Artificial Intelligence (AI) image generators have been gaining in popularity over the past few years. These AI tools can create a range of images and visual effects, from stills to animations. Examples include DALL-E, Stable Diffusion and Midjourney.
In this article, we will explore how these AI image generators work and the implications for design.
We will look at how each of these tools generate visuals, as well as their advantages and limitations.
How do AI image generators work?
AI image generators work by using deep learning algorithms to learn the patterns and features present in a large dataset of images.
These algorithms are typically based on neural networks, which are designed to simulate the behavior of the human brain.
The process of creating an image using an AI image generator typically involves the following steps:
- Training the model: The AI image generator is trained using a large dataset of images. During training, the model learns to identify patterns and features in the images, such as lines, shapes, and colors.
- Generating new images: Once the model has been trained, it can be used to generate new images. This is done by providing the model with a set of inputs, which can be random noise or other images. The model then uses its learned patterns and features to generate a new image that is similar to the input images.
- Refining the images: The generated images are often refined using additional algorithms to improve their quality and realism. This can include techniques such as image filtering, noise reduction, and color correction.
There are many different types of AI image generators, each with its own strengths and weaknesses.
Some generators are optimized for creating realistic images, while others are designed for creating more abstract or stylized images.
Additionally, some generators can be trained to generate images in specific styles or genres, such as portraits, landscapes, or still life.
Early AI image generators relied on Generative Adversarial Networks (GANs) but newer technologies are pushing towards stable diffusion models.
What are Generative Adversarial Networks?
A Generative Adversarial Network (GAN) is a type of artificial neural network that is used for generative modeling, which involves generating new data samples that are similar to a given dataset.
The GAN consists of two neural networks that are trained in a game-like fashion: a generator network and a discriminator network.
The generator network takes random noise as input and generates new data samples that are intended to be similar to the training data. The discriminator network takes both real and generated data samples as input and tries to distinguish between them.
The generator and discriminator networks are trained in an adversarial manner: the generator network tries to generate data samples that fool the discriminator network, while the discriminator network tries to correctly distinguish between real and generated data samples.
As the generator and discriminator networks compete against each other, the generator network gradually learns to produce data samples that are increasingly similar to the training data.
Over time, the discriminator network becomes more accurate at distinguishing between real and generated data samples, which encourages the generator network to produce better samples.
GANs have shown impressive results in generating realistic images, audio, and text, and have potential applications in a wide range of fields, including art, design, and medicine.
However, training GANs can be challenging, and requires careful tuning of hyperparameters and network architectures to prevent issues such as mode collapse, where the generator network produces limited variations of a few samples.
Pros and cons of GANs
Pros:
- Can produce very realistic images, especially with high-quality training data and architecture.
- Can be used for a wide range of applications, including image and video generation, image editing, and image-to-image translation.
- Training is relatively fast compared to other generative models.
Cons:
- Can be unstable and difficult to train, with challenges like mode collapse, where the generator produces limited variations of a few samples.
- Can produce biased results, as it may learn to reproduce biases present in the training data.
- May generate artifacts or distortions in the output.
What’s the stable diffusion model?
Stable diffusion works by applying a set of transformations to a noise vector in order to generate an image.
These transformations are applied iteratively over a series of time steps, during which noise is gradually added to the image to make it more complex and realistic.
The key to the Stable Diffusion algorithm is the use of a diffusion process that ensures that the generated images are of high quality and are diverse.
In physics, diffusion describes the gradual spread of particles in a fluid. In the context of generative modeling, the diffusion process is used to gradually generate an image by adding noise to it over a series of time steps.
At each time step, the noise vector is transformed by a function that is defined by a neural network.
The transformed noise is then mixed with the current image, and the resulting mixture is passed through another neural network that predicts a mask that determines how much of the current image should be retained and how much should be replaced with the mixture.
The process is repeated over multiple time steps, with noise being added at each step to gradually generate a complex and realistic image.
Pros and cons of stable diffusion
Pros:
- Can produce high-quality and diverse images, as the diffusion process used in Stable Diffusion ensures that the generated images are of high quality and are diverse.
- Does not rely on adversarial training, which can avoid some of the issues of GANs, such as mode collapse and bias amplification.
- Can produce consistent results over time.
Cons:
- Can be computationally expensive to train and generate images, as it involves a large number of time steps.
- May generate blurry images, especially when generating small or low-resolution images.
- May require larger amounts of training data to produce high-quality results.
Summary of GANs vs stable diffusion
In summary, GANs and Stable Diffusion each have their own strengths and weaknesses, and choosing the right model depends on the specific requirements of the task at hand.
GANs may be more suitable for tasks where realism is a top priority, while Stable Diffusion may be more suitable for tasks where diversity and consistency are important.
DALL-E
What is DALL-E?
DALL-E is a neural network-based artificial intelligence program developed by OpenAI that is capable of generating images from textual descriptions.
The name “DALL-E” is a combination of the artist Salvador Dalí and the character EVE from the movie WALL-E.
DALL-E uses a combination of computer vision and natural language processing to understand the text input and generate images accordingly.
DALL-E is a generative model that can create a wide variety of images, including animals, objects, and even abstract concepts.
It works by using a transformer-based architecture, similar to that used in OpenAI’s GPT-3 language model, but adapted for image generation.
DALL-E was trained on a massive dataset of images and their corresponding textual descriptions.
During the training process, the program learned to generate images that match the input descriptions.
Since its release in January 2021, DALL-E has gained a lot of attention for its impressive ability to generate highly detailed and imaginative images from textual prompts.
How does DALL-E work?
When a textual description is inputted into DALL-E, it first goes through a natural language processing model that breaks down the text into individual tokens and extracts the important features.
These features are then passed through a transformer-based neural network that generates a sequence of numbers, which are interpreted as parameters for the image generation process.
The generated parameters are then passed to a separate image generation network, which converts the parameters into an image via GANs.
During training, DALL-E was fed a massive dataset of image-text pairs and was trained to generate images that matched the input descriptions.
This process allowed the program to learn how to generate a wide range of images, including objects, animals, and even abstract concepts.
Overall, DALL-E’s ability to generate highly detailed and imaginative images from textual prompts has shown great potential for future applications in a wide range of industries, including art, design, and advertising.
Stable Diffusion
What is Stable Diffusion?
Stable Diffusion is a method of image generation developed by the CompVis group at LMU Munich. It works by combining two separate processes: deep learning and generative adversarial networks (GANs).
Deep learning involves teaching machines to recognize patterns in data, while GANs are used to create new images based on existing ones.
It is based on the idea of a diffusion process, which is a mathematical concept that describes the gradual spread of particles in a fluid. In the context of generative modeling, the diffusion process is used to gradually generate an image by adding noise to it over a series of time steps.
How does Stable Diffusion work?
Stable Diffusion differs from other generative models in that it does not rely on adversarial training, where a generator network is trained to fool a discriminator network. Instead, it uses a diffusion process to generate images that are diverse and high quality.
Stable Diffusion has shown impressive results in generating high-quality images, and it has potential applications in a wide range of fields, including art, design, and medicine.
Midjourney
What is Midjourney?
Midjourney was created by a team led by David Holz, to generate realistic images from text. It’s speculated that Midjourney uses a similar process to Stable Diffusion.
How does Midjourney work?
It’s not in the public domain how Midjourney works.
What are the current hurdles faced by AI image generators?
AI image generators have made significant progress in recent years, but there are still several hurdles that need to be overcome to improve their quality and reliability.
Researchers are actively working to address these challenges through new algorithms, improved training techniques, and larger and more diverse datasets.
By overcoming these hurdles, AI image generators could become even more powerful tools for a wide range of applications, including art, design, and medicine.
Some of the current hurdles faced by AI image generators are:
Diversity
AI image generators can produce images that are visually impressive, but they often lack diversity. Many image generators produce images that are similar to the training data, leading to a lack of variety in the generated images.
Realism
While some AI image generators produce images that are visually impressive, they still lack the realism and complexity of real-world images. For example, generated images may lack fine details or subtle variations in lighting and color that are present in real-world images.
Consistency
AI image generators can produce inconsistent results, even when generating images of the same object or scene. This inconsistency can make it difficult to use AI image generators for practical applications, such as in computer vision.
Data Bias
AI image generators can perpetuate and even amplify biases present in the training data. For example, if the training data contains biased representations of certain groups of people, the generated images may also contain those biases.
Resource Intensive
AI image generators require significant computational resources, including large amounts of training data and powerful hardware. This can make it difficult for smaller organizations or individuals to develop and use AI image generators.
English bloke in Bangkok. First used GPT-3 in 2020 and has generated millions of words with it since. Not really much of an achievement but at least it demonstrates a smidgen of authority. Studies natural language processing, Python and Thai in his spare time.
