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 Senior SEO Content Specialist at G2, bringing 4 years of B2B SaaS expertise to help buyers make informed software decisions. Specializing in AI technologies and enterprise software solutions, her work includes comprehensive product reviews, competitive analyses, and industry trends. Outside of work, you'll find her painting or reading.
