Machine learning-based image reconstruction has evolved into a genuinely game-changing technology – and what sets this apart from other PC features is that users can effectively mod improved versions of Nvidia DLSS and Intel XeSS into games with existing support, simply by swapping a .DLL file in the install directories. With that in mind, we wanted to use this unofficial modding technique to give a preview of sorts for the latest versions of DLSS and XeSS, to see what has changed.
The headlines are straightforward enough: The DLSS SDK has been updated to version 3.7 with a new reconstruction model called ‘Model E’, while XeSS has transitioned to version 1.3, promising greater quality and stability. We’ve covered DLSS across the years, but it’s been some time since we first looked at XeSS prior to its launch. Back then, my conclusions were that XeSS when running on an Intel GPU produced quality similar to DLSS with only a few shortcomings, and did not suffer from issues that we have typically seen with FSR 2. However, XeSS’s use of machine learning is interesting as it has multiple versions: it occupies a middle place between what AMD and Nvidia are doing. There’s a full on ML implementation for its own XMX ML hardware, along with a DP4a path that allows the majority of modern GPUs to enjoy the benefits, with a small hit to quality
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And as XeSS has evolved, users of non-RTX graphics cards have discovered that the DP4a path, while heavier, has clear quality advantages over AMD’s own FSR 2. However, at the same time, the simplified nature of the DP4a version means that few people have actually seen XeSS at its best. Based on my tests using Horizon Forbidden West, the hardware-based XMX version wins in quality on a few levels, but the biggest visible one is that particles do not have incessant trails following them like they have in the DP4a version. However, by swapping the existing XeSS .DLL file with the latest, there’s clear improvement in this area. This is the sort of reporting that works best in the video format, so I entreat you to check out the embedded content below.
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