values

processingThe request is still being processed.

completedGeneration is complete. Outputs are available.

succeededGeneration succeeded. Outputs are available.

failedGeneration failed. Check the error field.

Completed Response

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.mp4"
    ],
    "metrics": {
      "predict_time": 45.2
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Upload Files

Upload files to Atlas Cloud storage and get a URL you can use in your API requests. Use multipart/form-data to upload.

POST/api/v1/model/uploadMedia

Upload Example

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

Response

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

The following parameters are accepted in the request body.

Total: 0Required: 0Optional: 0

No parameters available.

Example Request Body

json

{
  "model": "atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video"
}

Output Schema

The API returns a prediction response with the generated output URLs.

idstringrequired

Unique identifier for the prediction.

statusstringrequired

Current status of the prediction.

processingcompletedsucceededfailed

modelstringrequired

The model used for generation.

outputsarray[string]

Array of output URLs. Available when status is "completed".

errorstring

Error message if status is "failed".

metricsobject

Performance metrics.

predict_timenumber

Time taken for video generation in seconds.

created_atstringrequired

ISO 8601 timestamp when the prediction was created.

Format: date-time

completed_atstring

ISO 8601 timestamp when the prediction was completed.

Format: date-time

Example Response

json

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.mp4"
  ],
  "metrics": {
    "predict_time": 45.2
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills integrates 300+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.

Supported Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ supported clients

Install

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

Setup API Key

Get your API key from the Atlas Cloud dashboard and set it as an environment variable.

bash

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

Capabilities

Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.

Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.

Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.

LLM ChatChat with Qwen, DeepSeek, and other large language models.

Media UploadUpload local files for image editing and image-to-video workflows.

Learn more

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server connects your IDE with 300+ AI models via the Model Context Protocol. Works with any MCP-compatible client.

Supported Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ supported clients

Install

bash

npx -y atlascloud-mcp

Configuration

Add the following configuration to your IDE's MCP settings file.

json

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Available Tools

atlas_generate_imageGenerate images from text prompts.

atlas_generate_videoCreate videos from text or images.

atlas_chatChat with large language models.

atlas_list_modelsBrowse 300+ available AI models.

atlas_quick_generateOne-step content creation with auto model selection.

atlas_upload_mediaUpload local files for API workflows.

Learn more

github.com/AtlasCloudAI/mcp-server

API Schema

Schema not available

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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:

Anchor-Frame Evolution — automatically extracts key "anchor frames" during generation as global temporal references.
Dual-Frame Constraint (Anchor + Last Frame) — combines the structural consistency of the global anchor frame with the motion continuity of the previous frame to construct video sequences autoregressively.
Semantic Stability — subject identity, scene details, and lighting stay consistent across multi-minute outputs, suppressing semantic drift and logical collapse.

3. Cinematic-Level Aesthetics (Inherited)

The model retains the curated training foundation of Wan 2.2:

Precise Control — detailed labels for lighting, composition, and color tone.
Complex Motion — superior generation of realistic, fluid motion across diverse semantics.

Why Infinite?

Most image-to-video models lock you into a single short clip (5–10 s). Infinite extends that into a controlled multi-segment clip — output duration equals prompt_count × duration_per_segment, up to 6 prompts x 5 s. Direct each segment with its own prompt; the API returns one server-stitched 30 fps MP4.

Prompts	Per-segment	Total output
1	5 s	5 s
3	5 s	15 s
6	5 s	30 s

60-second Quickstart

curl -X POST https://api.atlascloud.ai/api/v1/model/generateVideo \
  -H "Authorization: Bearer $APIKEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video",
    "image": "https://static.atlascloud.ai/media/images/db548fe3bd5cafa4ef7e0141d69c8566.jpeg",
    "prompt": [
      "She turns slowly toward the camera, golden hour light hitting her face.",
      "She walks forward through the wheat field, hand brushing the tops.",
      "Close-up: a single tear catches the sun as she smiles."
    ],
    "duration": 5,
    "resolution": "720p"
  }'

Returns one MP4 — segments are stitched server-side at 30 fps.

Base vs LoRA — which one?

	Base (this model)	LoRA variant
Model name	`atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video`	`…/infinite-image-to-video-lora`
Price (480 p, per second)	$0.020	$0.026 (+30 %)
Best for	Standard runs, fast iteration, bulk drafts	Higher fidelity, fine-grained control
Recommended for	Pre-production, A/B prompts	Final renders

Switch the variant by changing model only — all other fields are identical.

Request Fields

Field	Type	Required	Notes
`model`	string	✅	`atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video`
`image`	string (URL)	✅	Source frame; jpg/png
`prompt`	string[]	✅	Must be a JSON array. Plain string is rejected.
`duration`	number	✅	Fixed at 5 s per segment.
`resolution`	string	optional	`480p`, `720p`, or `1080p`. Defaults to `720p`.
`seed`	number	optional	`-1` for random

Pricing — at a glance

price = $0.020 × max(1, prompt_count) × max(5, duration_seconds) × resolution_factor
                                                                     480p → 1
                                                                     720p → 2
                                                                     1080p → 3

Common combos:

Prompts	Duration	Resolution	Total
1	5 s	480 p	$0.10
1	5 s	720 p	$0.20
1	5 s	1080 p	$0.30
3	5 s	720 p	$0.60
6	5 s	720 p	$1.20
6	5 s	1080 p	$1.80

Output Spec

Format: MP4 (H.264)
Frame rate: 30 fps (post-processed)
Resolution: 480 p / 720 p / 1080 p tiers, aspect-ratio preserving
Audio: none

Intended Use & Applications

Cinematic Long-Take Production — high-fidelity, consistent long-duration shots without manual stitching.
Low-Latency Interactive Content — leverage 4-step distillation for live broadcasts and AI-driven interactive installations.
Advanced Image-to-Video (I2V) — transform a static image into infinite, naturally moving visual scrolls via anchor-frame technology.
Professional Pre-visualization — minutes-long dynamic storyboards that compress pre-production time.

Content Policy

This model is tuned for adult-oriented (NSFW) generation. By calling it you confirm:

All depicted subjects are 18 +.
You hold the rights to the source image.
You will not generate content depicting real, identifiable people without their explicit consent.

Violations may result in account suspension.

Limitations

prompt must be a JSON array, never a plain string.
While anchor-frame technology suppresses cross-segment drift, it does not fully eliminate it — long prompts sharing fine identity details across many segments may still show minor variation.
480 p generates ~2× faster than 720 p; use 480 p for drafts.

LoRA variant: atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video-lora
Non-spicy alias: atlascloud/wan-2.2-turbo/infinite-image-to-video

Note: This model is designed to empower the creative community. Users are expected to follow AI ethical guidelines and copyright regulations.