What what what, Pixelmator Pro 1.8?! Oh yes, we’re feeling extra generous after the recent sneak peek at Pixelmator Pro 1.7 and we have some more great news about another great feature coming to Pixelmator Pro. That great feature is incredibly extensive support for AppleScript, the powerful and easy to use English-like scripting language for macOS.
Using AppleScript, you can control most of the tools and features in Pixelmator Pro and speed up repetitive tasks or even write scripts to create special effects. And because AppleScript is based on English, you don’t even need much programming experience to get started with it!
Here’s a quick example of something you can do with AppleScript.
Or, remember the tutorial for the Groovy text effect we released not too long ago? You can use AppleScript to automate that too! If you’d like to test this out, you can download both that sample script and the “Hello, world!” script below.
And to help us polish off AppleScript support, we’re going to need your help. So, starting today, we’re releasing the 1.8 update as a public beta. You can sign up for the beta by emailing us at email@example.com. And we’ll be waiting for your feedback at the same address!
Believe it or not, we’re finally doing it. We’re adding one of our most requested features of all time in the next major Pixelmator Pro update. And today we’re bringing you an ultra-exclusive sneak peek at what that feature will look like
Yes, Pixelmator Pro 1.7 will add text on a path! This, of course, also means curved text, circular text, and text on any kind of path you can draw or imagine. We’re using native Mac text rendering, so emoji and SVG fonts are also fully supported. And we wouldn’t be the Pixelmator Team if we didn’t approach this with the goal of making it simpler, easier, and more intuitive than in any other app. Needless to say, you’ll be the final judges but we’re pretty darn happy with how things look to have turned out!
Oh, and that’s just one of three major new features coming in 1.7 We’ll keep the others under wraps for now, but the update should be out in just a few weeks time! To be the first to know about it and the other awesome things we’re working on right now, you can sign up for our very cool (and very occasional) newsletter.
Pixelmator Pro 1.6.4 is out today, adding support for the WebP image format. And since this format is pretty interesting and unique, we wanted to share the news about today’s update here on our blog!
WebP support in Pixelmator Pro
First, a little about WebP support in Pixelmator Pro itself. It’s pretty extensive and we’ve added a few different things:
Support for both opening and exporting WebP images
Lossless compression and lossy compression with adjustable quality
Transparency (for both lossless and lossy compressed images)
A Quick Look plug-in so you can preview WebP images in the Finder
Awesome! If you’re not too familiar with it, you might be wondering — what is WebP and why is it such an interesting format? Well, it’s one of the candidates to be the next-gen format for displaying images on the web thanks to some improvements over the two main existing formats, JPEG and PNG.
Lossless and lossy compression
One reason for WebP’s promise is that it offers both lossless and lossy compression. What’s that? Well, compression refers to reducing the size of an image. Lossless compression is when you reduce the size of an image with no data (i.e. quality) loss, hence the term lossless. Lossy compression reduces the size of an image with some potentially “unnecessary” data being discarded or lost, hence the term lossy.
Used by the PNG format
Used by the JPEG format
All image data preserved
Some image data lost
Larger file sizes
Smaller file sizes
Even though it offers lossy compression and, theoretically, loses some data, JPEG is a great format for the web as it reduces the size of images dramatically and there’s no perceptible drop in quality when saving with a high enough quality setting.
However, as JPEG offers only lossy compression, if you’re looking to ensure you get no data loss at all, you have to use a different format: PNG. The trade-off with this is that you get a much bigger file size, at least several times larger than the equivalent JPEGs.
WebP combines both compression methods in one format and Pixelmator Pro supports them both. At 100% quality, you get lossless compression, like with PNG, and at 99% quality or lower, you get lossy compression, like with JPEG.
99% quality or lower
Another great thing about WebP is that it supports transparency. Right now, if you’d like to save with transparency, you have to use PNG as JPEG doesn’t support it. So once again, WebP combines the benefits of both primary web image formats into one. What’s more, unique to WebP, it supports both transparency and lossy compression at the same time, something that neither JPEG nor PNG can do.
File size improvements
Finally, both lossless and lossy compressed WebP images are around 25% smaller than comparable PNGs and JPEGs of the same quality.* Pretty great for speeding up loading times!
Although not all major web browsers support WebP just yet (Safari support is missing), most of them do and thanks to WebP support in Pixelmator Pro, you’re free to experiment and see for yourself whether you’d like to switch to WebP for certain images.
Today’s update is now available on the Mac App Store and is free for all existing Pixelmator Pro users.
You heard that right, Pixelmator for iOS just got an awesome major update! in Pixelmator 2.5, we’ve added a brand new, Files-based document browser, a new photo browser that makes it easier to browse and open images from your Photos library, and new image size presets along with a new browser.
Native Document Browser
Use the new Files-based document browser to open and manage your documents.
New Photo Browser
Use the new Files-based document browser to open and manage your documents.
Image Size Presets
Quickly create new images with common sizes.
A big step for Pixelmator for iOS
This update is a really big deal for Pixelmator for iOS. These new features might not be amazingly flashy but they’re incredibly important to the future of Pixelmator for iOS. And the headline feature is the awesome Files-based document browser, bringing a much-improved file browsing and opening experience along with great features like file search, tagging, and more.
Ever since the Files app was introduced, it was obvious it would be absolutely perfect for Pixelmator and we’ve been waiting for the right moment to replace our gallery with it. As we start working to refresh and update Pixelmator for iOS, this always had to be the they key first step in that process. And while it may seem relatively simple, this update actually took us around 6 months to design, develop, and test — a lot of work!
Today, we couldn’t be happier to be able to share this with you. The update is a free for all existing Pixelmator users and is available to download from the App Store right now.
That’s it for this round of major updates (we also recently updated Pixelmator Pro and Pixelmator Photo), next stop — more great updates and new features (of course!) and some new tutorials and other user materials over the coming weeks. Until next time!
The fantastic updates just keep on coming and now it’s time for Pixelmator Photo 1.2! The latest major update — months in the making — brings Magic Keyboard, trackpad, and mouse support, Split View support, the machine-learning powered ML Match Colors, and more. Let’s take a closer look.
Enjoy full support for the new, click-anywhere trackpad.
Use Pixelmator Photo and any other app side by side.
ML Match Colors
Match the look and feel of one photo to another.
Easily fine-tune the intensity of color adjustments and presets.
Magic Keyboard, trackpad, and mouse support
The new cursor in iPadOS brings a whole new way to work in Pixelmator Photo and we’re incredibly excited to be adding support for editing with a Magic Keyboard, trackpad, or mouse to Pixelmator Photo. If you’re all about using a cursor with iPad, we hope you’ll love the added precision.
Edit in Pixelmator Photo and any other app side by side
Thanks to Split View support, you can now edit in Pixelmator Photo and any other app side by side! Of course, Pixelmator Photo works with Slide Over, too.
Match the look and feel of one photo to another
The machine learning-powered ML Match Colors, first debuted in Pixelmator Pro, is now available in Pixelmator Photo. It lets you match the look and feel of completely different photos using our cutting-edge machine learning algorithm trained on 20 million professional photos. And it works with Split View, so you can simply drag and drop photos from other apps to use this new feature!
Adjustment intensity, quicker ways to copy adjustments, and more
This update turned out to be bigger than even we expected and we’ve got quite a few other great new features and improvements.
You can now control the intensity of adjustments using a handy new slider you’ll find at the bottom of the color adjustments pane. We’ve also added a new Recents collection of presets, which contains 5 automatically-generated adjustment presets with the settings from your most recently edited photos. And we’ve made it much easier to copy and paste adjustments between photos.
Head down to the App Store to make sure you’re all up to date and we’ll have some more great update news for you not too long from now. Stay tuned!
Fresh from the Pixelmator oven, we’ve just released Pixelmator Pro 1.6 Magenta, a major update you’re sure to love.
The all-new color picker
Pixelmator Pro now has a brand new color picker, designed to be incredibly powerful and full-featured, yet amazingly easy to use.
We love the Colors window, so why did we decide to make our own color picker? Well, for an app like Pixelmator Pro, it falls a little short in a few small ways. And those small things often make a big difference.
We wanted to give you an easy way to pick colors using the classic hue, saturation, and brightness control, hex and and RGB color codes, and good old color swatches. And we wanted everything to be in one place so it’s all super easy to find. We also wanted to have a beautiful and informative eyedropper for when you’re picking colors from your image. And this is the result:
We’ve also added a dedicated Color Picker tool, where the eyedropper is always active so you can quickly pick a series of colors from an image. You can also customize how the eyedropper works everywhere else in Pixelmator Pro. For example, you can change its sample size (so it picks an average color, rather than an exact sample), change which color code is displayed, or turn off displaying color names. We hope you’re as excited about this new picker as we are!
An easier way to select multiple objects
This is another great improvement to make Pixelmator Pro even easier to use — you can now drag over multiple objects to select them. Hint: to make this easier to use with illustrations and other images, lock the background layer.
Identify and replace missing fonts
Whenever you open an image with fonts that you don’t have installed on your Mac, you’ll see a handy notification letting you know. And, using the Replace Fonts feature, you’ll be able to replace those missing fonts in a snap!
Performance improvements and other goodies
Along with all this, we’ve also included some performance improvements. The image overlay — which includes things like guides, selection outlines, layer handles, and others — has been rewritten to use Metal, bringing obvious speed improvements. What’s more, you can now press the Shift – Command – h keyboard shortcut to hide or show it!
We’ve also improved the speed of layer strokes, added the ability to apply inside and outside strokes to text layers, and made a range of improvements the the shape tools, making it easier to create illustrations and drawings. All in all, it’s a pretty awesome update, even if we do say so ourselves.
If you want to know every detail about the update, head down to our What’s New page, where you’ll find the release notes and more. Otherwise, visit the Mac App Store to make sure you’re all up to date. And don’t hesitate to let us know what you think!
We’ve been working on all our apps recently and it just so happens that Pixelmator Pro, Pixelmator Photo, and Pixelmator for iOS are all going to get major updates in the next month. So we wanted to tell you a little bit more about each update and maybe even give you a chance to get your hands on them. Here goes.
Pixelmator Pro 1.6 Magenta
Our focus for Pixelmator Pro this year is on making the app even more user-friendly and enjoyable to use. And one thing that we’ve wanted to improve for a while now is the color picker. So we decided to make a color picker of our very own! Just look at how awesome it is:
This is a major update, so there are other big additions, but we’ll let you know more about them once the update is available. However, if you want to see what the other major additions are ahead of everyone else, you can jump onto the Pixelmator Pro beta and help us put this update through its paces. To join our team of beta testers, please shoot us a quick email.
Pixelmator Photo, our super powerful and advanced photo-editing app for iPad, is getting a major update too. One of the highlight features is Split View support so you can work in Pixelmator Photo and any other app side by side. You can also take a guess at what Split View might mean for the future of Pixelmator Photo…
There are other major new features but we’ll keep those details under wraps for now. Unless, of course, you want to check out the beta and help us make sure the update is as polished as possible. We’ve opened up a few hundred testing spots and you can join using the link below.
It liiiiives! After a brief hiatus of two and a half years or so (we kid, we kid), Pixelmator for iOS is getting a major update. The biggest addition is the new Files-based document browser as well as the new image size presets and photo browser.
You might be wondering what this means for Pixelmator and that’s a good question. The answer is that, little by little, we plan to refresh and improve the app and, eventually, make it compatible with Pixelmator Pro. This is one very fundamental step towards that goal. We don’t have a timeline just yet for full compatibility and this will take a while but we’re very excited to get started on it! If you’d like to take this beta for a spin, you can sign up via TestFlight below. We have a few hundred spots available.
Howdy! Pixelmator Pro 1.5.5 has just been released and it brings some significant improvements to ML Super Resolution and ML Denoise as well as two new Automator actions for those two features. Because seeing is believing, we thought we’d whip up a blog post with some examples to highlight the changes. Let’s start with ML Super Resolution.
ML Super Resolution
Even though we released it just about two months ago, we’ve made some pretty big improvements to ML Super Resolution in that time. In this update to it (a version 2.0, if you will) we’ve focused on four things in particular:
1. Removing compression blocks in heavily compressed images
2. Improving how portrait photos are upscaled
3. Preserving sharp edges and details in illustrations
4. Making the new, smarter algorithm faster and more efficient
Compression block removal
The previous algorithm already did a great job at removing the small compression artifactsCompression Artifacts Compression artifacts often look like small dots and appear at edge areas — where contrasting colors meet in images. that usually appear around object edges in compressed JPEG images. But JPEGs, especially ones that have been compressed quite a lot, will also feature visible compression blocksCompression Blocks JPEG compression blocks appear when the compression algorithm reduces subtly differently colored pixels (for example, in gradients) to one color. They’re most visible in photos that include the sky or other gradients.. This new version of ML Super Resolution does an even better job with the small artifacts and it takes care of the blocks!
We had actually been working on this since before the original release and wanted to include it when ML Super Resolution first came out. However, adding the block removal code would cause some pretty crazy blurring issues and, honestly, we couldn’t work out why. After some digging, it became clear that certain sizes of text in our training dataset were being interpreted as compression blocks! Once we addressed that, we were good to go.
Upscaling portrait photos
How ML Super Resolution upscales extremely pixelated faces is kind of magical. We made some improvements to make sure facial features are recreated in as natural a way as possible, even from a few pixels.
ML Super Resolution
Improved quality around edges and small details
Another thing we focused on is edge areas — especially between colors that have different hues and levels of saturation but the same lightness. These can appear in any image but are especially common in illustrations. Edges such as this weren’t quite as sharp as we would have liked and there was some slight ringing that we really wanted to get rid of, too.
ML Super Resolution(Old Algorithm)
ML Super Resolution(New Algorithm)
Smarter and more efficient
In order to make these improvements, we retrained our algorithm and slightly increased the size of the Core ML model included in Pixelmator Pro — from 5 MB to around 7 MB. Despite being a little bigger, this version of the model is actually faster than the previous one thanks to some additional optimizations. This is also a good place for a reminder that when you use any of our machine learning features, everything is processed securely on your device by a trained model integrated using Core ML.
1. Trainable neural network parameters (more is better): 1,692,928 (new algorithm) vs. 1,185,408 (old algorithm).
2. Floating point operations per pixel (fewer is better): 903,870 (new algorithm) vs. 1,128,062 (old algorithm).
It’s probably fair to say that ML Denoise, our machine learning-powered noise removal feature, has been in a kind of beta stage until today. When we create any automatic feature, one of the main goals is for it to never make any image worse. With noise removal, that’s very difficult. So we took a conservative approach and tried to avoid this. This meant that the algorithm would sometimes do nothing at all, which is a pretty frustrating experience. What’s more, sometimes some loss of quality or sharpness is worth it because there are ways to recover it. With today’s update, ML Denoise is much better at removing heavy camera noise and is a little more confident in general.
ML Denoise in action
You can see ML Denoise in action with a few example images below. Your feedback about this feature over the last few months was a great help, by the way!
Seeing as this is a machine learning-based algorithm and requires lots of processing power, for now, this will continue to be a one-click action. Adding a slider is potentially possible but getting real-time feedback and good performance requires us to do some more research. It is still in our plans for the future, though.
Automator actions for ML Denoise and ML Super Resolution
And to round off the update, we’ve added two new Automator actions: Increase Resolution of Images and Denoise Images, bringing the total number of Pixelmator Pro actions to 9.
Increase Resolution of Images
To learn more about using Automator, check out our tutorial on the topic. Thanks to these new actions, doing awesome things like this will now be much faster and easier.
As usual, this update also includes a number of smaller improvements and fixes. It’s free to download for every existing Pixelmator Pro user and you’ve probably already received it automatically. But, just in case you haven’t, head down to the Mac App Store to make sure you’re all up to date.
We’ll be back soon with some more news about upcoming Pixelmator Pro updates — we’ve got some new tools in the works (not ML-based this time) and some great improvements to the color picking UX that we just cannot wait to share with you. Until next time!
It’s no secret that we’re pretty big fans of machine learning and we love thinking of new and exciting ways to use it in Pixelmator Pro. Our latest ML-powered feature is called ML Super Resolution, released in today’s update, and it makes it possible to increase the resolution of images while keeping them stunningly sharp and detailed. Yes, zooming and enhancing images like they do in all those cheesy police dramas is now a reality!
Let’s see some examples
Before we get into the nitty-gritty technical stuff, let’s get right to the point and take a look at some examples of what ML Super Resolution can do. Until now, if you had opened up the Image menu and chosen Image Size, you would’ve found three image scaling algorithms — Bilinear, Lanczos (lan-tsosh, for anyone curious), and Nearest Neighbor, so we’ll compare our new algorithm to those three.
Note that the images below are zoomed in to 200% to make the changes easier to see, but you can zoom out to 100% by clicking the magnifying glass button.
Pretty incredible, right? Until now, if an image was too small to be used at its original resolution, either on the web or in print, there was no way to scale it up without introducing visible image defects like pixelation, blurriness, or ringing artifacts. Now, with ML Super Resolution, scaling up an image to three times its original resolution is no problem at all.
How does it all work?
As computers get ever more powerful, the additional power opens up new possibilities. One of the uses of machine learning, on a very fundamental level, is to make predictions about things. In this case, we gathered a set of images, scaled them down, and then ‘taught’ the algorithm to go from the scaled-down version to the original resolution, high-quality image, predicting the values of each new pixel. The algorithm can’t recreate detail that is too small to be visible but it can make amazing predictions about edges, shapes, contours, and patterns that traditional algorithms simply cannot.
Traditional approaches use (relatively) simple mathematics to interpolate the values of pixels when scaling images.
When adding new pixels, the most basic algorithm, Nearest Neighbor, simply takes the color of the closest neighboring pixel. This results in the classic blocky appearance because the previously imperceptibly small pixels are now big enough to be seen.
The Bilinear algorithm is a little more advanced. A texture map of the image is created according to an algorithm and the values of the 4 closest texels (texture elements) are used when recreating each pixel in the new image. The goal of this approach is to make the transition between pixels much smoother. However, when upscaling quite significantly (or upscaling small images) this algorithm creates the familiar blurry appearance.
Lanczos is yet more advanced, using a complicated mathematical formula to interpolate (another word for predict) the value of any newly created pixels while keeping edges as sharp as possible. Its main disadvantage is that, in its attempts to retain sharpness, the algorithm can sometimes create ringing artifacts. So, ultimately, it’s useful in certain specialized situations, but not much more.
The machine learning way
So, how does the machine learning approach work? Put simply, it takes into account the actual content of every image, attempting to recognize edges, patterns, and textures, recreating detail based on our dataset and extensive training. When upscaling, it can make much better predictions because a red pixel next to a blue pixel can be a completely different type of texture or edge in different images even though, to the primitive approaches, they’re always the same.
The ML Super Resolution convolutional neural network
To create the ML Super Resolution feature, we used a convolutional neural network. This type of deep neural network reduces raster images and their complex inter-pixel dependencies into a form that is easier to process (i.e. requires less computation) without losing important features (edges, patterns, colors, textures, gradients, and so on). The ML Super Resolution network includes 29 convolutional layers which scan the image and create an over-100-channel-deep version of it that contains a range of identified features. This is then upscaled, post-processed and turned back into a raster image. Below is a simplified representation of the neural network.
First, the input image is passed through a high pass filter for basic edge detection. Then, the first convolutional layer reduces the size of these features and pools the data. In the Descriptor Fusion block, the image is scanned to find any JPEG compression blocks within it and this is fused with the other features identified so far.
The next convolutional layers and residual blocks are where the magic happens — these detect the features (edges, patterns, colors, textures, gradients, and so on) in the image, building them up into a complex representation that is over 100 channels deep. In a convolutional neural network, more layers mean better accuracy but with a large enough number of layers, a network becomes near-impossible to train. Residual blocks are designed to increase the complexity and accuracy of networks without making them impossible to train.
Finally, all the features identified by the neural network are enlarged in the Enlarge block. After this, the two residual blocks and the final convolutional layer post-process the data and turn the features back into an image. It’s also important to note that all this happens on-device and the entire trained machine learning model is included inside the Pixelmator Pro app package.
Dealing with noise and artifacts
Small images often contain compression artifacts and noise. If we want our upscaled images to be usable, artifacts and noise shouldn’t be scaled up together with the actual contents of the image. In fact, if possible, they should be removed altogether. And, as mentioned above, ML Super Resolution is designed to do just that, borrowing some of the technologies we developed for ML Denoise to remove both camera noise and JPEG compression artifacts. By the way, in this update, ML Denoise has also been improved, bringing noise removal that is between 2 to 4 times better than before.
Naturally, the machine learning way requires a lot more processing power than the primitive approaches — between 8 to 62 thousand times more, in fact.
Total Floating Point Operations (FLOPs) per pixel*
ML Super Resolution
1,128,062 FLOPs (1.1 megaFLOPs)
* When upscaling 1 pixel by 300%, creating 9 pixels.
Making this available in an app like Pixelmator Pro has only become possible in the last couple of years — even on Mac computers from 5 or so years ago, ML Super Resolution can take minutes to process a single image due to slower performance and less available memory. On the latest hardware, however, images are processing in a few seconds, and even faster on iMac Pro, Mac Pro, or any Mac with multiple GPUs thanks to our use of Core ML 3 and its multi-GPU support. For the same reasons, the performance of ML Super Resolution is also significantly improved when using an eGPU.
Compute time with eGPU2
MacBook Pro (13-inch, Mid 2012)
MacBook Air (13-inch, 2018)
MacBook Pro (16-inch, 2019)
iMac Pro (2017)
1. For this test, a 300,000 pixel image was upscaled to three times its original size.
2. Tested using an AMD Radeon RX 5700 XT eGPU.
3. External GPUs require a Thunderbolt 3-equipped Mac.
Using the 2012 MacBook Pro as a baseline, the latest devices are up to 200x faster!
We’re incredibly excited about ML Super Resolution and we honestly hope you’re going to love it too. If you’d like to, you can download all the images in this blog post using the link below and test everything in today’s update out for yourself.
Pixelmator Pro 1.5.4 is now available from the Mac App Store, so head on down there and make sure you’re up to date. The trial version has also been updated so if you don’t yet have a copy, you’re welcome to try it out. That’s it for now, but we hope to surprise you with one more cool new feature before the year is up — stay tuned!
ML Super Resolution requires macOS Mojave or later.