Batch Processing AI Images with Ollama and Python

Efficiently process large datasets with Ollama and Python for AI image applications.

Introduction to Ollama and Batch Processing

Ollama is a state-of-the-art AI model for generating images. It's a popular choice for developers and researchers due to its ability to produce high-quality images. However, working with large datasets can be challenging. That's where batch processing comes in – a technique for processing multiple items in a single, automated run.

PromptShot AI's Ollama API can help streamline this process, making it easier to create and manage large datasets.

The Benefits of Batch Processing with Ollama

Batch processing with Ollama offers several benefits, including:

• Increased efficiency: Process multiple images at once, saving time and reducing manual labor.
• Improved accuracy: Ollama's AI capabilities ensure high-quality images, reducing the need for manual editing.
• Scalability: Handle large datasets with ease, making batch processing ideal for large-scale applications.

Setting Up Ollama for Batch Processing

To get started with batch processing using Ollama and Python, follow these steps:

Step 1: Install the Required Libraries

pip install ollama requests

Step 2: Import the Necessary Libraries and Set Up the API

import requests
from ollama import Ollama

ollama = Ollama(
    api_key='YOUR_API_KEY',
    api_secret='YOUR_API_SECRET',
    url='https://api.promptshotai.com'
)

Step 3: Define the Batch Processing Function

def process_batch(
    images,
    prompt,
    width,
    height,
    num_iterations
):
    results = []
    for image in images:
        result = ollama.generate_image(
            prompt=prompt,
            width=width,
            height=height,
            num_iterations=num_iterations
        )
        results.append(result)
    return results

Example Use Cases

Here are a few example prompts to get you started:

Example 1: Generate a Portrait of a Celebrity

prompt =