Usage Examples

Complete Python Examples

Basic Text-to-Video

import openai
import time

# Initialize client
client = openai.OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.laozhang.ai/v1"
)

def generate_video(prompt, model="sora_video2"):
    """Generate video"""
    try:
        response = client.chat.completions.create(
            model=model,
            messages=[
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "text",
                            "text": prompt
                        }
                    ]
                }
            ]
        )

        return response.choices[0].message.content
    except Exception as e:
        print(f"Error: {e}")
        return None

# Usage example
prompt = "A cute cat playing with a ball in a sunny garden"
result = generate_video(prompt)
print(result)

Streaming Output Example

import openai

client = openai.OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.laozhang.ai/v1"
)

def generate_video_stream(prompt, model="sora_video2"):
    """Generate video with streaming, displaying progress in real-time"""
    stream = client.chat.completions.create(
        model=model,
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": prompt
                    }
                ]
            }
        ],
        stream=True
    )

    video_url = None
    for chunk in stream:
        if chunk.choices[0].delta.content:
            content = chunk.choices[0].delta.content
            print(content, end='', flush=True)

            # Extract video link
            if "https://" in content and ".mp4" in content:
                import re
                match = re.search(r'https://[^\s\)]+\.mp4', content)
                if match:
                    video_url = match.group(0)

    print("\n")
    return video_url

# Usage example
prompt = "A cute cat playing with a ball in a sunny garden"
video_url = generate_video_stream(prompt)
print(f"\nVideo link: {video_url}")

Image-to-Video (URL)

import openai

client = openai.OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.laozhang.ai/v1"
)

def generate_video_from_image(prompt, image_url, model="sora_video2"):
    """Generate video from image"""
    response = client.chat.completions.create(
        model=model,
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": prompt
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": image_url
                        }
                    }
                ]
            }
        ]
    )

    return response.choices[0].message.content

# Usage example
prompt = "Generate video: Make this figurine jump out from the desk and become a living person~"
image_url = "https://filesystem.site/cdn/download/20250407/OhFd8JofOAJCsNOCsM1Y794qnkNO3L.png"
result = generate_video_from_image(prompt, image_url)
print(result)

Image-to-Video (Base64)

import openai
import base64

client = openai.OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.laozhang.ai/v1"
)

def encode_image(image_path):
    """Encode local image to base64"""
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

def generate_video_from_local_image(prompt, image_path, model="sora_video2"):
    """Generate video from local image"""
    # Encode image
    base64_image = encode_image(image_path)

    response = client.chat.completions.create(
        model=model,
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": prompt
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/png;base64,{base64_image}"
                        }
                    }
                ]
            }
        ]
    )

    return response.choices[0].message.content

# Usage example
prompt = "Make this scene come alive, add more dynamic elements"
image_path = "/path/to/your/image.png"
result = generate_video_from_local_image(prompt, image_path)
print(result)

Automatic Video Download

import openai
import re
import requests
import os

client = openai.OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.laozhang.ai/v1"
)

def extract_video_url(content):
    """Extract video link from response"""
    match = re.search(r'https://[^\s\)]+\.mp4', content)
    return match.group(0) if match else None

def download_video(url, save_path):
    """Download video to local"""
    try:
        response = requests.get(url, stream=True)
        response.raise_for_status()

        with open(save_path, 'wb') as f:
            for chunk in response.iter_content(chunk_size=8192):
                f.write(chunk)

        print(f"Video saved to: {save_path}")
        return True
    except Exception as e:
        print(f"Download failed: {e}")
        return False

def generate_and_download(prompt, save_dir="./videos", model="sora_video2"):
    """Generate video and download automatically"""
    # Create save directory
    os.makedirs(save_dir, exist_ok=True)

    # Generate video
    print("Generating video...")
    response = client.chat.completions.create(
        model=model,
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": prompt
                    }
                ]
            }
        ]
    )

    content = response.choices[0].message.content
    print(content)

    # Extract video link
    video_url = extract_video_url(content)
    if not video_url:
        print("Video link not found")
        return None

    # Download video
    import time
    filename = f"sora_{int(time.time())}.mp4"
    save_path = os.path.join(save_dir, filename)

    if download_video(video_url, save_path):
        return save_path
    return None

# Usage example
prompt = "A cute cat playing with a ball in a sunny garden"
video_path = generate_and_download(prompt)
print(f"Video path: {video_path}")

JavaScript/Node.js Examples

Basic Text-to-Video

const OpenAI = require('openai');

const client = new OpenAI({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://api.laozhang.ai/v1'
});

async function generateVideo(prompt, model = 'sora_video2') {
  try {
    const response = await client.chat.completions.create({
      model: model,
      messages: [
        {
          role: 'user',
          content: [
            {
              type: 'text',
              text: prompt
            }
          ]
        }
      ]
    });

    return response.choices[0].message.content;
  } catch (error) {
    console.error('Error:', error);
    return null;
  }
}

// Usage example
generateVideo('A cute cat playing with a ball in a sunny garden')
  .then(result => console.log(result));

Streaming Output

const OpenAI = require('openai');

const client = new OpenAI({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://api.laozhang.ai/v1'
});

async function generateVideoStream(prompt, model = 'sora_video2') {
  const stream = await client.chat.completions.create({
    model: model,
    messages: [
      {
        role: 'user',
        content: [
          {
            type: 'text',
            text: prompt
          }
        ]
      }
    ],
    stream: true
  });

  let videoUrl = null;
  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content;
    if (content) {
      process.stdout.write(content);

      // Extract video link
      const match = content.match(/https:\/\/[^\s\)]+\.mp4/);
      if (match) {
        videoUrl = match[0];
      }
    }
  }

  console.log('\n');
  return videoUrl;
}

// Usage example
generateVideoStream('A cute cat playing with a ball in a sunny garden')
  .then(url => console.log(`\nVideo link: ${url}`));

Automatic Video Download

const OpenAI = require('openai');
const fs = require('fs');
const https = require('https');
const path = require('path');

const client = new OpenAI({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://api.laozhang.ai/v1'
});

function downloadVideo(url, savePath) {
  return new Promise((resolve, reject) => {
    const file = fs.createWriteStream(savePath);
    https.get(url, (response) => {
      response.pipe(file);
      file.on('finish', () => {
        file.close();
        console.log(`Video saved to: ${savePath}`);
        resolve(savePath);
      });
    }).on('error', (err) => {
      fs.unlink(savePath, () => {});
      console.error(`Download failed: ${err.message}`);
      reject(err);
    });
  });
}

async function generateAndDownload(prompt, saveDir = './videos', model = 'sora_video2') {
  // Create save directory
  if (!fs.existsSync(saveDir)) {
    fs.mkdirSync(saveDir, { recursive: true });
  }

  // Generate video
  console.log('Generating video...');
  const response = await client.chat.completions.create({
    model: model,
    messages: [
      {
        role: 'user',
        content: [
          {
            type: 'text',
            text: prompt
          }
        ]
      }
    ]
  });

  const content = response.choices[0].message.content;
  console.log(content);

  // Extract video link
  const match = content.match(/https:\/\/[^\s\)]+\.mp4/);
  if (!match) {
    console.log('Video link not found');
    return null;
  }

  const videoUrl = match[0];
  const filename = `sora_${Date.now()}.mp4`;
  const savePath = path.join(saveDir, filename);

  // Download video
  await downloadVideo(videoUrl, savePath);
  return savePath;
}

// Usage example
generateAndDownload('A cute cat playing with a ball in a sunny garden')
  .then(path => console.log(`Video path: ${path}`));

Cherry Studio Use Case

Cherry Studio is a powerful AI conversation client that supports Sora 2 video generation.

Configuration Steps

Configure API

Add laozhang.ai API configuration in Cherry StudioSee: Cherry Studio Configuration Documentation

Enable Video Feature

Find sora_video2 in model settings and enable video generation feature

Text-to-Video

Simply enter text description to generate video

Image-to-Video

Upload image + prompt to generate video

Text-to-Video

Directly enter in Cherry Studio dialogue box:

A cute orange cat napping in the sun, suddenly woken by a butterfly, starts chasing the butterfly

System will automatically:

Display prompt optimization results
Show generation progress (queue status, percentage)
Provide video playback link

Image-to-Video

Click upload image button and select reference image
Enter prompt:

Generate video: Make this scene come alive, add breeze blowing and leaves swaying effects

Batch Generation Examples

Python Batch Generation

import openai
import time
import concurrent.futures

client = openai.OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.laozhang.ai/v1"
)

def generate_single_video(prompt, model="sora_video2"):
    """Generate single video"""
    try:
        response = client.chat.completions.create(
            model=model,
            messages=[
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "text",
                            "text": prompt
                        }
                    ]
                }
            ]
        )
        return {
            "prompt": prompt,
            "result": response.choices[0].message.content,
            "success": True
        }
    except Exception as e:
        return {
            "prompt": prompt,
            "error": str(e),
            "success": False
        }

def batch_generate(prompts, max_workers=3, model="sora_video2"):
    """Batch generate videos"""
    results = []

    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
        futures = [
            executor.submit(generate_single_video, prompt, model)
            for prompt in prompts
        ]

        for future in concurrent.futures.as_completed(futures):
            result = future.result()
            results.append(result)

            if result['success']:
                print(f"✓ Success: {result['prompt'][:30]}...")
            else:
                print(f"✗ Failed: {result['prompt'][:30]}... - {result['error']}")

    return results

# Usage example
prompts = [
    "A cat playing with a ball in the garden",
    "Beach at sunset, waves gently lapping the shore",
    "Busy city street with heavy traffic",
    "Forest stream with sunlight filtering through leaves",
]

results = batch_generate(prompts, max_workers=2)

# Statistics
success_count = sum(1 for r in results if r['success'])
print(f"\nGeneration complete: Successful {success_count}/{len(prompts)}")

Best Practices

Prompt Optimization

Describe specific scenes and actions
Include details like lighting and atmosphere
Use authorized real person IDs like @sama
Avoid describing real human faces (will be rejected)

Error Handling

Set reasonable timeout (recommended 5 minutes)
Add retry logic
Record failure reasons
Use streaming output to monitor progress

Video Download

Download immediately after generation (valid for 1 day)
Use streaming download for large files
Save to local or cloud storage
Make backups

Cost Optimization

Both models have extremely high stability
Control concurrency when batch generating
No charge for failures, retry with confidence
Download videos promptly (storage validity 1 day)

Next Steps

FAQ

View frequently asked questions

Model Pricing

Learn detailed model comparison

API Reference

View complete API documentation

Quick Start

Back to quick start guide

Getting Started

Core API

Video Generation APIs

Image Generation APIs

AI Understanding

Usage Examples

Complete Python Examples

Basic Text-to-Video

Streaming Output Example

Image-to-Video (URL)

Image-to-Video (Base64)

Automatic Video Download

JavaScript/Node.js Examples

Basic Text-to-Video

Streaming Output

Automatic Video Download

Cherry Studio Use Case

Configuration Steps

Usage Examples

Text-to-Video

Image-to-Video

Batch Generation Examples

Python Batch Generation

Best Practices

Next Steps

FAQ

Model Pricing

API Reference

Quick Start

Getting Started

Core API

Video Generation APIs

Image Generation APIs

AI Understanding

​Complete Python Examples

​Basic Text-to-Video

​Streaming Output Example

​Image-to-Video (URL)

​Image-to-Video (Base64)

​Automatic Video Download

​JavaScript/Node.js Examples

​Basic Text-to-Video

​Streaming Output

​Automatic Video Download

​Cherry Studio Use Case

​Configuration Steps

​Usage Examples

​Text-to-Video

​Image-to-Video

​Batch Generation Examples

​Python Batch Generation

​Best Practices

​Next Steps

FAQ

Model Pricing

API Reference

Quick Start

Complete Python Examples

Basic Text-to-Video

Streaming Output Example

Image-to-Video (URL)

Image-to-Video (Base64)

Automatic Video Download

JavaScript/Node.js Examples

Basic Text-to-Video

Streaming Output

Automatic Video Download

Cherry Studio Use Case

Configuration Steps

Usage Examples

Text-to-Video

Image-to-Video

Batch Generation Examples

Python Batch Generation

Best Practices

Next Steps