Diffusion AI Models, the Visual Revolution: How Image Generation works

Introduction

They are a type of generative model in artificial intelligence that generates content, such as images, through a gradual transformation process:

• This process starts with a random input (noise) and refines that input little by little until it obtains a coherent and detailed result.
• In recent years, diffusion models have become popular due to their ability to generate high-quality images and have been implemented in tools such as DALL-E 2 and Stable Diffusion.

How do Diffusion Models work? The operation of diffusion models is based on two main phases:

Main Components:

1.- Input Text:

• Description: The text that is entered into the model, which is generally an instruction or detailed description of the image that you want to generate.
• Function: Acts as the guide or reference that the model will use to generate the image.

2.- Token Embeddings:

• Description: Vector representations of the words or tokens in the input text.
• Function: These embeddings capture the semantic meaning of each word and the contexts in which they are used, allowing the model to understand and process the text more effectively.

3.- Image Tensor:

• Description: A multidimensional representation (tensor) of the image in the model data space. Initially, it may be a noisy generated image that is progressively refined.
• Function: Serves as the structure upon which the diffusion model iteratively works to remove noise and create a coherent and detailed image.

4.- Generated Image:

• Description: The final image resulting from the inverse diffusion process that has progressively eliminated the noise from the initial tensor image.
• Function: The visual output that matches the description of the input text, representing the culmination of the image generation process.

These components work together in an integrated manner to transform a textual description into a detailed and accurate visual image, using the principles of diffusion models.

Training of Diffusion Models:

Training a diffusion model involves teaching the model how to add noise to the data and then how to reverse that process. To do this, the model is trained on a data set of real images and learns to approximate each step of the degradation and generation process. Thus, the model learns the characteristics of the original images and how to reconstruct them from the noise.

Example How to design an image with AI from Text

Leonardo AI is an artificial intelligence platform designed to generate images from text.

• Uses neural networks and advanced deep learning techniques to create high-quality, realistic images.
• The platform is easy to use and does not require prior design or programming knowledge.

Main features:

• Realistic image generation: Leonardo AI uses a large amount of data to create images that look like real ones.
• Easy to use: The interface is intuitive and easy to navigate, allowing users to create images without complications.
• Preset models: The tool has a variety of preset models to generate images of landscapes, characters, objects and animals in a matter of seconds.
• Customization: Users can customize the generated images by adjusting parameters such as size, resolution and style.
• Pricing: Leonardo AI offers a free daily quota of 150 credits, and it is possible to purchase additional credits if more images are needed.

Example:

Step 1: Click start:

Step 2: Create image:

Step 3:

• Describe the image you want to generate: “A tiger in a tropical jungle”
• Click generate.

Step 4: The generated images are obtained:

Step 5: Download and Edit - The generated images can be downloaded, cropped, edited or resized as needed.

Websites that use Diffusion Models to generate images and art:

Glideapps.com by OpenAI: Uses diffusion models for generating, editing and modifying images from text.

Palette Generator by Google: Broadcast applications for colorizing images, filling pixels, and restoring images.

Askaichat.app by Midjourney: Platform that allows you to generate impressive visual art from textual descriptions using diffusion models.

Examples of Diffusion Model Applications:

• Image generation: Models such as Stable Diffusion and DALL-E 2 can create detailed images from text descriptions, from photographs of realistic objects to artistic images.
• Image Restoration: Improve low-quality images or remove noise from old or deteriorated images, accurately restoring details.
• Converting sketches into detailed images: Transform simple sketches into complete images, adding colors, textures and details.
• Video: There is research on diffusion models applied to video generation, although this area is still developing and presents greater challenges.