Text to Image: Part 2 -- how image diffusion works in 5 minutes
People & Blogs
Introduction
In the continuation of our exploration of text-to-image generation, we delve into an impressive technique known as diffusion, which powers systems like Imagine and DALL·E 2. In the previous installment, we discussed how images can be represented as visual words and how a large language model could generate new images based on those representations, exemplified by Google’s Parti system. Another approach, like that used in Imagine, involves creating an image-generating program that modifies itself to operate based on language inputs.
How Does an Image Generator Work?
If asked to generate a hundred new images, one could simply choose a random value for each pixel, resulting in an array of noise patterns—similar to the static seen on a television tuned to an empty channel. However, generating a hundred specific images, like images of raspberries, poses a more significant challenge.
Conceptually, you can visualize two distributions: one for random images and another for raspberry images. Random images are straightforward to generate, while raspberry images require a different approach. Imagine having a “magical” way to gradually convert random images into raspberry images—that's where diffusion comes into play.
Diffusion is a method for transitioning from one distribution to another. Essentially, while converting random images to raspberry images is intricate, reversing the process is simpler. If we add increasing amounts of noise to a raspberry image, we eventually render it indistinguishable from random noise. This illustrates a pathway in image space where earlier frames depict steps towards randomness. By reversing this animation, we define a path leading from a random image to a raspberry image.
By applying this technique across a multitude of raspberry images, we can create multiple paths and train a neural network to predict their transformations. Once trained, this network can then assist in generating new raspberry images.
Conditioning the Network
Starting from a point representing a random image, when the diffusion neural network is fed that image, it outputs a direction for alteration. Continually stepping along this direction produces new images. The powerful aspect of this approach is that we can condition a single diffusion network to accept various inputs—say, the name of different fruits. When prompted with "apple," it generates a different vector compared to "mango."
To navigate complex descriptions, like a “raspberry beret,” a language model translates our phrases into a knowledge representation that guides the diffusion model. This is how Imagine operates.
Practical Examples
To illustrate, we can generate a few examples. Here are a couple of raspberry berets created using Imagine. For a playful twist, we can try “beret of raspberries,” resulting in a hat that’s made out of actual raspberries. Observing closely, we note how leaves from the raspberry plant are integrated into the designs.
Next, we explore an unusual concept: chocolate guacamole pancakes. They appear distinct from the authentic version described in a prior video. Lastly, consider a squirrel nestled in a nutshell, a callback from part one.
OpenAI’s DALL·E 2 is a popular text-to-image system that merges elements from these approaches. Released before Imagine, DALL·E 2 combines a visual word language model with a diffusion mechanism to decode visual words into images. Instead of organizing images in a grid of patches, DALL·E 2 encodes the image as a long vector known as a CLIP embedding, allowing for some exciting capabilities.
Observations and Challenges
However, these techniques can produce bizarre mistakes. Parti, for instance, might depict a live squirrel lounging in a latte, while Imagine could place an avocado unceremoniously on a bear’s nose rather than in pancakes. These models struggle with tasks such as counting and managing spatial relationships. For example, Parti may mistakenly swap left and right positions when interpreting prompts.
Moreover, biases in these systems can lead to inappropriate outputs for certain queries, presenting an ongoing challenge within advanced AI systems.
Conclusion
This article has provided insights into how diffusion works in image generation and how it interplays with language models to create fascinating and often imperfect illustrations based on textual input.
Keywords
- Text-to-image generation
- Diffusion
- Imagine
- DALL·E 2
- Visual words
- Random images
- Raspberry images
- Neural network
- Conditioning
- Bias
FAQ
What is diffusion in the context of image generation?
Diffusion is a method for transforming random images into target images, such as specific objects or scenes, by adding noise and reversing the transformation process.
How do neural networks aid in generating images?
Neural networks can be trained to predict transformations between random images and target images, enabling them to generate new images based on previously established paths.
What is the role of language models in defining image content?
Language models convert input text into knowledge representations that guide the diffusion model, enhancing the accuracy and relevance of the generated images.
Can these models accurately represent complex requests?
While they can handle various prompts, these models often struggle with precision in spatial relationships and may produce biased or incorrect results.
What are some examples of generated images?
Images such as "raspberry beret," "beret of raspberries," and "chocolate guacamole pancakes" illustrate the diverse outcomes possible with text-to-image generation systems.
