# Image Generator Image generator is an artificial intelligence (AI)-based technology designed to automatically generate images. This technology uses machine learning algorithms, especially branches of deep learning such as neural networks, to understand visual data and generate new images. Image generators are often used in various fields such as art, design, animation, games, and even scientific research. ## How Image Generators Work 1. Training Model The model is trained using a large dataset containing various images and related information (e.g., text descriptions, labels, or metadata). The algorithm learns patterns, shapes, colors, and visual elements from these images. 2. Input Processing Text-to-Image: The model receives a text description, such as "a futuristic city at night," and then generates an image accordingly. Image-to-Image: The initial image is given, and the model modifies or adds elements based on instructions. Parameter-Controlled Generation: The user can set certain aspects, such as color, style, or size. 3. Output The model generates new images that look realistic or conform to a certain style, depending on the type of image generator and its training data. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://hazle-ai.gitbook.io/hazle-ai/welcome-to-hazle-ai/image-generator.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.