How Are AI Image Generators Trained?
What Are AI Image Generators?
AI image generators are a class of large neural network models that can generate realistic images and art from simple text prompts. Leading examples of these foundation models include DALL-E, Stable Diffusion, and Midjourney.
These AI image generation models work by being trained on massive datasets to understand the relationship between language concepts and visual representations. After training, they can transform text prompts into images that match the prompt, allowing users to generate custom visual media.
AI image generators represent the cutting edge of generative artificial intelligence, showcasing abilities like creativity and imagination once thought impossible for machines. Their capabilities will only continue improving as models grow bigger and training techniques advance.
How Are AI Image Generators Trained?
Training an AI image generator is an intensive, multi-phase process requiring substantial compute resources and time. The key stages are:
The first step is compiling a massive training dataset, typically containing tens of millions of image-text pairs. These are gathered by scraping public sources like books and the internet.
The images cover a wide range of visual concepts, while the text captions describe the image content. This diverse data teaches the model to associate words and phrases with visual representations.
Before training begins, researchers test out different neural network architectures to find the optimal model design for image generation.
Key hyperparameters like number of layers and connections are tuned. Performance metrics like image quality and training efficiency determine the best model structure.
The chosen model architecture then undergoes extensive training on the massive image-text dataset. This is an iterative process where the model gradually improves at transforming text into corresponding images.
Training occurs on powerful GPUs and can take weeks or months of continuous processing to complete. The huge dataset is cycled through repeatedly to refine the model's parameters.
AI Training Techniques
Models like DALL-E, Stable Diffusion, Midjourney, and others are trained using combinations of the following techniques. Rather than assuming there is an advantage that makes a model like Midjourney better than DALL-E or vice versa, it’s better to think of it as each training technique being after slightly different results.
These aren’t the only techniques available for training generative image models, but they are among the most popular.
The models are trained on large labeled datasets of image-text pairs, and sometimes scraped image-caption data from the internet. Each image is paired with descriptive text captions that provide direct supervision for the model to learn the relationship between textual concepts and visual features.
The model is optimized through gradient descent techniques to minimize the error in generating images that match the paired text captions. This supervised training allows the model to ground textual concepts to visual instantiations and generate new images based on novel text prompts.
The models apply methods like autoencoders and self-supervised learning on large unlabeled image datasets.
Autoencoders allow the model to reconstruct images through an information bottleneck, teaching useful image feature representations.
Contrastive self-supervised learning maximizes agreement between differently augmented views of the same image using a contrastive loss, also teaching useful visual features. The unsupervised pre-training provides the model a strong visual abstraction capability prior to fine-tuning on paired image-text data.
The models are fine-tuned using reinforcement learning from human feedback. Humans rate generated images on scales of realism, relevance to the text prompt, aesthetic quality, and so on.
The model is rewarded for images rated highly by humans through policy gradient reinforcement learning. This provides direct feedback for the model to improve its text-to-image generation capability. Over time, the model learns to produce images that humans find highly realistic and relevant.
Most Popular Generative Image Models
Two major categories of models are used for AI image generation:
These generate images by iteratively denoising and refining random noise using a neural network. This process is guided by a series of diffusion steps, where each step gradually refines the image by adding small amounts of noise and adjusting the pixel values. The goal is to gradually transition from noise to the final target image in a controlled and coherent manner.
Some of the most popular models available today, such as Stable Diffusion and DALL-E use this approach.
Generative Adversarial Networks (GANs)
GANs train two neural nets against each other - one generates images from noise while the other evaluates realism.
In a nutshell, the generator network uses a random distribution to create an initial image and then responds to feedback from the discriminator until it can produce an image that the discriminator accepts as close enough to it’s target.
In summary, extensive training of neural networks on massive datasets enables AI image generators to transform language into realistic visual media. Mastering the use and training of generative models is an time-consuming process. But pays off in powerful generative capabilities. Advancements in model architecture, datasets and compute power will continue improving these AI systems.