Adobe Illustrator has this feature called Retype (beta). With it you can select an image in Illustrator and enter Retype (beta) to determine the fonts that were used (at least close matches) in the JPG! It will also do the same for text that has been outlined. It’s amazing!
This tech lets you use augment text-based instructions with visual hints, such as rough sketches and paint strokes. Draw & Delight then uses Firefly to generate high-quality vector illustrations or animations in various color palettes, style variations, poses and backgrounds.
Incorporating 3D elements into 2D designs (infographics, posters, logos or even websites) can be difficult to master, and often requires designers to learn new workflows or technical skills.
Project Neo enables designers to create 2D content by using 3D shapes without having to learn traditional 3D creation tools and methods. This technology leverages the best of 3D principles so designers can create 2D shapes with one, two or three-point perspectives easily and quickly. Designers using this technology are also able to collaborate with their stakeholders and make edits to mockups at the vector level so they can quickly make changes to projects.
And for a deeper dive, check out his 20-minute version:
Meanwhile my color-loving colleague Hep (who also manages the venerable color.adobe.com) joined me for a live stream on Discord last Friday. It’s fun to see her spin on how best to apply various color harmonies and other techniques, including to her own beautiful illustrations:
Adobe prototyper Lee Brimelow has been happily distracting himself by creating delightful little creatures using Firefly, like this:
Today he joined us for a live stream on Discord (below), sharing details about his explorations so far. He also shared a Google Doc that contains details, including a number of links you can click in order to kick off the creation process. Enjoy, and please let me know what kinds of things you’d like to see us cover in future sessions.
A brush makes watercolors appear on a white sheet of paper. An everyday object takes shape, drawn with precision by an artist’s hand. Then two, then three, then four… Superimposed, condensed, multiplied, thousands of documentary drawings in successive series come to life on the screen, composing a veritable visual symphony of everyday objects. The accumulation, both fascinating and dizzying, takes us on a trip through time.
As I say, another day, another specialized application of algorithmic fine-tuning. Per Vice:
For $19, a service called PhotoAI will use 12-20 of your mediocre, poorly-lit selfies to generate a batch of fake photos specially tailored to the style or platform of your choosing. The results speak to an AI trend that seems to regularly jump the shark: A “LinkedIn” package will generate photos of you wearing a suit or business attire…
…while the “Tinder” setting promises to make you “the best you’ve ever looked”—which apparently means making you into an algorithmically beefed-up dudebro with sunglasses.
Meanwhile, the quality of generated faces continues to improve at a blistering pace:
Another day, another special-purpose variant of AI image generation.
A couple of years ago, MyHeritage struck a chord with the world via Deep Nostalgia, an online app that could animate the faces of one’s long-lost ancestors. In reality it could animate just about any face in a photo, but I give them tons of credit for framing the tech in a really emotionally resonant way. It offered not a random capability, but rather a magical window into one’s roots.
Now the company is licensing tech from Astria, which itself builds on Stable Diffusion & Google Research’s DreamBooth paper. Check it out:
Interestingly (perhaps only to me), it’s been hard for MyHeritage to sustain the kind of buzz generated by Deep Nostalgia. They later introduced the much more ambitious DeepStory, which lets you literally put words in your ancestors’ mouths. That seems not to have bent the overall needle in awareness, at least in the way that the earlier offering did. Let’s see how portrait generation fares.
Speaking of Bilawal, and in the vein of the PetPortrait.ai service I mentioned last week, here’s a fun little video in which he’s trained an AI model to create images of his mom’s dog. “Oreo lookin’ FESTIVE in that sweater, yo!” 🥰 I can only imagine that this kind of thing will become mainstream quickly.
I’m not sure whom to credit with this impressive work (found here), nor how exactly they made it, but—like the bespoke pet portraits site I shared yesterday—I expect to see an explosion in such purpose-oriented applications of AI imaging:
We’re at just the start of what I expect to be an explosion of hyper-specific offerings powered by AI.
For $24, PetPortrait.ai offers “40 high resolution, beautiful, one-of-a-kind portraits of your pets in a variety of styles.” They say it takes 4-6 hours and requires the following input:
~10 portrait photos of their face
~5 photos from different angles of their head and chest
~5 full-body photos
It’ll be interesting to see what kind of traction this gets. The service Turn Me Royal offers more human-made offerings in a similar vein, and we delighted our son by commissioning this doge-as-Venetian-doge portrait (via an artist on Etsy) a couple of years ago:
Christian has trained a model on Rivians & says (ambitiously, but not without some justification) that “This is how all advertising and marketing collateral will be made sooner than most of the world realizes.”
On a related note, here’s a thread (from an engineer at Shopify) on fine-tuning models to generate images of specific products (showing strengths/limitations).
I see numerous custom models emerging that enable creation of art in the style of Spider-Man, Pixar, and more.
Motion Library allows you to easily add premade animated motions like fighting, dancing, and running to your characters. Choose from a collection of over 350 motions and watch your puppets come to life in new and exciting ways!
Check out a fun historical find from Adobe evangelist Paul Trani:
The video below shipped on VHS with the very first version of Adobe Illustrator. Adobe CEO & Illustrator developer John Warnock demonstrated the new product in a single one-hour take. He was certainly qualified, being one of the four developers whose names were listed on the splash screen!
How lucky it was for the world that a brilliant graphics engineer (John) married a graphic designer (Marva Warnock) who could provide constant input as this groundbreaking app took shape.
If you’re interested in more of the app’s rich history, check out The Adobe Illustrator Story:
The Corridor Crew has been banging on Stable Diffusion & Google’s new DreamBooth tech (see previous) that enables training the model to understand a specific concept—e.g. one person’s face. Here they’ve trained it using a few photos of team member Sam Gorski, then inserted him into various genres:
From there they trained up models for various guys at the shop, then created an illustrated fantasy narrative. Just totally incredible, and their sheer exuberance makes the making-of pretty entertaining:
“Shoon is a recently released side scrolling shmup,” says Vice, “that is fairly unremarkable, except for one quirk: it’s made entirely with art created by Midjourney, an AI system that generates images from text prompts written by users.’ Check out the results:
Meanwhile my friend Bilawal is putting generative imaging to work in creating viral VFX:
Magdalena Bay has shared a new Felix Geen directed video for “Dreamcatching.” The clip, multi-dimensional explored through cutting-edge AI technology and GAN artwork, combined with VQGAN+CLIP, is a technique that utilizes a collection of neural networks that work in unison to generate images based on input text and/or images.
We ask: how can we use language-guided models to turn our cat into a painting, or imagine a new product based on our favorite toy? Here we present a simple approach that allows such creative freedom.
Using only 3-5 images of a user-provided concept, like an object or a style, we learn to represent it through new “words” in the embedding space of a frozen text-to-image model. These “words” can be composed into natural language sentences, guiding personalized creation in an intuitive way.
I mentioned Meta Research’s DALL•E-like Make-A-Scene tech when it debuted recently, but I couldn’t directly share their short overview vid. Here’s a quick look at how various artists have been putting the system to work, notably via hand-drawn cues that guide image synthesis:
This new tech from Facebook Meta one-ups DALL•E et al by offering more localized control over where elements are placed:
The team writes,
We found that the image generated from both text and sketch was almost always (99.54 percent of the time) rated as better aligned with the original sketch. It was often (66.3 percent of the time) more aligned with the text prompt too. This demonstrates that Make-A-Scene generations are indeed faithful to a person’s vision communicated via the sketch.
The technology’s ability not only to synthesize new content, but to match it to context, blows my mind. Check out this thread showing the results of filling in the gap in a simple cat drawing via various prompts. Some of my favorites are below: