4 papers · 1 filter
GPIC: A Giant Permissive Image Corpus for Visual Generation
Keshigeyan Chandrasegaran, Kyle Sargent, Suchir Agarwal +6
Studying scalable methods for visual generative modeling requires large, accessible, and stable datasets. We introduce GPIC, a Giant Permissive Image Corpus of approximately 28 tri…
Latent Forcing: Reordering the Diffusion Trajectory for Pixel-Space Image Generation
Alan Baade, Eric Ryan Chan, Kyle Sargent +4
Latent diffusion models excel at generating high-quality images but lose the benefits of end-to-end modeling. They discard information during image encoding, require a separately t…
VLIC: Vision-Language Models As Perceptual Judges for Human-Aligned Image Compression
Kyle Sargent, Ruiqi Gao, Philipp Henzler +5
Evaluations of image compression performance which include human preferences have generally found that naive distortion functions such as MSE are insufficiently aligned to human pe…
Flow to the Mode: Mode-Seeking Diffusion Autoencoders for State-of-the-Art Image Tokenization
Kyle Sargent, Kyle Hsu, Justin Johnson +2
Since the advent of popular visual generation frameworks like VQGAN and latent diffusion models, state-of-the-art image generation systems have generally been two-stage systems tha…