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 ArleyArt.com offers wall decor with unique presentations at reasonable prices. All artwork is printed to order on heavyweight satin (semi-gloss) photo paper, inserted into a 100% archival safe, acid-free clear sleeve, and a flat mailer or rolled if the poster is larger than 11x14". The prints are carefully packaged to ensure safe delivery. Each print is ready-to-frame, and items are usually shipped within 1-2 business days of payment confirmation. Personalized and custom photo items may take longer. ArleyArt prints are available in sizes 8x10", 11x14", and 13x18", though not all products are available in all sizes. The images used in each product are Arley's own work, in the public domain, licensed, or used with permission. Custom posters can be created from your favorite quote or athlete

 Someone with a stronger will, more dedication, time, and a technical mindset could squeeze better results out of some of the methods in this study. For instance, selecting a correct Upscaler model can drastically affect the upscaling outcome. But going through lists of literally hundreds of them, let alone downloading, installing, and waiting between ten to ninety minutes to see each result—that's a task for a dedicated research institute. :) Instead, I went with the most common models and (mostly) default values in most cases.

 All the initial files were sharpened in Lightroom before sending them to upscalers. After LR export (HQ PNG), each file was ~9 Mb and 1024×1536px.

 For printing, keeping files as PNG isn't necessary—JPEG with lowest compression works perfectly fine. But I kept the initial file format to make the experiments more consistent.

 I ran all tests (except for Google Collab that works in the cloud) on 2013 Mac Pro with the following configuration:

 My upscaler of choice for many good reasons—Gigapixel AI—is an all-in-one AI-based upscaler with a buy-once-own-forever license. When you launch the app, it offers to download the most recent upscaler models. Afterward, it will regularly download updates and occasionally add new models.

 Gigapixel AI offers a minimalist and clean interface. Every control is in its place, and the overall experience is straightforward and intuitive.

 You drag and drop your images to Gigapixel AI's window. They appear as a list with parameters for each position (that can be changed individually or for all images simultaneously).

 A handy Auto mode lets Gigapixel AI decide which model best applies to your images and with what parameters. Or you can set everything manually. That includes Suppress noise, Remove blur, Fix compression, Face Recovery, and Gamma Correction.

 To help you with these choices, the central part of the screen is occupied by a before-after comparison preview. You can zoom in and out and drag the loupe across your image. The preview only runs the selected upscaler model on the zoomed part of the image, so it works faster than complete upscale—to show how this particular fragment will look after you launch the upscaler.

 In this comparison, Gigapixel AI upscales are put against original images resized to have the same resolution. In all the following tests, I will be comparing the results from the upscaler in question to those of Gigapixel AI.

 Also, I won't be posting all results for each upscaler. You can download all fragments via the link at the end of this study.

 Gigapixel offers several built-in AI models (see specs below). However, I found the Standard mode the most useful, delivering the best results in each benchmark position. Here is a comparison of Lines vs Art & CG vs Standard models with Line Art test.

 PNG file size: at ~130Mb per image, Gigapixel AI is top of the list at this, too. However, with the use of a fantastic free tool ImageOptim↗︎ I could reduce the file size to 40Mb with 90% lossy optimization and to around 25Mb with 80%.

 Gigapixel AI is capable of upscaling to up to 6 times the original size—and you can set it to an even higher multiplier, but after that, "the quality is not guaranteed."

 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.

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 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.

 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).

 There 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.