What is DALL-E?
DALL-E (stylized as DALL.E) is a generative artificial intelligence (AI) tool that lets users create realistic images and art from text prompts given in natural language. OpenAI launched it to the public in January 2021.
DALL-E is a variation of the language model called a generative pre-trained transformer (GPT) that powers GPT-3 and ChatGPT. But DALL-E is specifically designed for image generation. It uses a smaller version of GPT-3 and is trained on text-image pairs taken from the internet to create original art on its own in any style.
The name DALL-E is a combination of the names of the Spanish surrealist artist Salvador Dali and the Pixar movie about an eco-friendly robot, WALL-E.
DALL-E image generator and its successor DALL-E 2 released in 2022, is part of synthetic media software. Synthetic media tools are generative AI technology that creates images, text, and videos based on prompts. Text-to-image generators before DALL-E had not shown the level of accuracy or control in drawing multiple objects or the spatial reasoning abilities of DALL-E, making it a game changer in the field.
DALL-E’s competitors include Midjourney, Stable Diffusion, and DALL -E Mini, an open-source AI art generator.
Technology components of DALL-E
For users, the working of DALL-E looks simple: Enter a prompt and hit “generate.” But behind the scenes, DALL-E uses a number of AI technologies together. This includes:
- GPT-3: GPT-3 is a large language model that uses natural language processing and natural language generation to create text. DALL-E uses a subset of GPT-3 architecture. It utilizes 12 billion parameters that are optimized for image generation out of the 175-billion+ parameters that GPT-3 has.
- Contrastive language-image pre-training (CLIP): CLIP is an artificial neural network trained on 400 million pairs of images with text captions from the internet. It predicts the most relevant text snippet for a given image. CLIP analysis and ranks DALL-E’s umpteen outputs to select the most suitable image for a prompt.
- Discrete variational autoencoder (dVAE): dVAE is a neural network for unsupervised learning that uses an encoder and decoder to compress and transform an input into a desired format of the output. In DALL-E, dVAE is used to decode text to an image.
How DALL-E Works
Using the above-mentioned technologies, here’s how DALL-E works:
- Encoding: When a user gives a prompt, DALL-E understands the text using the GPT-3. It encodes the text into tokens that capture the semantic meaning and context of the input.
- Decoding: dVAE then generates image output for the encoded text based on patterns from its training datasets.
- Refinement: The image output is refined in multiple steps by adding more details and complexity, resulting in a final high-quality image.
DALL-E generates unique images through this iterative encoding, decoding, and refining process.
DALL-E applications
As an AI image generator, DALL-E has a wide range of potential applications in different fields. Some notable use cases are:
- Creative inspiration: The model provides artists, designers, and content creators a tool to quickly generate visuals for creative purposes, such as artwork, illustrations, or design elements. It can be a tool for quick inspiration, or it can supplement the existing creative process.
- Concept visualization: DALL-E aids in visualizing abstract and complex concepts. It generates images of ideas, scenarios, or objects that are challenging to depict directly.
- Product design and prototyping: DALL-E assists in the early stages of product design by generating visual representations of potential designs based on text descriptions. Unlike traditional computer-aided design (CAD) technologies, designers can quickly explore different product concepts before going for a physical prototype.
- Advertising and marketing: Marketers can use DALL-E to create and tailor visually compelling imagery for advertising campaigns, product promotions, or branding purposes.
- Publications, media, and content creation: DALL-E easily creates illustrations, graphics, and imagery that can be used in books, magazines, blogs, and other media publications. It can even be used to create visual aids and educational materials.
- Entertainment, media, and gaming: The DALL-E image generator can create visuals that goes beyond the usual computer-generated imagery (CGI) for games, animations, movies, virtual reality (VR), and augmented reality (AR) experiences.
- Fashion: It’s a useful tool for designers to brainstorm and generate hundreds of fashion costumes in different styles and colors.
- Art: Anyone, who is not familiar with painting or art, can create their own AI-generated art using DALL-E.
How to use DALL-E and DALL-E 2
Follow these steps to use OpenAI’s AI image generators and create AI images:
- Go to OpenAI's website and sign up for an account using an email address. Users with accounts in Google, Microsoft, or Apple can use the respective option and create their OpenAI account.
- Alternatively, users can navigate to OpenAI’s product page like DALL-E and DALL-E 2, and sign up from that page. Note: users need to verify their email address and their phone number for a one-time verification as part of the signup process.
- Once an OpenAI account has been created, users can explore any of the OpenAI’s products like DALL-E, and ChatGPT.
- In DALL-E, users get a screen with a tab for entering a prompt and a “generate” button. Enter a text prompt and click on “generate”.
It should be noted that DALL-E operates on a credit system to measure usage. Each text-to-image request needs a credit that should be bought from OpenAI. Users who signed up for DALL-E before April 6 2023, however, get free credits on a monthly basis as early adopters.
Benefits of DALL-E
DALL-E offers multiple advantages as an AI art generator. It provides a good solution whenever creative visuals are to be generated based on a small amount of text input. Here are some of the benefits of DALL-E:
- Faster production: DALL-E takes anywhere between a few seconds to minutes to generate an image from a text prompt. This speeds up content production.
- Customization and iteration: Dall-E enables highly customized image creation with detailed text descriptions. The AI-generated images can be refined or edited in subsequent iterations by modifying the prompts.
- Accessibility: Since the model uses natural language for input, it doesn’t require extensive training and is easily accessible to users.
- Extendability: Since DALL-E accepts images as input, users can use the tool to reimagine an existing image too.
- Cross-domain applications: Since DALL-E is domain or industry-agnostic, it can be used in different industries, from advertising and entertainment to education and fashion, as seen in the use cases.
- Low cost: The tool significantly reduces the cost of generating visual content as it requires only the tool and text prompts.
Limitations and challenges of DALL-E
While DALL-E has significant benefits, it has certain limitations too that are important to consider.
- Technical challenges: Even though DALL-E is trained on a large dataset, the model’s language understanding is limited. Often, it doesn’t generate appropriate visuals for a variety of prompts.
- Algorithmic bias from training data: Since DALL-E relies heavily on the data it's trained on, it is possible that the model may reproduce biases present in the training data unintentionally.
- Ethical concerns: There are concerns about the unethical use of the AI model to generate digitally manipulated images called deep fakes.
- Legal concerns: Since DALL-E is trained on images from the internet, there are still unaddressed questions on the copyright of images AI-generated images.
DALL-E vs. DALL E-2
DALL-E and DALL-E 2 are both closed-source, proprietary AI art generators developed by OpenAI.
DALL E is the initial version of OpenAI’s text-to-image generator and DALL-E 2 is the advanced version of DALL-E. Compared to DALL-E, DALL E-2 is trained on approximately 650 million image-text pairs scraped from the internet.
It also uses a diffusion model along with CLIP. The diffusion model removes any noise from the output resulting in much higher-quality, photorealistic images. As a result, DALL-E 2 generates images much faster and provides superior images.
Want to explore more? Learn more about synthetic media and its types.
Soundarya Jayaraman
Soundarya Jayaraman is a Content Marketing Specialist at G2, focusing on cybersecurity. Formerly a reporter, Soundarya now covers the evolving cybersecurity landscape, how it affects businesses and individuals, and how technology can help. You can find her extensive writings on cloud security and zero-day attacks. When not writing, you can find her painting or reading.
