When you purchase through links in our articles, we may earn a small commission. Nvidia's DLSS 5 makes NBA 2K27 look incredibly real, which is kind of the problem NBA 2K27 shows off DLSS 5's impressive new realism -- and hints at a future where animation, not graphics, becomes gaming's weakest link. That sounds weird, but hear me out. Nvidia showed me and other journalists an early preview of 2K's NBA 2K27 ahead of the game's September 4 launch, because NBA 2K27 will also mark the debut of Nvidia's DLSS 5 technology, which applies AI models to rendered game scenes to drastically improve realism. It's the little things that sell it. Shadows fell naturally across Haliburton's hand as it gripped the ball.

We demonstrate the abilities of the proposed paradigm for image deblurring, super-resolution, and compressive sensing.

We study the problem of posterior sampling using pretrained discrete diffusion foundation models, aiming to recover images from noisy measurements without retraining task-specific models. While diffusion models have achieved remarkable success in generative modeling, most advances rely on continuous Gaussian diffusion. In contrast, discrete diffusion offers a unified framework for jointly modeling categorical data such as text and images. Beyond unification, discrete diffusion provides faster inference, finer control, and principled training-free Bayesian inference, making it particularly well-suited for posterior sampling. However, existing approaches to discrete diffusion posterior sampling face severe challenges: derivative-free guidance yields sparse signals, continuous relaxations limit applicability, and split Gibbs samplers suffer from the curse of dimensionality. To overcome these limitations, we introduce Anchored Posterior Sampling (APS) for masked diffusion foundation models, built on two key innovations -- quantized expectation for gradient-like guidance in discrete embedding space, and anchored remasking for adaptive decoding. Our approach achieves state-of-the-art performance among discrete diffusion samplers across linear and nonlinear inverse problems on the standard benchmarks. We further demonstrate the benefits of our approach in training-free stylization and text-guided editing.

In our main paper, we describe our methods based on the "V ariance Exploding" hyperparameters Therefore, we can use "V ariance Preserving" Note that although the inference algorithms are shown to be equivalent, the choice between "V ariance Preserving" and "V ariance Exploding" may affect the training of diffusion networks. The proof uses a basic property of Gaussian marginals (see [ 4 ] for the complete version). In denoising, the corrupted image is the original image with additive white Gaussian noise. Equation 23 is the SVD of H . The grayscale image is obtained by averaging the red, green, and blue channels of each pixel.

I don't use a Windows Copilot PC as a daily driver, though I have several in my office. But there's one absolutely critical Copilot feature that forces me to swap out my current laptop, attach a Copilot PC to my docking station, and boot it up. Very few people have bought a Copilot PC in the last year. So these features, which are currently locked to Copilot PCs and their NPU, aren't well known: Windows Recall; Paint's Cocreator, Generative Erase, Object Select, and Sticker Generator; Click-to-Do; Photos' Super Resolution, Relight and Restyle Image; the intelligent search features within the Settings menu; Windows Studio Effects; and Live Captions. My editor assumed I would prefer the last feature, Live Captions, probably because it's both useful and cool.