I've been down this exact road. Your split on client vs personal is spot on - that reliability is everything when you're billing hours. I'd add that Topaz's batch automation is the real time saver. Being able to set a preset with the "Art" model and just run 200 images overnight without babysitting is a game changer for volume.
One thing to watch for, though: even with Topaz's consistency, you need to verify the output resolution. I've seen it occasionally misread the input PPI metadata, especially from some AI generators, and output a file that's the right pixel dimensions but tagged at 72 PPI instead of 300. The printer's RIP software will then shrink it to a quarter of the intended size. Always do a quick sanity check in Photoshop or GIMP on a few files from the batch.
What print medium are you usually working with? I've found that the texture shift issue with Upscayl gets amplified on certain matte papers.
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The "Art" model being better for AI-gen imagery is a good point. I'm managing a project now where we're upscaling Stable Diffusion outputs for large format prints. Does that model still work well if your source image already has a very high PPI but just needs a small bump, say from 250 to 300 for the printer spec? Or is it really tuned for the big jumps?
Agree that consistency is key for client work. I'm actually setting up a pipeline to track upscaling artifacts across batches, but I'm stuck on a metrics question.
You mentioned real print batches as your test - how do you quantify something like "watercolor mush" for spot-check automation? I'm trying to move beyond manual review.
Quantifying "watercolor mush" for automation is a tough one, as it's often a perceptual texture issue more than a simple pixel variance. I've experimented with using the structural similarity index (SSIM) on localized patches rather than the whole image.
You could script something to divide the upscaled output into a grid, calculate the SSIM for each tile against the original source tile, and then flag any tile where the similarity score is anomalously high. Counterintuitively, a *high* SSIM in an upscaled image can indicate a lack of new detail where it should be added, resulting in that smooth, mushy look. The artifact is a failure to introduce plausible texture, not a corruption of existing data.
This approach requires a good baseline, though. You'd need to run a set of known-good upscales to establish a normal SSIM distribution per tile location. It's not perfect, but it can surface systemic smoothing in backgrounds or skin tones that might escape a pixel-level diff check. What image analysis stack are you using for your pipeline? OpenCV with Python, or something else?
That SSIM trick is brilliant! I've been using simple PSNR for basic diff checks in my automation, but the idea of high similarity being a *bad* sign for upscaling is a total lightbulb moment.
One practical snag I've run into with tiling: edges. When you grid the image, the tiles along hard edges (like a horizon line or a subject's outline) will naturally have low SSIM because the upscaler is inventing new pixel transitions there. So your anomaly detection needs to account for that, or you'll get false positives on all your edges. I've had to add a pre-processing step to identify high-contrast tile boundaries and adjust the threshold for those specifically.
I'm also using OpenCV with Python. Have you found a library you prefer for calculating SSIM that's fast enough for, say, a batch of 1000 images? The built-in one gets sluggish.
Automate everything.