How AI, like the Craiyon tool, creates photorealistic images and art from text
Artificial intelligence is growing at a breakneck pace. Many practical applications for AI have been made to solve real-world problems. AI has even gotten smart enough to generate its own works of art; some tools available today are becoming more advanced and getting proficient at creating surprisingly realistic images.
Services like DALL-E 2, Craiyon, Parti, and Imagen have taken the world by storm; the internet's response to these crazy yet sometimes eerily realistic generations has been overwhelming. Have you ever seen a unicorn jumping on the moon? How about a brain flying in space on a rocket ship? The only limit to what can be created is your imagination.
As an AI company ourselves, we've been getting many questions about how AI image generation works, so we decided to answer some commonly asked questions about the subject.
What's the difference between the image generation platforms?
There are a lot of text-to-image generators online, and a few stand out as unique. Even Google's research team is working on two image generation projects simultaneously. All these projects use their own AI systems to generate these images.
Parti is a project by Google's Research team, abbreviated for Pathways Auto-Regression Text-to-Image model. This means it predicts tokens from text sequentially (like you would translate a sentence). So, it takes the text as input for a transformer decoder and tries to predict some tokens. A VQGAN further uses these tokens to create the image. The main idea behind Parti is that it reuses models using Google Pathways (out of scope).
Parti sets out to create "high-fidelity photorealistic image generation and supports content-rich synthesis involving complex compositions and world knowledge."
Imagen is another research project by Google, specifically Google Brain, that uses a cascaded diffusion model to create photorealistic images. The idea of diffusion models, in contrast to generative adversarial networks, or GANs, is that the tool does not predict the whole picture at once. Instead, it starts with noise and gradually tries to remove it. At every step of the process, Imagen predicts and eliminates the noise in the image.
The idea behind Imagen (also used in other models, like GLIDE) is that you add textual embedding to the current status of the image. This way, you can train the model to predict text-dependent images. Imagen is making this prediction of a base image on only a small resolution to get higher accuracy and is adding multiple super-resolution models at the end (by also passing the text embeddings to those models). This seems to create more realistic results with a higher resolution.
Imagen says it "builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation."
Google considers both research projects complementary and not competing since they use different approaches to generate "high-quality, photorealistic images from a single text prompt."
DALL-E 2 is a follow-up project of DALL-E. However, it is based on diffusion models (DALL-E was an autoregressive model based on GPT3). Between DALL-E and DALL-E 2, OpenAI also released GLIDE, another autoregressive model on which Imagen is heavily based. DALL-E 2 (like Imagen) predicts the image using a diffusion model and adding some encoding. However, in contrast to Imagen, it used CLIP encodings. CLIP is also a model from OpenAI which can find matchings between text and images. It only makes sense to use these encodings since we can assume that these encodings do contain the information from the image.
OpenAI's tool is not available to the public due to concerns about how the tool could be misused. And for a good reason, too. Just look at some of the images the tool generated on their website! Unlike Google's tools, anyone can apply and request access to it.
Craiyon, formerly called DALL-E Mini before OpenAI asked the team to rebrand the project, uses unfiltered scraped data and a simpler version of the DALL-E AI model, trained with fewer data, to generate images. The Craiyon web app is not affiliated with OpenAI and only resembles that project because it is designed similar, and on a much smaller scale, to DALL-E.
Craiyon isn't as sophisticated as the other tools mentioned here but is the standout of the group in that it's open-source and accessible for public use.
What's the difference between image generation and computer vision?
Computer Vision is a branch of science dealing with the perception, understanding, generation, and analysis of digital images. In contrast, Image Generation is a sub-class of Computer Vision focused on producing images using deep learning or other complex AI methods.
Both applications use a database of images with text descriptions as input to help them function.
Computer vision is one of our headline AI services. We know that sorting and classifying images can be repetitive and costly. Our AI algorithms reduce human error and bias by an accuracy percentage of 90% or more. Put simply, the benefit of using computer vision is improved efficiency, saving money on costs, and improving the accuracy, consistency, speed, and scalability of their image processing operations.
Additionally, there are many daily tasks that computer vision and AI can provide as valuable support to humans. By learning from all of your sample data, SoftwareOne can support you by utilizing AI to automatically:
- Perform quality inspection for manufacturing and production lines
- Analyze scenes and detect changes in remote sensing images
- Perform various digital file analyses, extract text or patterns, and classify those document
- Index, search, and retrieve media files automatically
- Anonymize the media for your privacy protection needs
How does an AI draw pictures?
David Mosen, Chief Data Scientist at SoftwareOne, explains it best. He says that most AI systems that generate images use neural networks, computer systems designed to mimic how brains work. They do so by making connections between different nodes, or neurons. It's pretty similar to the way that human brains generate images. Our brains take information from our eyes (or our memory) and combine it to form an image.
Neural networks work similarly. They combine information from a series of input nodes to form an output image. The difference is that, unlike our brains, neural networks can be trained to generate any picture we want - whether it's a realistic photo or a piece of abstract art.
More technically, each node represents a small piece of information, and the connections between the nodes determine how that information is processed. The more nodes and relationships between nodes, the more complex the system can be.
To create an image, an AI must learn about the world around us. This is done by feeding it a massive amount of data, such as images of different objects, people, landscapes, etc. The AI then analyzes this data and looks for patterns.
Once the AI has learned about the world, it can start to generate its own images. To do this, it creates a series of connections between them. The end result is an image that the AI itself creates!
Is there value in developing creative AI applications?
In addition to art, creative AI can contribute to cases where human creativity gets stuck in complexity or objectivity. AI creativity is already used in some fields, such as human face anonymization by creating unseen faces using GANS.
Some people believe that AI will become creative enough to replace human creativity, but we aren't convinced that will happen.
Is artificial intelligence intelligent or really just following a script?
Existing AI solutions typically follow a script, although sometimes hard to see when they are too deep and complex. However, we can ask the same question about human intelligence! Is our brain's neural network following a script engraved from environmental inputs and our perception over time in our life?
If SoftwareOne doesn't make AI image generators, what AI does SoftwareOne do?
SoftwareOne services are intended for enterprise clients instead of consumers. SoftwareOne takes a comprehensive approach to develop AI platforms and creating value for our clients through innovative and fresh ways to make business more accessible and streamlined.
Starting by auditing and discovering AI opportunities within the business using existing data and examining our client's core issue, we then validate the technical and business feasibility of using AI and develop a working prototype. Next, we implement the solution, continue developing the prototype, and deploy it for the company. The SoftwareOne team then manages the solution continuously for quality and effectiveness and iterates on it over time, providing lifetime value and improvements in business workflows and management.
This approach has helped companies worldwide improve how they do business. For example, SoftwareOne has developed finance solutions for debt collection, improving data quality and security, validation of results, and collaboration across different teams.
We did this by improving how collections agencies grouped debtors by behavior, forecasted cash flow (see image), engaged with debtors, optimized discounts, and structured bankruptcy prevention.
This is just one example of how SoftwareOne helps companies improve through the use of artificial intelligence. We have countless examples of practical AI use cases for businesses around the world, and our team of experts would be happy to speak with you about how AI could make a positive impact on your business.
By learning from all the existing sample data, SoftwareOne can support you in utilizing AI to automatically:
- Perform quality inspection for manufacturing and production lines
- Analyze scenes and detect changes in remote-sensing images
- Perform various digital file analysis, extract text or pattern, and classify those document
- Index, search, and retrieve media files automatically
- Anonymize the media for your privacy protection needs
Author
