minimax/hailuo-02/pro

image-to-video

PRO

Hailuo 02 is a new AI video generation model from Hailuo AI.

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 0.48 per run. For $10 you can run this model approximately 20 times.

Here's what you can do next:

Video Upscaler Video extend

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": "minimax/hailuo-02/pro",
    "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": "minimax/hailuo-02/pro",
    "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": "minimax/hailuo-02/pro"
}

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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What Makes Hailuo 02 Outperform Other AI Video Generation Models?

1080P Native Output

Nobody wants blurry footage. Hailuo 02 gives you the full HD 1080P video straight from the model, not upscaled afterwards. This is a huge leap from the previously limited 720P. Your video will look sharp even on those big screens. Ads, explainers, shorts—it all matters if it looks clear. And Hailuo 02 can deliver that clarity with just a few clicks.

Multiple Duration Options

Flexibility means a lot with AI video generation, and Hailuo 02 offers clip format in both 6-second and 10-second formats. Creators can mix and match formats seamlessly, and the length of content becomes more controllable without impacting quality. It's a small detail but has a big impact on workflow. There’s no need to re-render full scenes when mocking up combinations—just generate and combine.

Enhanced Motion & Physical Effects

This model doesn’t merely understand movement—it completely owns it. Hailuo 02 is equipped with state-of-the-art motion rendering, allowing it to capture dynamic scenes and physical interactions more smoothly than most competitors. It adapts easily to prompts involving complex physics, such as flying debris, bouncing objects, or camera shakes. The result: videos that feel organic and cinematic.

Better Scene Transitions

Image-to-video models can sometimes produce awkward cuts or uneven frame stitching, but Hailuo 02 really smooths things out. Transitions between frames resemble real-world camera work more than simple machine stitching. The end result is closer to a movie trailer than a slideshow, especially for creators transforming still images into dynamic sequences.

Real-World Reliability

Hailuo 02 doesn’t just show strong benchmark performance—it delivers in real creative use cases. Users have reported consistent output when repeating the same prompts. This excellent consistency and low randomness is a major benefit for professionals who value repeatability, especially in team environments with high standards.

Lower Cost

AI tools tend to be powerful but pricey—Hailuo 02 is both powerful and affordable. Compared to premium tools like Veo 3 or closed systems like Sora, it offers incredible value. Designed for creators and startups, not just enterprises, it’s a great entry point for building professional-quality video content on a budget.

What Users Are Saying About Hailuo 02

"The physics are insane. I have to say that not every video came out perfect on the first try, but I still got great results really easily. The way cats jump is just… perfect."

"Truly impressed by how well it follows prompts. Even when I pushed the boundaries of imagination, it executed beautifully. Less distortion, stunning fast motion. Welcome back, king!"

"After many long waits, the return of Hailuo AI is here. With their latest model, 02. It packs a punch, with 1080p visuals, physics, prompt adherence strength, and cinematic capabilities. I present to you a compilation of my tests so far."

"With up to 1080p output and 10-second clips, this new model delivers accurate prompt adherence, dynamic motion, and some of the best physics understanding we’ve seen yet. It feels like a serious leap forward!"

Use Cases of Hailuo 02

Short-form social content

Ideal for TikTok, Instagram Reels, or YouTube Shorts. Hailuo 02 provides cinematic visuals in just seconds.
Ad and product videos

Create high-resolution, motion-rich videos that make products stand out—no full production team required.
Game and film prototyping

Rapidly visualize characters, scenes, and trailers before full-scale production.
Educational and training videos

Turn complex concepts into visually engaging animated sequences—perfect for teachers, course creators, and businesses.
AI-enhanced storytelling

Animate still images into cinematic micro-scenes without any animation skills.
Presentation and pitch support

Make your idea or product pitch unforgettable with custom AI-generated video sequences.

Tips for Using Hailuo 02

Use cinematic prompts

Think like a director—describe angles, lighting, and scene dynamics to enhance visual richness.
Use motion-rich language

Incorporate action verbs like "zoom in," "pan left," or "fly over" to drive dynamic camera effects.
Guide your video with a first frame

Uploading a still image helps the model lock in composition, structure, and mood.
Start small, go big later

Begin with 6-second clips to test ideas, then extend or merge for longer sequences.
Preview, tweak, repeat

Don’t aim for perfection right away. Iterate quickly and refine results through multiple generations.

FAQs

Q1: Does Hailuo 02 support audio generation?

No—for now, it focuses purely on visuals. You can manually pair it with music, voiceovers, or sound effects for full creative control.

Q2: Can I use it for commercial projects?

Absolutely. Just ensure you review the licensing terms and platform policies if you intend to monetize your output.

Q3: How long does video generation take?

Typically, it takes 30 to 90 seconds for a 6-second or 10-second clip, depending on prompt complexity.

Q4: Is there a mobile version?

While there’s no dedicated app, most platforms supporting Hailuo 02 run smoothly on mobile browsers.

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