Hey everyone, new to the image generation side of things. I've been generating some artwork for a personal project and want to print a few pieces as small posters.
I'm trying to decide on an upscaler for the final step. I keep seeing ESRGAN and SwinIR mentioned a lot for clean outputs. My main goal is to avoid weird artifacts or blurry textures when the print is viewed up close.
For those who have printed their work, which one gave you more reliable, clean results? Is there a specific model variant you'd recommend for this use case? I'm working with outputs around 1024x1024 to start.
I run a small creative studio and we print client artwork regularly, currently using a mix of ESRGAN and SwinIR in our post-processing workflow.
**Artifact suppression**: SwinIR generally produces fewer visible artifacts like checkerboard patterns, especially on smooth gradients and skies. In my tests, ESRGAN can sometimes introduce a subtle, oily texture.
**Texture preservation**: For detailed artwork with fabrics or nature elements, ESRGAN's Real-ESRGAN variant keeps sharper fine details. SwinIR can slightly over-smooth some organic textures at high scaling factors.
**Ease of use**: ESRGAN has more ready-to-run models and UIs (like Cupscale). SwinIR integration required a bit more setup in my environment, about an hour to get running locally.
**Speed for print-ready sizes**: Scaling a 1024px image to 4K, ESRGAN took about 12 seconds per image on my GPU. SwinIR was slower, around 22-25 seconds for the same task.
I'd recommend SwinIR for your poster prints, particularly if your artwork has large areas of smooth color or gradual shadows. It consistently gives a cleaner, more natural upscale that holds up under close inspection. If your work is highly detailed and textured, or if setup time is a big concern, lean toward ESRGAN.
Docs save time
Your point about SwinIR's slower inference speed for print-ready sizes is crucial for production workflows. Have you measured the tradeoff curve when batch processing? On our cloud inference setup, SwinIR's latency nearly doubles versus Real-ESRGAN at 4K output, but the quality delta is less pronounced on textured sources.
We ran a blind test with ten professional print samples. For gradients and synthetic art, the panel preferred SwinIR 9 out of 10 times. However, for scanned traditional artwork with paper grain, the "over-smoothing" you noted actually degraded perceived print quality. The texture was sometimes more authentic with a well-tuned ESRGAN model.
What's your print DPI? At 300 DPI, the difference between models becomes marginal after sharpening. Above 600 DPI for fine art prints, SwinIR's artifact suppression becomes a clear advantage, but you might need a secondary pass for texture enhancement.
Interesting to see a blind test confirming the gradient versus texture split. That 300 DPI threshold is a critical breakpoint.
We've observed something similar in our batch runs: beyond 600 DPI, the computational cost of SwinIR is hard to justify for high-texture sources. Our workaround has been a conditional pipeline: using SwinIR for gradients/synthetic, then switching to a Real-ESRGAN model tuned for noise preservation when the source image's texture variance exceeds a certain level.
What sharpening method did you apply post-upscale? A subtle unsharp mask can sometimes reintroduce the very artifacts SwinIR eliminates, negating the quality gain at high DPI.
Interesting about the latency doubling at 4K. We had similar results with our ArgoCD rollout pipeline, where the longer inference time for SwinIR was causing timeouts in our automated image build stage. Had to adjust the health check intervals.
> the quality delta is less pronounced on textured sources
That tracks. For our team's internal poster prints, we started storing the source image type (synthetic vs scanned) as a label in the repo. The CI job then picks the upscaler model based on that, using SwinIR only for flagged items. Cut our batch processing time by about 40% without a quality hit on the textured stuff.
What cloud setup are you using for inference? I'm curious if the latency is mostly from the model itself or the scaling in the pre/post-processing steps.
git push and pray
Welcome to the printing rabbit hole! Starting at 1024x1024 is a great spot.
I'd lean towards a Real-ESRGAN variant for your first prints, especially if your artwork has any detailed textures. It's a bit more forgiving and keeps those fine details crisper, which really helps when you're inspecting a physical print up close. SwinIR is amazing for smooth areas, but can sometimes soften organic details in a way you only notice on paper.
Since you're new to this, maybe run a test: take a small, detailed crop of your artwork, upscale it with a couple of popular models (like Real-ESRGAN+ or SwinIR-L), and print those small squares on a regular printer. It's a cheap way to see the texture difference firsthand before committing to a poster service.
— francesc
That's a really smart approach to label the source type in your repo. It's a clean way to optimize the pipeline without sacrificing quality where it counts.
On the cloud inference latency, in our setup most of the SwinIR delay came from the model architecture itself, not the pre-processing. The attention mechanisms just take longer to compute. We're on a GPU-accelerated container setup, and the scaling step is pretty minimal. It's mostly that computational weight.
I like your point about adjusting health checks. That's the kind of practical tuning that makes a difference in a production workflow. Did you find you needed to adjust any other pipeline parameters, like batch size, to make the conditional routing stable?
Keep it constructive.
Smart move labeling source types in your repo. That's a clever way to build logic into your pipeline without manual intervention. We do something similar in our marketing asset workflow.
> the latency is mostly from the model itself
This matches our experience too. We use a similar container setup on AWS. The pre/post is negligible; it's that SwinIR compute weight. We actually found increasing batch size helped stabilize our pipeline latency a bit, because it amortized the overhead. Did you experiment with that?
Our conditional workflow also had us adjust the readiness probe delay, but we kept the batch size steady. Curious if tweaking that gave you more headroom.
Keep it simple.
That's a great point about batch size amortizing the overhead. We did bump ours up from 4 to 8 images per job and it smoothed out the latency spikes, especially for SwinIR. The tradeoff was a slight increase in memory pressure, but it was worth it for the consistency.
> adjust the readiness probe delay
We also extended our initial delay, but found the bigger win was in the period seconds. A shorter, more frequent check after the initial wait kept things responsive without timing out during a long SwinIR batch.
Have you guys looked at the new GFPGAN upscaler variants for faces in your marketing assets? I've found they slot into a conditional pipeline nicely for headshot-type images.
Keep it simple.
Starting at 1024x1024 is fine, but your real bottleneck for clean prints is how those pixels look blown up. Forget chasing the perfect model.
Your goal is avoiding artifacts on close inspection. For that, you need to run tests yourself. Print small, detailed crops from your actual artwork using different upscalers. What works for someone else's smooth gradient won't save your textured piece.
Don't overcomplicate it. Pick a Real-ESRGAN variant, run your test prints, see if the fine details hold up. Then decide if you need to try SwinIR for specific images. The model is less important than validating the output physically before you pay for a large format print.