Image Generation

curl https://router.requesty.ai/v1/images/generations \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_REQUESTY_API_KEY" \
  -d '{
    "model": "azure/openai/gpt-image-1",
    "prompt": "A sunset over mountains with vibrant orange and purple skies",
    "n": 1,
    "size": "1024x1024",
    "quality": "auto"
  }'

{
  "created": 1719000000,
  "data": [
    {
      "url": "https://..."
    }
  ]
}

{
  "created": 1719000000,
  "data": [
    {
      "b64_json": "/9j/4AAQSkZJRgABAQ..."
    }
  ]
}

curl https://router.requesty.ai/v1/images/edits \
  -H "Authorization: Bearer YOUR_REQUESTY_API_KEY" \
  -F "model=azure/openai/gpt-image-1" \
  -F "prompt=Make the sky a dramatic sunset" \
  -F "image[]=@./photo.png" \
  -F "size=1024x1024" \
  -F "quality=auto"

curl https://router.requesty.ai/v1/images/edits \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_REQUESTY_API_KEY" \
  -d '{
    "model": "azure/openai/gpt-image-1",
    "prompt": "Make the sky a dramatic sunset",
    "images": [
      { "image_url": "data:image/png;base64,iVBORw0KGgo..." }
    ],
    "size": "1024x1024"
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_REQUESTY_API_KEY",
    base_url="https://router.requesty.ai/v1",
)

with open("photo.png", "rb") as image_file:
    response = client.images.edit(
        model="azure/openai/gpt-image-1",
        prompt="Make the sky a dramatic sunset",
        image=image_file,
        size="1024x1024",
    )

print(response.data[0].url)

import OpenAI from 'openai';
import fs from 'fs';

const client = new OpenAI({
  apiKey: 'YOUR_REQUESTY_API_KEY',
  baseURL: 'https://router.requesty.ai/v1',
});

const response = await client.images.edit({
  model: 'azure/openai/gpt-image-1',
  prompt: 'Make the sky a dramatic sunset',
  image: fs.createReadStream('./photo.png'),
  size: '1024x1024',
});

console.log(response.data[0].url);

curl https://router.requesty.ai/v1/images/edits \
  -H "Authorization: Bearer YOUR_REQUESTY_API_KEY" \
  -F "model=azure/openai/gpt-image-1" \
  -F "prompt=Replace the background with a forest" \
  -F "image[]=@./portrait.png" \
  -F "mask=@./mask.png" \
  -F "size=1024x1024"

curl https://router.requesty.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_REQUESTY_API_KEY" \
  -d '{
    "model": "vertex/google/gemini-2.5-flash-image-preview",
    "messages": [
      {
        "role": "user",
        "content": "Generate an image of a sunset over mountains"
      }
    ],
    "image_config": {
      "aspect_ratio": "16:9",
      "image_size": "2K"
    }
  }'

{
  "model": "vertex/google/gemini-2.5-flash-image-preview",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "I've generated an image of a sunset over mountains as requested.",
        "images": [
          {
            "type": "image_url",
            "image_url": {
              "url": "data:image/png;base64,your_base64_image"
            }
          }
        ]
      }
    }
  ]
}

import base64
from io import BytesIO
from PIL import Image
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_REQUESTY_API_KEY",
    base_url="https://router.requesty.ai/v1",
)

response = client.chat.completions.create(
    model="vertex/google/gemini-2.5-flash-image-preview",
    messages=[
        {
            "role": "user",
            "content": "Generate a futuristic cityscape at night"
        }
    ],
    extra_body={
        "image_config": {
            "aspect_ratio": "16:9",
            "image_size": "2K"
        }
    }
)

# Extract the generated image
message = response.choices[0].message
if hasattr(message, 'images') and message.images:
    for i, image_data in enumerate(message.images):
        # Extract base64 data from data URL
        # Format: "data:image/png;base64,actual_base64_data"
        base64_str = image_data['image_url']['url'].split(',')[1]
        image_bytes = base64.b64decode(base64_str)
        
        # Open with PIL
        image = Image.open(BytesIO(image_bytes))
        
        # Save the image
        image.save(f'generated_image_{i}.png')
        print(f"Image saved as generated_image_{i}.png")

# Access the text response
print(message.content)

import OpenAI from 'openai';
import fs from 'fs';

const client = new OpenAI({
  apiKey: 'YOUR_REQUESTY_API_KEY',
  baseURL: 'https://router.requesty.ai/v1',
});

async function generateImage() {
  const response = await client.chat.completions.create({
    model: 'vertex/google/gemini-2.5-flash-image-preview',
    messages: [
      {
        role: 'user',
        content: 'Generate a serene landscape with a lake'
      }
    ],
    image_config: {
      aspect_ratio: '16:9',
      image_size: '2K'
    }
  });

  const message = response.choices[0].message;
  
  // Handle generated images
  if (message.images && message.images.length > 0) {
    message.images.forEach((imageData, index) => {
      // Extract base64 data from data URL
      // Format: "data:image/png;base64,actual_base64_data"
      const base64Data = imageData.image_url.url.split(',')[1];
      const imageBuffer = Buffer.from(base64Data, 'base64');
      
      // Save to file
      fs.writeFileSync(`generated_image_${index}.png`, imageBuffer);
      console.log(`Image saved as generated_image_${index}.png`);
    });
  }

  // Access the text response
  console.log(message.content);
}

generateImage();