Unveiling the Mechanics of Image Generation AI

What is image generation AI? Its evolution and potential

Image generation AI is a field of artificial intelligence (AI) that automatically generates images that look as if they were drawn by a human, based on text or instructions. In recent years, this field has developed rapidly and is being used in a variety of fields, including art, design, and entertainment.

Image-generative AI not only stimulates our creativity and opens up new possibilities for expression, but also contributes to improving business efficiency and solving social issues. In this article, we will take a detailed look at the definition and overview of image-generative AI, as well as its history and evolution.

Definition and Overview of Image Generative AI

AI that generates images from text and instructions

Image generation AI is an AI system that understands text and instructions entered by humans and generates images based on them. For example, if you enter text such as “bouquet of red roses” or “futuristic cityscape,” the AI will generate an image that matches that image.

Image generation AI can create new images based on learned data, rather than simply combining existing images. This makes it possible to more specifically express the images that humans imagine.

Leveraging Machine Learning and Deep Learning

Image generation AI is realized by making full use of AI technologies called machine learning and deep learning.

• Machine learning: A technology that allows computers to learn from data and discover patterns and regularities. Image generation AI learns from large amounts of image data to understand the features and structure of images and acquire the knowledge to generate new images.
• Deep Learning: A type of machine learning that uses neural networks that mimic the neural circuits of the human brain to learn more complex patterns. Image generation AI can now generate high-quality, diverse images through deep learning.

Applications in creative and business fields

One of the attractions of image generation AI is its wide range of applications.

• Creative fields: Used in a variety of fields including art creation, illustration, design, game development, fashion, architecture, etc. Artists and designers can use image generation AI to gain new inspiration and shorten their production time.
• Business field: It is widely used in business scenes such as advertising creative, product design, marketing material creation, stock photography, etc. Image generation AI can support corporate marketing activities and create more effective visual content.
• Others: In the field of education, it is used to create teaching materials and support learning. In the medical field, it is used as a tool to assist in image diagnosis and deepen understanding of diseases.

History and evolution of image generation AI

Image generation AI has evolved significantly over many years of research and development. Here we look back at its history along with the emergence of key technologies.

Early Image Generation: Rule-Based and Template-Based

Early image generation AI generated images based on rules and templates defined in advance by humans. For example, face image generation AI defined the shapes and positions of facial features (eyes, nose, mouth, etc.) by rules and generated face images by combining them.

However, these systems had limited expressive power and were unable to generate a wide variety of images. In addition, creating rules and templates required specialized knowledge.

Introduction to Machine Learning: The Emergence of GANs (Generative Adversarial Networks)

GANs (Generative Adversarial Networks), introduced by Ian Goodfellow et al. in 2014, revolutionized the field of image generation AI. GANs are a learning mechanism in which two neural networks (a generative network and a discriminative network) compete with each other.

• Generative Networks: Attempts to create realistic looking fake images.
• Discrimination networks: Learn to distinguish between real and fake images.

By competing with each other, the generative network is able to create increasingly sophisticated fake images, eventually becoming capable of generating images that are indistinguishable from the real thing to the human eye.

With the advent of GANs, image generation AI has evolved dramatically, making it possible to generate higher quality and more diverse images.

The evolution of deep learning: VAE, Transformer, and diffusion models

Since the emergence of GANs, deep learning technology has evolved further, and various image generation models have been developed, such as VAE (Variational Autoencoder), Transformer, and Diffusion Model.

• VAE: A model that learns the latent features of data and generates new images from those features. VAEs are good at controlling the style and content of images, and are used for image editing and transformation.
• Transformer: Originally developed for natural language processing, it has recently been applied to image generation as well. Vision Transformer (ViT) is a model that applies Transformer to image recognition, and has achieved high performance on large-scale image datasets such as ImageNet.
• Diffusion model: A model that learns the process of restoring an original image from a noisy image. Diffusion models can not only generate high-quality and diverse images, but are also used in image super-resolution and noise removal.

Introducing large-scale models: Stable Diffusion, Midjourney, DALL-E 2

In recent years, the performance of image generation AI has been further improved with the emergence of large-scale image generation AI models such as Stable Diffusion, Midjourney, and DALL-E 2. These models are trained with huge amounts of image data and can generate high-quality and diverse images.

• Stable Diffusion: An open source image generation AI developed by Stability AI. It can not only generate high-quality images from text, but also edit and convert images.
• Midjourney: An image generation AI developed by Midjourney that can be used on Discord. It can generate beautiful images like works of art.
• DALL-E 2: An image generation AI developed by OpenAI. Not only can it generate high-quality images from text, but it can also edit and convert images.

How image generation AI works: A deep dive into key technologies

As the name suggests, image generation AI is a technology that uses artificial intelligence to generate images. Two major technologies, machine learning and deep learning, are deeply involved behind it. Here, we will explain in detail the basics of these technologies as well as the models and algorithms specific to image generation AI.

Machine Learning and Deep Learning Fundamentals

To understand image generation AI, you first need to understand the basic concepts of machine learning and deep learning.

Supervised Learning, Unsupervised Learning, and Reinforcement Learning: Their Role in Image Generation

Machine learning can be broadly divided into three types: supervised learning, unsupervised learning, and reinforcement learning. In image generation AI, unsupervised learning and reinforcement learning are mainly used.

• Unsupervised learning:
• A learning method in which AI itself finds features and patterns in data without being given labels (correct answers) for the data. Image generation AI learns features such as color, shape, and texture from large amounts of image data and combines them to generate new images.
• Example: VAE (Variational Autoencoder) and GAN (Generative Adversarial Networks) are typical examples of image generation models that use unsupervised learning.
  Reinforcement learning:
• This is a method in which AI learns to maximize rewards through repeated trial and error. In image generation AI, you can generate higher quality images by setting a reward function that evaluates the quality of the generated image and training the model to maximize that reward.
• Example: BigGAN, developed by DeepMind, is a model that uses reinforcement learning to generate high-quality images.

Neural Network Basics

At the core of deep learning is the neural network. This model mimics the neural circuits of the human brain and has a network structure in which a large number of nodes (neurons) are interconnected.

• Input layer, hidden layer, output layer:
  *A neural network consists of three layers: an input layer, a hidden layer, and an output layer.
• Input layer: A layer that receives external data, such as pixel values of an image.
• Hidden layer: A layer between the input layer and the output layer. By stacking multiple layers, it is possible to learn more complex features and patterns.
• Output layer: This layer outputs the final result (the generated image).
• Activation function (ReLU, Sigmoid function, etc.):
• Each node takes an input signal and converts it into an output signal through a nonlinear function called an activation function. Activation functions introduce nonlinearity into neural networks, allowing them to learn complex patterns.
• ReLU (Rectified Linear Unit): It has the characteristics of simple calculation and fast learning.
• Sigmoid function: It is suitable for expressing probabilities because the output value is between 0 and 1.
• Weights and biases:
• The connections between each node have parameters called “weights,” and by adjusting these weights, the neural network learns. A bias is a parameter that each node has, and it adjusts how easily the node is activated.
• By optimizing the weights and biases using training data, a neural network can generate appropriate outputs for given input data.

Loss functions and optimization

• Type of loss function for image generation (MSE, L1, SSIM, etc.):
• A loss function is an index that measures the error between the output of an AI model and the target value (teaching data). In image generation AI, various loss functions are used to evaluate the similarity between the generated image and the target image.
• Mean Squared Error (MSE): Calculates the mean squared error for each pixel. It is easy to calculate, but tends to produce blurry images.
• L1 loss (Mean Absolute Error, MAE): Calculates the average absolute error for each pixel. It tends to produce clearer images than MSE.
• SSIM (Structural Similarity Index Measure): An index for evaluating the structural similarity of images. It allows for evaluation close to human visual characteristics, but has the disadvantage of high computational costs.
• Optimization algorithms (Adam, SGD, etc.):
  *An optimization algorithm is an algorithm that updates the weights and biases of a neural network in order to minimize the value of a loss function.
• Adam (Adaptive Moment Estimation): It has the ability to automatically adjust the learning rate, enables stable learning, and is widely used.
• SGD (Stochastic Gradient Descent): Updates parameters using randomly selected data (mini-batches). It has low computational cost, but training may be unstable.

Image generation AI learns to generate high-quality images by appropriately combining these loss functions and optimization algorithms.

Core technology of image generation AI

In the course of its evolution, various technologies have been developed and refined in image generation AI. Here, we will explain in detail the mechanisms, characteristics, and challenges of the four main technologies that form the core of image generation AI: GAN (generative adversarial network), VAE (variational autoencoder), Transformer, and diffusion model.

GAN (Generative Adversarial Networks)

GAN is a model in which two neural networks, a generator and a discriminator, compete with each other to learn. Through this competitive learning process, the generator is able to generate fake images that are indistinguishable from the real thing.

• The role of generative and discriminative networks:
• Generative networks: Taking random noise as input, the goal is to generate lifelike images.
• Discriminative network: Attempts to distinguish between real images and fake images generated by a generative network.
• Learning process:

1. A generative network generates images from random noise.
2. A classification network tries to distinguish between real and generated images.
3. Based on the results of the discriminative network, the generative network adjusts its parameters and learns to generate more realistic images.
4. The discrimination network also learns to more accurately distinguish real from fake.

By repeating this cycle, the generative network becomes increasingly able to generate images that are more realistic, allowing the discriminative network to make more accurate judgments.

• Challenges (mode collapse, vanishing gradients, etc.):
• Mode Collapse: A phenomenon in which a generative network can only generate certain types of images.
• Vanishing gradient problem: This is a phenomenon in which backpropagation learning becomes less effective as the network becomes deeper.
• GAN evolution: DCGAN, StyleGAN, BigGAN, etc.:
• DCGAN (Deep Convolutional GAN): By introducing a convolutional neural network (CNN) into GAN, we have achieved stable learning and high-quality image generation.
• StyleGAN: You can now have finer control over the style of your images (e.g. facial expression, hairstyle, age, etc.).
• BigGAN: By using large-scale models and datasets, it is now possible to generate diverse images with high resolution.

VAE (Variational Autoencoder)

VAE is a model that learns the latent features of data and generates new data from those features. VAE can compress high-dimensional data such as images and audio into a low-dimensional latent space and generate new data from that latent space.

• The role of latent space and encoder-decoder:
• Latent space: A low-dimensional space that represents the features of data. VAE maps input data into this latent space and generates new data from that information.
• Encoder: A neural network that compresses input data into a latent space.
• Decoder: A neural network that generates new data from a latent space.
• Advantages and disadvantages of VAE:
• Advantages: By probabilistically modeling the data generation process, we can generate diverse data. Also, by manipulating the latent space, we can control the style and content of the images.
• Disadvantages The quality of the generated images may be inferior to GANs, and the latent space is difficult to interpret.

Transformer

Transformer is a model originally developed for natural language processing, but in recent years it has also been applied to image generation. Transformer uses a self-attention mechanism to capture the relationships between elements of input data.

• Mechanism of attention and its application to image generation:
• Self-Attention: A mechanism where each element of the input data (e.g. a word in a sentence or a pixel in an image) calculates how much attention it should receive relative to all other elements, allowing processing to take context and global structure into account.
• Application to image generation: Vision Transformer (ViT) is a model that applies Transformer to image recognition. ViT divides an image into small regions called patches and processes each patch with self-attention, allowing it to efficiently capture the information of the entire image.
• Vision Transformer (ViT) Overview and Features:
• ViT has achieved high performance on large-scale image datasets such as ImageNet, and has been applied to various image recognition tasks, including image classification, object detection, and segmentation.
• ViT does not have a recursive structure like an RNN, so it is capable of parallel processing and has the advantage of being highly efficient at learning.

Diffusion model

The diffusion model is a model that learns the process of restoring an original image from a noisy image. Through this learning process, it can understand the structure and features of the image and generate high-quality images.

• Training the noise removal process and image generation:
• The diffusion model first adds noise to an image. Then the model is trained to predict the image before the noise is added. Through this training process, the model learns the structure and features of the image and is able to remove the noise.
  *Once trained, the model can generate new images by starting with random noise and gradually removing the noise.
• Stable Diffusion, DALL-E 2 use:
• Stable Diffusion and DALL-E 2 are image generation AIs based on the diffusion model. These models are attracting attention because they can generate high-quality and diverse images.
• Stable Diffusion is open source and available for anyone to use. DALL-E 2 can be used through APIs provided by OpenAI.

Technical details of key image generation AI models

Here, we will focus on two of the most popular image generation AI models, Stable Diffusion and Midjourney, and dig deep into the technical details of each model.

Stable Diffusion

Stable Diffusion is an image generation AI model announced by Stability AI in 2022. It is based on a deep learning model called the Diffusion Model and can generate high-quality images from text input. Because it is released as open source, researchers and developers around the world have been using the technology to create various derivative models and applications.

Digging deeper into diffusion models

The diffusion model generates images by learning the process of adding noise to an image (forward diffusion process) and the process of removing noise (reverse diffusion process).

• Noise Schedule: The noise schedule defines how noise is added to an image in the Forward Diffusion Process. Linear and cosine noise schedules are available in Stable Diffusion.
• Sampling Method: The sampling method determines the algorithm used to remove noise in the Reverse Diffusion Process. Stable Diffusion offers sampling methods such as DDIM (Denoising Diffusion Implicit Models) and PLMS (Pseudo Linear Multistep).
• U-Net Architecture: U-Net is a neural network architecture that is often used in image segmentation tasks. Stable Diffusion uses a model based on U-Net, which can efficiently learn image features during the denoising process.

Integration with text encoder CLIP

Stable Diffusion can generate images from text input by working with a text encoder called CLIP (Contrastive Language-Image Pre-training) developed by OpenAI. CLIP has learned a large number of image-text pairs and can understand the semantic relationship between text and images.

In Stable Diffusion, we use CLIP to convert text input into vectors, and then feed the vectors into U-Net to generate an image that corresponds to the text.

Interpreting the prompt and the image generation process

The image generation process in Stable Diffusion is as follows:

1. Enter Prompt: The user enters a text prompt.
2. Encode the prompt: Use CLIP to convert the prompt into a vector.
3. Noise Generation: Generates random noise.
4. Denoising: Use U-Net to generate an image from noise, with the prompt vector information also used as input.
5. Image Output: Output the generated image.

Parameter adjustment and customization

Stable Diffusion has a variety of parameters that can be adjusted to give you detailed control over the style and quality of the generated image.

• CFG Scale (Classifier Free Guidance Scale): Adjusts the fidelity to the prompts. Higher values will produce images that are more faithful to the prompts.
• Sampling steps: Adjust the number of steps for noise reduction. More steps will produce a higher quality image, but it will take longer to generate.
• Seed: Specifies the random number seed. Setting the same seed value will generate the same image for the same prompt.

In addition, because Stable Diffusion is open source, you can freely customize the model structure, training data, etc. This makes it possible to create image generation AI specialized for specific styles or tasks.

Midjourney

Midjourney is an image generation AI developed by Midjourney Inc. and can be used as a chatbot on Discord. It uses a unique algorithm, the details of which have not been made public, but it is known for generating high-quality, artistic images.

Details of our unique algorithm

Midjourney’s algorithm is presumably a combination of existing technologies such as GAN and VAE, but no detailed information has been released. However, the images generated by Midjourney have a unique style that differs from other image generation AI, and it is believed that some kind of ingenuity has been put into the algorithm.

Collaboration with the Discord community

Midjourney is available as a chatbot on Discord, allowing users to share prompts with other users and get feedback through the Discord community, which has been a big part of Midjourney’s popularity.

Prompt writing and techniques

Midjourney allows you to control the style and content of the generated image by adding certain keywords and parameters to the prompt. For example, adding the parameter “–ar 16:9” will generate an image with a 16:9 aspect ratio.

The Midjourney community has shared various prompt writing methods and techniques, and by using this information you can generate higher quality, more purposeful images.

DALL-E 2

DALL-E 2 is an image generation AI model developed by OpenAI and was announced in April 2022 as an evolution of its predecessor, DALL-E. DALL-E 2 is not only capable of generating high-quality and diverse images from text input, but also has a variety of functions such as editing and converting images and generating variations.

Diffusion model improvements

DALL-E 2 is based on the diffusion model but with some improvements.

• Collaboration with CLIP (Contrastive Language-Image Pre-training): DALL-E 2 generates images from text input by collaborating with a model called CLIP, which has been trained on a large number of image-text pairs. CLIP can understand the semantic relationship between text and images, enabling more accurate and high-quality image generation.
• Hierarchical latent variable model: DALL-E 2 employs a hierarchical latent variable model, which can learn the overall structure and detailed information of an image separately, making it possible to generate more complex and diverse images.
• High Resolution Image Generation: DALL-E 2 can generate high resolution (1024×1024 pixels) images, which allows for more detailed image representation.

Collaboration with CLIP

DALL-E 2 works with CLIP to generate images from text input. CLIP has learned a large number of text-image pairs and can understand the semantic relationships between text and images.

DALL-E 2 uses CLIP to convert text input into vectors, and then inputs the vectors into an image generation model to generate images that correspond to the text. By working with CLIP, DALL-E 2 can generate high-quality images even from text that contains abstract concepts or complex instructions.

Image editing technology

DALL-E 2 provides a variety of functions for editing and converting images.

• Inpainting: A feature that allows you to specify a part of an image and fill in that part naturally. For example, you can erase a person’s face or replace the background with a different landscape.
• Outpainting: This feature extends an image by adding new area around it.
• Variations: A feature that generates various variations based on an existing image.

These functions are realized by combining CLIP and image generation models. For example, in Inpainting, the surrounding information of the masked area is analyzed by CLIP, and the image generation model fills in the missing parts based on that information.

Safeguards and ethical considerations

DALL-E 2 has a safeguard function to prevent the generation of harmful content. For example, violent images, sexual images, and images containing hate speech are restricted from being generated.

OpenAI has also developed and is asking users to adhere to ethical guidelines for the use of DALL-E 2. These guidelines are intended to promote the responsible use of AI and to prevent it from having a negative impact on society.

How to evaluate image generation AI

In order to evaluate the performance of image generation AI, it is important to combine quantitative indicators and qualitative evaluations. Here, we will explain each evaluation method.

Quantitative evaluation

Quantitative evaluation is a method of objectively evaluating the performance of image generation AI using quantified indicators.

Inception Score (IS), Fréchet Inception Distance (FID)

• Inception Score (IS): An index that evaluates the quality and diversity of generated images. The higher the IS, the higher the quality and diversity of the generated images.
• Fréchet Inception Distance (FID): A metric that measures the similarity between the distribution of generated and real images. The lower the FID, the closer the generated image is to the real image.

Precision, Recall, F1 Score

These metrics are used to evaluate the performance of AI models in tasks such as object detection and segmentation.

• Precision: The percentage of correctly detected objects.
• Recall: The percentage of correctly detected objects out of the actual objects present.
• F1 score: The harmonic mean of Precision and Recall, which represents the overall performance of the model.

CLIP score

The CLIP score is an index that measures the similarity between a generated image and text using the CLIP model developed by OpenAI. The higher the CLIP score, the more faithful the generated image is to the text instructions.

Qualitative evaluation

Qualitative evaluation is a subjective evaluation by human reviewers. It allows you to evaluate things like the beauty and creativity of an image, which cannot be captured by quantitative evaluation.

Human Rating

• Survey: The generated images are evaluated by the evaluator in the form of answering questions.
• Rating: The generated images are rated by evaluators in the form of a score.
• Comparative Evaluation: Multiple images are compared and the evaluator selects which image is the best.

Expert Review

Evaluation by art and design experts is important in assessing the creativity and artistic merit of image-generating AI. Experts will take into account not only technical aspects, but also aesthetic sensibility and expressiveness.

Summary: Understanding how image generation AI works and maximizing its potential

Image generation AI has made remarkable progress in recent years thanks to the evolution of machine learning and deep learning. The fusion of various technologies such as GAN, VAE, Transformer, and diffusion models has made it possible to perform a wide range of tasks, such as generating high-quality images from text and editing and converting existing images.

Major image generation AI models such as Stable Diffusion, Midjourney, and DALL-E 2 each have different algorithms and features and are used in a variety of fields. These models are opening up new possibilities not only in creative fields such as art, design, advertising, and games, but also in a wide range of fields such as medicine, education, and business.

However, the advancement of image generation AI has also highlighted several challenges. In addition to technical challenges such as the difficulty of generating high-quality images, balancing diversity and controllability, and reducing computational costs, there are also ethical challenges such as copyright infringement, the generation and misuse of fake images, and bias and discrimination.

To solve these issues, not only technological research and development but also discussion and legal reforms in society as a whole are necessary. For example, there are many debates on various issues, such as whether to allow copyright for images generated by AI, how to regulate fake images, and how to mitigate AI bias.

Image generation AI is still a developing technology, but its possibilities are endless. It will be possible to generate more diverse images with greater accuracy, and new technological innovations are expected, such as real-time generation, 3D image generation, and integration with video generation.

By each of us understanding how image-generating AI works and recognizing its potential and challenges, we can use this revolutionary technology more safely and effectively, helping to create a more prosperous society.