Visual output from Midjourney Version 5. Text prompt: 'High contrast surreal collage poster of architecture and computing.' Image credit: Niall Patrick Walsh via Midjourney
Thirty years after the thaw of the last 'AI Winter,' the landscape of artificial intelligence is one of a forest in full bloom. On a weekly basis throughout 2023, new tools have been released taking advantage of the latest advances in machine learning algorithms, while existing software and applications scramble to maintain relevance by incorporating our new AI companions into their interfaces. Architects and designers have also turned their creative energy to investigating how AI-powered image generators can imagine new styles, new experiences, and new worlds, while also exploring how AI chatbots can suggest sustainability strategies, inform fee negotiations, and aid office management.
How did this AI forest come to be? Who are its caretakers? How might it continue to grow in the future? What is its relationship to the design and operation of the built environment? In our search for answers, we speak with one expert spanning architectural practice and academia on how designers can unlock this newfound potential of machine learning.
Are pictures worth a thousand words?
David Holz was not motivated by money. Bowing out of the venture-backed tech startup he had built over twelve years, the self-described “serial entrepreneur” started a new company of just ten people. “It’s just about having a home for the next ten years to work on cool projects that matter,” Holz told The Verge in an August 2022 interview. “Hopefully not just to me but for the world — and to have fun.”
You likely haven’t heard of David Holz. However, you may have heard of his company and the digital tool that shares its name. Midjourney caused an earthquake among artists and designers when it launched in July 2022, triggering a tsunami of vibrant, whimsical digital artwork shared across social media sites. Holz’s tool, maintained by ten colleagues, is currently used by over 14 million users generating everything from surreal futuristic worlds to the pope donning an oversized stylish coat. If Holz’s aim was to allow the world to have fun, it's 'mission accomplished.'
AI tools such as Midjourney can help us to unlock things we cannot see. — Matias del Campo
Midjourney is an example of a generative text-to-image tool, which uses artificial intelligence to generate images responding to text prompts written by users. No drawing, no coloring, no illustrative input from humans whatsoever— just words. A marketplace that few would have imagined only two years ago, Holz’s Midjourney now competes with rival tools such as DALL-E by OpenAI, Imagen by Google, and Stable Diffusion by Stability AI in allowing anybody with an internet connection to create detailed images on whatever topic comes to mind in a matter of seconds. Despite the frenzy caused by the launch of such tools throughout 2022, the photorealistic quality of their output has continued to improve in the months since. The latest version of Midjourney, Version 5, has all but ditched the whimsical aura and distorted figures of previous versions and can now generate images indistinguishable from reality.
Text-to-image tools were only one-half of the forces that thrust AI into the public discourse in 2023. The other half came courtesy of OpenAI, the creator of Midjourney competitor DALL-E, who launched the AI-powered chatbot ChatGPT in November 2022. Where generative text-to-image tools have been trained using a dataset of millions of images, ChatGPT’s AI model was trained on millions of text documents and webpages written by humans. As a result, ChatGPT can respond to text inputs from users with coherent answers in seconds, from questions on history to advice on dinner recipes. By January 2023, ChatGPT was reported to have reached 100 million monthly active users, making it the fastest-growing consumer application in history, as users flocked to see first-hand the bot’s surreal generation of articles, jokes, essays, business ideas, and more.
While the labels ChatGPT and GPT-4 are sometimes used interchangeably in the discourse surrounding AI, there is an important distinction to be made.
Before addressing the implications of AI-based applications such as ChatGPT and Midjourney on architecture and design, it is worth reflecting on the underlying technology which drives them. ChatGPT is powered by a large language model (LLM) called GPT-4; a deep machine learning algorithm that can recognize, summarize, translate, predict, and generate text and other content such as software code, based on knowledge derived from massive datasets. While the labels ChatGPT and GPT-4 are somtimes used interchangeably in the discourse surrounding AI, there is an important distinction to be made. ChatGPT is an application; an online tool. GPT-4 is the brain behind such applications. ChatGPT relies exclusively on GPT-4 to perform its tasks. However, a machine learning model such as GPT-4 is not exclusive to ChatGPT and can be deployed for uses far beyond the chatbot.
As Zapier succinctly described in March 2023: “If we think of ChatGPT as a Dell computer, then GPT is the Intel processor that powers it. After all, different computers can run on Intel processors in much the same way other AI applications can run on GPT-3 or GPT-4.” Incidentally, OpenAI’s text-to-image tool DALL-E used a version of GPT-4’s predecessor GPT-3 when it was first unveiled in January 2021.
Seeing the forest for the trees
For a closer analogy of the potential applications of machine learning models beyond headline grabbers such as ChatGPT and DALL-E, we can look to nature; specifically to forests, trees, branches, and offshoots. Machine learning models such as GPT-4 sit at the base of an extensive tree with many branches and offshoots. Despite its outsized media attention, ChatGPT is but one branch of the GPT-4 tree. As noted above, the text-to-image application DALL-E represents another branch, having first emerged from an ‘ancestral’ GPT-3 tree in 2021. Other applications, or branches, of the GPT-4 tree can create functional websites from rough sketches, code online games from scratch, or in the case of the mobile app Be My Eyes, analyze and describe real-world objects for visually-impaired people to the “same level of context and understanding as a human volunteer,” according to OpenAI.
From within the ChatGPT and DALL-E branches, families of useful, lucrative offshoots can emerge depending on how a user exploits each application. Offshoots from the ChatGPT branch have so far included media companies using ChatGPT to prepare for interviews, simplify subjects matters, and generate ideas for popular content, as well as healthcare companies using ChatGPT to flag potential drug interactions, suggest treatment options for specific conditions, or provide relevant clinical guidelines. Meanwhile, architecture-specific offshoots from the DALL-E branch can include generating precedent images for design concepts, exploring artistic styles, and performing realistic edits to existing images using text instructions. The GPT-4 tree does not depend on any one of these branches or offshoots to survive, but each branch, whether the ChatGPT branch, the DALL-E branch, or the Be My Eyes branch, and all their dependent offshoots, rely on the GPT-4 tree for life.
Even designers with years of engagement in the field, let alone the majority of architects who have little or none, are battling to simply see the forest for the trees.
The GPT-4 tree is also one tree in a wider ‘species’ of LLM trees, each of which uses a rival LLM to support its branches. Stable Diffusion creators Stability AI, for example, recently developed and publicized its own LLM named StableLM. By releasing their LLM open-source, the StableLM tree will soon bloom with branches and offshoots grown by third-party programmers and companies eager to augment and adapt the model’s text and code generation capabilities. Zooming out even further, these various LLM trees belong to just one species of tree in the AI forest, with other species supported by alternative machine learning algorithms differentiated by their methods of learning and the extent of human supervision.
Architects and designers can engage with this forestry ecosystem across several scales. Like highly-specialized botanists, some may find themselves occupied fully with one specific branch of a particular tree, as have the many designers today who are exclusively exploiting Midjourney as a design companion. Others will see these branches as low-hanging fruit and instead become dendrologists of a whole GPT-4 or StableLM tree, seeking to find new ways of utilizing the machine learning model’s billions of variable parameters beyond more common image and text generation uses. Other architectural naturalists may go even further and, like modern-day Darwins, embark on a search for new orchards beyond our prevailing theories on the relationship between humans and artificial intelligence. With the accelerated pace of AI advances through the early part of the 2020s, even designers with years of engagement in the field, let alone the majority of architects who have little or none, are battling to simply see the forest for the trees.
Artificial intelligence and the architect
“There is a whole plethora of areas that the architecture discipline can benefit from by using machine learning and artificial intelligence,” architect and educator Dr. Matias del Campo told me in a recent conversation about text-to-image tools. Del Campo is an associate professor of architecture at the University of Michigan’s Taubman College of Architecture and Urban Planning and director of the school’s Architecture and Artificial Intelligence Laboratory. Del Campo's lab is an interdisciplinary group encompassing architecture, robotics, computer science, and data science, whose mission is to uncover ideas, concepts, and technologies with respect to using artificial intelligence in architectural design. Beyond his academic commitments, del Campo is also a director of the architecture practice SPAN alongside Sandra Manninger; a firm which in del Campo’s words “oscillates between speculating about possibilities regarding AI and architectural design, and the implementation of those possibilities in the form of projects, books, articles, papers, lectures, and exhibitions.”
Architects are wonderfully outfitted to create interesting results from Midjourney.— Matias del Campo
Del Campo belongs to a community of architectural figures whose interests in computation and artificial intelligence began over twenty years ago. Today, he is joined by a new cohort of designers who, like millions of others beyond the architecture profession, were introduced to the power of AI tools through chatbots such as ChatGPT and generative image tools such as Midjourney. “Architects are wonderfully outfitted to create interesting results from Midjourney,” del Campo told me. “Through our architectural education, we have been introduced to a large range of topics, from art and painting styles to literature, photography, and fashion. We have a unique ability to combine these varying subject matters into a comprehensive sentence that can be wonderfully visualized by artificial intelligence. All of those bits and pieces of disparate knowledge came together to create this AI explosion that was seen in the architecture discipline in 2022.”
Much of my conversation with del Campo revolved around text-to-image tools such as Midjourney, but as the architect explains, the applications of artificial intelligence in architecture go much further. To return to our earlier analogy, del Campo sees promise in stepping back from the Midjourney branch to appreciate the broader algorithmic tree. “This explosion of tools in the architecture discipline might not be something that everybody adopts forever, but it has introduced a whole new generation of architects to the world of AI,” del Campo told me. “We can now start to ask wider questions such as: How can we use AI to elevate the living standards of millions of people? How can we reduce the consumption of materials using machine learning algorithms? How can we use AI to generate structures for buildings that are more efficient and sustainable than those of today? The more people working on these questions, the more likely we are to succeed.”
“Prediction is very difficult, especially if it’s about the future,” said the physicist and Nobel laureate Niels Bohr. Nevertheless, the question of how artificial intelligence will impact the architectural profession of the near future is one which Archinect’s editorial has wrestled with before and will continue to, amid the ever-expanding forest of AI tools, applications, and models. “A year is a long time in computer science terms, and predictions are always difficult,” del Campo explained. “AI will very soon have a big impact on mundane processes such as cost calculation optimization, checking plans for codes, ensuring plans are drawn correctly, and perhaps negotiating with another AI that is operated by local planning authorities you need to submit plans to. So in the best case, it will hopefully free up time for architects to design.”
I believe [AI] is going to play the key role of augmenting the possibilities within our minds as humans.— Matias del Campo
“In the design process, too, AI will play a role,” del Campo continued. “But we shouldn’t make the mistake of thinking about these machines in the same way that we think about humans. AI tools such as Midjourney can help us to unlock things we cannot see. It can provoke us towards architectural solutions we didn’t previously think about. But it is fundamentally still an algorithm based on an enormous database, that is able to go through this database at an enormous speed. I believe it is going to play the key role of augmenting the possibilities within our minds as humans. But it is not about being replaced by AI. I don’t think that will happen any time soon.”
As my conversation with del Campo concluded, I asked if he, like many others, was surprised by the sudden acceleration in machine learning applications such as Midjourney and DALL-E in the early 2020s. “Sandra Manninger and I began conversations with computer scientists on AI in the late 1990s,” he told me. “When this started, they were able to simulate interactions between only two neurons, which is nothing. Even in the early 2010s, the algorithms and computing power we see today didn’t exist. What we are experiencing right now, I wouldn’t even have expected one year ago. Such developments were perhaps not as unexpected in computer science circles, but in the architectural world, they certainly were.” Today, there is evidence that the ever-advancing capabilities of machine learning models such as GPT-4 are even outpacing the comprehension of those responsible for their creation. In a March 2023 conversation, OpenAI CEO Sam Altman remarked about the company’s latest model GPT-4: “Do we understand everything about why the model does one thing and not one other thing? Certainly not. Not always. But I would say we are pushing back, like, the fog of war more and more.”
Brave new world?
Through this fog of war, an AI arms race is underway between tech companies large and small, all of whom fear being left behind by their competitors. It’s a dangerous recipe from an embattled industry. The red-hot crypto-craze of the late 2010s has cooled. The decade-long supply of venture capital spurred by low-interest loans has been drowned by recent interest rate hikes, exemplified by the collapse of Silicon Valley Bank in March 2023. For many tech companies, foraging the vast, unregulated, unexplored AI forest for money trees is the only game in town.
Fearing the fallout of this arms race, over one thousand notable tech figures signed an open letter in March 2023 calling for “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4.” The authors cited concerns such as malicious actors “flooding information channels with propaganda and untruth,” and job-hunting “nonhuman minds that might eventually outnumber, outsmart, obsolete, and replace us.” The letter instead calls for a “level of planning and management” that is currently absent in a space occupied only by AI labs “locked in an out-of-control race to develop and deploy ever more powerful digital minds that no one — not even their creators — can understand, predict, or reliably control.”
Just as real-life forests are fueled by nutrients such as nitrogen and phosphorus, our AI forest is fueled by data.
Practicing architects are not direct participants in the AI arms race, but neither are they immune from its effects. Beyond wider fears of job replacement expressed in the open letter, the deployment of AI tools in the architectural design process brings with it a series of hazards. Just as real-life forests are fueled by nutrients such as nitrogen and phosphorus, our AI forest is fueled by data, farmed from the physical and digital worlds, from city streets to social media sites. In this arms race, where the speed of deployment has so far taken precedent over questions of ethics, bias, and safety, such data is inevitablycontaminated by the many biases embedded in the built environment, be it biases against women, people of color, the elderly, or those with varying degrees of physical abilities and motion. Are architects prepared to place an increased responsibility for the design and operation of the built environment on AI tools, at a time when the decision-making processes powering such tools, to the extent we even understand them, are closely guarded secrets?
In tandem with the question of data bias is the question of data collection, ownership, and liability. Throughout 2023, the creators of AI image tools including Midjourney and Stable Diffusion have been slammed with copyright suits from artists who claim their work was taken from the internet and used in AI data training sets without their consent. In a future scenario where a tech-savvy architecture graduate acquires a machine learning algorithm from a computer science friend, trains a machine learning model exclusively on the architectural drawings, models, and imagery of projects by Zaha Hadid Architects, and generates a complete architectural project to be sold to a client, who can claim license over the scheme? The graduate? Zaha Hadid Architects? The friend who wrote the algorithm? The machine learning model itself? If the resulting scheme exhibits defects derived from the original Zaha Hadid Architects projects, resulting in a fatal accident during construction, who is liable for prosecution? With AI-specific legislation currently being developed across Europe and the United States, such legal considerations will inevitably find their place in the architectural education and licensure curricula of the near future.
The AI forest is growing faster than humanity could ever hope to cut it down.
As architects, educators, legislators, and tech leaders ponder such questions and concerns, the AI forest continues to relentlessly sprout new trees, branches, and offshoots. In human literature, forests have served as the setting for fairytales and horror stories alike. Similarly, storytellers on how artificial intelligence will alter the future course of human civilization range from ecstatic digital utopians to fearful techno-luddites. Concerned open letters aside, the AI forest is growing faster than humanity could ever hope to cut it down. The challenge, therefore, moves to how we view the forest, how we condition the data soil that sustains it, and who, if anybody, we appoint to manage it.
Like its natural counterparts, the AI forest can be either a celebration of intricacy, cooperation, and humane responsibility, or a mismanaged well of exploitation, illegality, and greed.
DALL-E has been programmed to use computational thinking to generate ideas for architecture instead of just learning from existing designs. DALL-E is a virtual architect and the only known architectural works are built in Second Life.How is AI related to architecture? ›
The Role of AI in the Architectural Process
It simulates reasoning, learning from experiences, problem-solving, and more. AI is a powerful tool for architects, handling repetitive tasks and information processing at a blazing speed.
The Relationship Between AI, ML and DL
Machine Learning is a sub-category of AI, and Deep Learning is a sub-category of ML, meaning they are both forms of AI. Artificial intelligence is the broad idea that machines can intelligently execute tasks by mimicking human behaviours and thought processes.
In summary, while artificial intelligence (AI) is being increasingly used in the field of architecture, it is unlikely to completely replace human architects.What is DALL-E and how does it work? ›
Dall-E is a generative AI technology that enables users to create new images with text to graphics prompts. Functionally, Dall-E is a neural network and is able to generate entirely new images in any number of different styles as specified by the user's prompts.What programming language is used in DALL-E? ›
E) and DALL-E 2 are deep learning models developed by OpenAI to generate digital images from natural language descriptions, called "prompts". DALL-E was revealed by OpenAI in a blog post in January 2021, and uses a version of GPT-3 modified to generate images.What is an example of AI in architecture? ›
For example, if an architect is designing a house for a family, they can collect data about that family. The architect can also use an AI system to pull zoning data, building codes, disabled design data, and more before generating variations and different options.How is artificial intelligence changing the future of architectural design? ›
AI can be used to create sustainable designs that reduce the environmental impact of buildings. By analyzing data on factors such as energy usage, water consumption, and waste management, AI algorithms can help architects to create buildings that are optimized for sustainability.Which AI generator is best for architecture? ›
The AI art generator Midjourney is the favored tool in architecture. And designers are using it to conjure up their wildest dreams.What are the three domains of AI? ›
The domain of AI is classified into Formal tasks, Mundane tasks, and Expert tasks.
Machine learning and deep learning are both types of AI. In short, machine learning is AI that can automatically adapt with minimal human interference. Deep learning is a subset of machine learning that uses artificial neural networks to mimic the learning process of the human brain.What are the examples of AI? ›
Apple's Siri, Google Now, Amazon's Alexa, and Microsoft's Cortana are one of the main examples of AI in everyday life. These digital assistants help users perform various tasks, from checking their schedules and searching for something on the web, to sending commands to another app.Will robots take my job architecture? ›
Fornuately, architecture measures in at less than 2 percent chance of becoming automated, compared to other professions like telemarketers, sewers, and insurance underwriters, which face a 99 percent chance of being replaced by technology in just the next ten years.Will architecture be in demand in the future? ›
Architects remain in demand despite facing numerous challenges. Whether we are building houses or other structures, people will always need professionals to do the work. To continue to provide valuable services to your clients, architects must stay abreast of the latest trends and technologies.Can architects be automated? ›
Automation is revolutionizing the AECO industry. From experimenting with uncanny built forms to using robots for construction, automation is allowing engineers and architects to be more creative and empowered. Architecture automation is rapidly gaining popularity because of its ability to reduce human effort.What is not allowed on DALL-E? ›
Do not upload images of people without their consent. Do not upload images to which you do not hold appropriate usage rights. Do not create images of public figures.What is DALL-E useful for? ›
DALL. E 2 can create original, realistic images and art from a text description. It can combine concepts, attributes, and styles.Who gets access to DALL-E? ›
Starting today (September 28), anyone can use DALL-E 2 to create AI-generated images and let their imaginations run wild.Does DALL-E use machine learning? ›
Create AI-Generated Images With DALL-E
It makes use of incredible advances in machine learning, including GPT-3 and diffusion models. DALL-E's knowledge of the human world, including the history of art, comes from millions of images that were likely gathered from the internet.
Python and Java are both languages that are widely used for AI. The choice between the programming languages depends on how you plan to implement AI. For example, in the case of data analysis, you would probably go with Python.
Python. Python is the most popular programming language for AI, it's one of the hottest languages going around, and it's also easy to learn! Python is an interpreted, high-level, general-purpose programming language with dynamic semantics.What are the 4 types of AI with example? ›
- Reactive machines. Reactive machines are AI systems that have no memory and are task specific, meaning that an input always delivers the same output. ...
- Limited memory. The next type of AI in its evolution is limited memory. ...
- Theory of mind. ...
One of the most significant benefits of AI for architecture is its ability to enhance sustainability. AI-powered tools can analyze environmental factors, such as solar radiation, wind patterns, and local climate, to optimize building performance and reduce energy consumption.What is the impact of artificial intelligence on the design industry? ›
AI has revolutionised the graphic design process, making it more efficient and user-friendly. With AI, designers can automate tedious tasks, freeing more time for creative work. Additionally, AI algorithms can analyse data and generate designs tailored to specific audiences and demographics.What is the purpose of architectural design? ›
Architectural design is a discipline that focuses on covering and meeting the needs and demands, to create living spaces, using certain tools and especially, creativity. Therefore, the aim is to combine the technological and the aesthetic, despite the general belief that architecture is only a technological task.Why is architecture always changing? ›
One of the main reasons why architecture changes over time is due to the criticisms people have over designs. As architects work to problem solve, architecture changes to accommodate the needs that have been voiced and to fix the issues that have been raised.Which AI generator is better than DALL-E? ›
|AI art generator||Price||Output speed|
|Bing Image Creator||Free||Fast|
|DALL-E 2 by OpenAI||Free + Credits (depends on sign up date)||Fast|
|Dream by WOMBO||Free + Subscription||Fast|
The most common software used for architecture are Revit, ArchiCAD, All Plan, Sketchup.What is the most powerful AI model? ›
GPT-3 was released in 2020 and is the largest and most powerful AI model to date. It has 175 billion parameters, which is more than ten times larger than its predecessor, GPT-2.What are the three different levels of AI development? ›
AI is divided broadly into three stages: artificial narrow intelligence (ANI), artificial general intelligence (AGI) and artificial super intelligence (ASI).
For safe, secure, sustainable AI, enterprises must learn to gainfully manage the three dimensions of AI– righteous, reliable, and restrictive– as an integral part of AI and data governance.Who is the father of AI? ›
John McCarthy is one of the "founding fathers" of artificial intelligence, together with Alan Turing, Marvin Minsky, Allen Newell, and Herbert A. Simon.What is the difference between machine learning and artificial intelligence? ›
Differences between AI and ML
While artificial intelligence encompasses the idea of a machine that can mimic human intelligence, machine learning does not. Machine learning aims to teach a machine how to perform a specific task and provide accurate results by identifying patterns.
Artificial Intelligence is the concept of creating smart intelligent machines. Machine Learning is a subset of artificial intelligence that helps you build AI-driven applications. Deep Learning is a subset of machine learning that uses vast volumes of data and complex algorithms to train a model.Why do we need artificial intelligence? ›
Today, the amount of data that is generated, by both humans and machines, far outpaces humans' ability to absorb, interpret, and make complex decisions based on that data. Artificial intelligence forms the basis for all computer learning and is the future of all complex decision making.What are the 5 ideas of AI? ›
In this fun one-hour class, students will learn about the Five Big Ideas in AI (Perception, Representation & Reasoning, Learning, Human-AI Interaction, and Societal Impact) through discussions and games.What is a simple example of strong AI? ›
Here are some examples: Self-driving cars: Google and Elon Musk have shown us that self-driving cars are possible. However, self-driving cars require more training data and testing due to the various activities that it needs to account for, such as giving right of way or identifying debris on the road.Will AI replace interior designers? ›
While AI has many benefits and can enhance the ID process, it is still very unlikely that it will ever completely replace human interior designers. It can provide valuable insights and assistance, but it will always need a human to empathise and put themselves in the shoes of the clients.Will AI replace graphic designers? ›
No, AI is unlikely to replace designers entirely. While AI can be used to automate certain aspects of design, such as generating layouts or color schemes, it cannot replace the creativity, critical thinking, and problem-solving skills that designers bring to their work.Can lawyers be replaced by AI? ›
Professor Eric Talley of Columbia Law School, who recently taught a course on Machine Learning and the Law, says AI won't replace lawyers but will instead complement their skills, ultimately saving them time, money and making them more effective.
In summary, while artificial intelligence (AI) is being increasingly used in the field of architecture, it is unlikely to completely replace human architects.What is the highest paying job in architecture? ›
- Architectural technician.
- Architectural drafter.
- Architectural designer.
- Historic preservation architect.
- Urban planner.
- Landscape architect.
- Retrofit architect.
- Industrial architect.
With training and experience, architects can make lucrative salaries compared to other professionals in the general workforce. The average salary for an architect is $96,580 per year . An architect's salary depends on their geographic area, employer and professional experience.Do architects use coding? ›
The primary reason why architects should learn to code is to grow their value in the field. It's not a fundamental skill, but it's an important one. Architectural programming can help you grow your skills and expand your career in many ways. Coding is like getting under the hood of design software.How do architects use AI? ›
AI tools are altering the architectural industry's planning, production, and building processes. Using these resources, architects can boost efficiency, develop designs much more quickly, and save time and resources for other issues such as cost analysis and green building initiatives.How architects are using AI? ›
By utilizing AI algorithms, architects can quickly generate and evaluate different design options, ultimately helping them to arrive at a final design more efficiently. One way that AI can be used in architecture is through the use of generative design software.Is DALL-E illegal? ›
In the eyes of the Copyright Office, the public is free to reproduce, publish, or sell your DALL-E 2-generated masterpiece, no strings attached. Congress could change the law, or the courts could recognize a copyright in AI-generated work despite what the Copyright Office thinks.What is the difference between Midjourney and DALL-E? ›
One of DALL-E's main advantages over Midjourney and other AI-powered image-generating platforms is its capacity to produce extremely imaginative and intricate images. Pushing the boundaries of what is possible with AI-generated images, DALL-E is able to produce images that go beyond what is feasible in the real world.Do I own the images made in DALL-E? ›
You might be unaware that the images you create with DALL-E are yours to own, and you are free to do what you like with them. That includes removing the watermark and selling your Dall-E creations if that's your goal.Are you allowed to use DALL-E images? ›
DALL-E 2 users are allowed to use generated images for commercial purposes like printing, selling or licensing. Users must credit DALL-E 2 with the work by the watermark in the image corner.
We began by previewing DALL. E 2 to a limited number of trusted users. As we learned more about the technology's capabilities and limitations, and gained confidence in our safety systems, we slowly added more users and made DALL. E available in beta in July 2022.Is DALL-E now open to the public? ›
In September 2022, DALL-E 2 officially closed its waiting list and opened the platform to the public. Users start with 50 free credits to transform searches into fully generated artwork and 15 free credits every month from then on. The site also allows you to buy more credits.Is DALL-E available for everyone? ›
That means anyone can sign up and use it. DALL-E is a deep learning image synthesis model that has been trained on hundreds of millions of images pulled from the Internet.How expensive is DALL-E? ›
Currently, DALL-E charges $0.02 (USD) for an image that you need to have a pixel resolution of 1024 x 1024. Meanwhile, an image with a pixel resolution of 512 x 512 is set at a price of $0.018. Lastly, a picture with 256 x 256-pixel resolution will cost $0.016 in total.Can I use DALL-E for free? ›
DALL. E 2 is one of the best AI image generators available—and you can try it out now for free. The idea is that you enter a text prompt—like, "an oil painting of a monkey in a spacesuit on the moon"—and the AI does its best to generate an image that matches your idea.Can I sell my art from DALL-E? ›
Can I Sell Art Made with DALL-E? Yes! All users get full usage rights to commercialize the images they create with DALL-E, including the right to reprint, sell, and merchandise. This includes images they generated during the research preview (from a few months ago).How long does it take to make a picture with DALL-E? ›
You can also purchase 115 credits for US$15. P.S. If you can't wait to try it, give DALL·E mini a go for free. However, the quality of its images are generally poorer (giving rise to a host of DALL·E memes) and takes about ~60 seconds per prompt (DALL·E 2 in comparison only takes 5 seconds or so).