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Overview

The NanoGPT API offers access to all the state-of-the-art image models. This guide covers how to use our image generation endpoint.

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Image Generation

Here’s a complete example using the Recraft model:

import requests
import json
import base64
from PIL import Image
import io

BASE_URL = "https://nano-gpt.com/api"
API_KEY = "YOUR_API_KEY"

headers = {
    "x-api-key": API_KEY,
    "Content-Type": "application/json"
}

def generate_image(prompt, model="recraft-v3", width=1024, height=1024):
    """
    Generate an image using the Recraft model.
    """
    data = {
        "prompt": prompt,
        "model": model,
        "width": width,
        "height": height,
        "negative_prompt": "blurry, bad quality, distorted, deformed",
        "nImages": 1,
        "num_steps": 30,
        "resolution": "1024x1024",
        "sampler_name": "DPM++ 2M Karras",
        "scale": 7.5
    }

    response = requests.post(
        f"{BASE_URL}/generate-image",
        headers=headers,
        json=data
    )

    if response.status_code != 200:
        raise Exception(f"Error: {response.status_code}")

    result = response.json()
    
    # Decode and save the image
    image_data = base64.b64decode(result['image'])
    image = Image.open(io.BytesIO(image_data))
    image.save("generated_image.png")
    
    return result

# Example usage
prompt = "A serene landscape with mountains and a lake at sunset, digital art style"
try:
    result = generate_image(prompt)
    print("Image generated successfully!")
    print("Cost:", result.get('cost', 'N/A'))
    print("Image saved as 'generated_image.png'")
except Exception as e:
    print(f"Error: {str(e)}")

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Parameters

The image generation API accepts the following parameters:

  • prompt (string): The text description of the image you want to generate
  • model (string): The model to use for generation
  • width (integer): Width of the generated image (default: 1024)
  • height (integer): Height of the generated image (default: 1024)
  • negative_prompt (string): Things to avoid in the generated image
  • nImages (integer): Number of images to generate (default: 1)
  • num_steps (integer): Number of denoising steps (default: 30)
  • resolution (string): Output resolution (default: “1024x1024”)
  • sampler_name (string): Sampling method (default: “DPM++ 2M Karras”)
  • scale (float): Guidance scale (default: 7.5)

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Best Practices

  1. Prompt Engineering

    • Be specific and detailed in your prompts
    • Include style references when needed
    • Use the negative prompt to avoid unwanted elements

  2. Image Quality

    • Higher resolution settings produce better quality but take longer
    • More steps generally mean better quality but slower generation
    • Adjust the guidance scale based on how closely you want to follow the prompt

  3. Cost Optimization

    • Start with lower resolution for testing
    • Use fewer steps during development
    • Generate one image at a time unless multiple variations are needed

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Error Handling

The API may return various error codes:

  • 400: Bad Request (invalid parameters)
  • 401: Unauthorized (invalid API key)
  • 429: Too Many Requests (rate limit exceeded)
  • 500: Internal Server Error

Always implement proper error handling in your applications:

try:
    result = generate_image(prompt)
except requests.exceptions.RequestException as e:
    if e.response:
        if e.response.status_code == 401:
            print("Invalid API key. Please check your credentials.")
        elif e.response.status_code == 429:
            print("Rate limit exceeded. Please wait before trying again.")
        else:
            print(f"API Error: {e.response.status_code}")
    else:
        print(f"Network Error: {str(e)}")