After trying alternatives, my choice was clear: I was ready to pay for a clean design, intuitive user experience, almost no waiting time, and overall convenience.
Most importantly: Gigapixel AI didn't make me think. I don't want to lose time and energy struggling with tons of settings, lines of code, and unexpected errors. I want the infrastructural software to
Super Zoom is a neural filter in Adobe Photoshop that uses artificial intelligence to—as its name suggests—increase the resolution of an image. The feature uses deep learning to analyze the details of an image and generate new pixels for smooth upscaling.
SuperZoom has an additional option called "Enhance face details." Let's check what it does: we have a benchmark exactly for that.
A decent enhancement, especially in the hair! Let's see how Super Zoom with Face enhancement compares to Gigapixel AI with Standard model.
It's integrated into Adobe Photoshop, so if you already have a subscription that includes PS, it comes as a bundle. And not just the Super Zoom, but other neural filters, too.
There is a trick to making a low-resolution image appear better. You need to first upscale it—and even a medium-quality upscaler will do—and then downscale it back to its original size. For this and many other situations when you don't intend to print your upscale or won't need a high-resolution file with fine details—Photoshop Super Zoom is a good choice. Especially, if you are already a user of Adobe infrastructure.
Google Colab is a free online platform that allows users to write and run code in a web browser. It also provides access to powerful computing resources, such as GPUs, for running complex machine-learning models. Numerous colabs can upscale images (and not only that).
For this test, I chose SuperRes Diffusion—batch upscaling and super-resolution colab based on Latent-Diffusion. If you decide to try it yourself, I recommend this short and comprehensive article on how to use it.
Despite some tech-savviness, setting up the SuperRes Diffusion colab was a bit of a struggle, and using it was overall a confusing experience. I encountered numerous glitches along the way. It stopped in the middle of a script's execution or showed errors, after some of which I had to restart the whole thing.
To run SuperRes or similar colabs, you must be a Google Drive user AND grant a colab—someone else's program—access to your Drive's entire content: with modification rights. I am a trusting (or just reckless ;)) person, but for many, this can be a privacy concern.
I'll be honest, the colab world is very new to me, and my first user experience with it wasn't smooth. Even though using SuperRes turned out to be easier than it seemed at the beginning, the environment doesn't make a non-software engineer feel comfortable. And occasional errors that explain nothing (e.g., "Cannot read properties of undefined (reading 'next'") don't help, too.
Then there is also a GPU time limit for free users that is difficult to control and runs out too fast.
In the end, I am sure colabs are powerful and versatile tools for those advanced enough to be able to use them. But in the case of SuperRes Diffusion, its speed and quality of its results couldn't outweigh its disadvantages.
chaiNNer is "a flowchart/node-based image processing GUI aimed at making chaining image processing tasks (especially upscaling done by neural networks) easy, intuitive, and customizable."
chaiNNer is a powerful tool to do so many things that upscaling becomes a fraction of its functionality. It is a spaceship! This is fantastic if you're into space traveling but overwhelming when you need a drive to a local supermarket. 8)
After downloading and installing chaiNNer, you will need to also download and set up the libraries you want to use. Upscale.wiki offers a massive list! There are hundreds of them: from universal-purposed models to very specifically-targeted ones (VHS restoration, Super Mario textures upscaling, models trained on coins, or cats, etc.). They have descriptions, but not all have examples of their outcomes. So you might have to choose from a few dozen of seemingly similar entries.
chaiNNer's interface is nicely designed, but it might scare an unprepared beginner. Especially if this is your first time working with node-based software. The principle, however, is pretty simple. There is a canvas where you arrange cards that represent inputs (like your image), actions (like applying an upscaler model), and outputs (like saving the result). By setting/dragging links between those cards, you define the workflow.
With chaiNNer I ran all tests using three different models: 4x Valar (for realistic photos), Face-Ality V1 (trained on faces), and Digipaint (an upscaler for digital art).
Posters For Every RoomThere is a learning curve to start with chaiNNer. Its interface is well-designed and pleasant to use, but the whole thing might be overwhelming for an unprepared user.
chaiNNer runs on your computer, utilizing its resources. If your machine is not advanced enough, that might result in waiting hours for even one upscale.
chaiNNer is a powerful tool with features going way beyond simple upscaling. And open databases of free AI models are its infinite fuel source. With some learning and practicing, chaiNNer might become an impactful addition to your toolbox.
With its lightweight interface (maybe not the best one aesthetically ]:->), Upscayl offers a straightforward and fast user experience while utilizing advanced AI models and producing a decent result.
You can use the same models as Upscayl with chaiNNer, where they are only 1/100th of its powers. and that gives much more versatility in configuration.
Upscayl is a minimalist AND free tool that works! It has a small variety of preset models and almost no settings you can tweak. So simplicity and availability are definitely its strong points. It's not as versatile as chaiNNer, but it's definitely a better fit for someone who look for a more effortless and accessible instrument.
For the final part of this test, I decided to see how Gigapixel AI upscales will behave on paper—with a particular focus on fine art prints. Each sample was printed at 300DPI on A3 size (297 mm × 420 mm, or 11.7 in × 16.5 in.)
For the final part of this test, I decided to see how Gigapixel AI upscales will behave on paper—with a particular focus on fine art prints. Each sample was printed at 300DPI on A3 size (297 mm × 420 mm, or 11.7 in × 16.5 in.)
Printing enthusiasts and professionals know: you can emphasize any image's strong sides or conceal its shortcomings with the right choice of medium and printing technique.For this test I went with:
Gigapixel AI showed excellent results even on the most demanding paper. All prints turned out perfectly detailed, without artifacts or pixelation. A3 is a perfect size for both selling prints directly or exhibiting them at a gallery show (or on your wall!). And Gigapixel upscales absolutely shined as A3 pictures. However, my impression is that you can go at least 1,5x bigger without losing quality.
In this study, I tried to show specific tools but also outline the ways you can go if you are serious about upscaling your images. Whether you are ready to buy yourself a headache-free ready-made solution, or you will settle with a simpler free tool, or you will go into node-based programming and simply coding—there are plenty of options that await you on all those paths.
We can’t deny the fact that the latest smartphone cameras have made some remarkable achievements in developing a decent landscape photography image quality, especially if these images are only being used on social media sites. Although there are a lot of innovative algorithms that create landscape photography images that look similar to those from a DSLR on screen, have you ever tried to create a fine art print from your smartphone or your DSLR, only to be disappointed with the results?
There are many factors that affect how well smartphone or camera images print. So, before we get into the steps to getting colors right for fine art prints, let’s look at a few important steps needed to capture the image correctly before the printing stage (the first two steps below).
An image file format describes how data related to the image is stored and most importantly, how much data is retained. Landscape photography image data can be stored in compressed and uncompressed format. Each format has its advantages and disadvantages.
As a landscape photographer, you probably shoot in Raw or Jpeg, or sometimes both. And then you usually edit the image in Photoshop, Adobe Camera Raw, or Lightroom. Then you save your image. How do you save it? Do you save as a PSD, Tiff. or Jpeg?
JPEG format is meant for transmitting photos over networks and showing on screens. It sheds a lot of the (unrecoverable) original data that was captured by your camera. In this format, the camera processes the appearance of the photo; your scope for further manual editing remains but is greatly reduced. When using a JPEG, I always use the highest quality setting for saving the file.
For fine art prints I use a TIFF file format to save all my travel or landscape photos. You will also want to save your work as a Photoshop PSD or a TIFF file format when you have Photoshop layers that you want to preserve. These formats allow for uncompressed saving with no loss of data and are preferred for high quality fine art prints – especially large ones.
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