diffusion-models
Many AI-generated images cannot be traced to any training example News
MIT researchers trained 24 diffusion models and measured how far you could perturb the training set before an output changed, finding that many generated images have no identifiable source in the training data at all, and that this gets more common as datasets grow.
A diffusion model picks its answer a fifth of the way through News
Researchers logged every token commitment in a masked diffusion language model and found it locks in the final answer 15 to 24 percent of the way through generation, while half the reasoning is still blank -- so the visible reasoning is written around a frozen conclusion.
DistillAlign explains why fast video models get prettier and more repetitive at the same time News
A new preprint shows that the standard technique for speeding up video generators optimises a mathematical objective that deliberately concentrates on safe, common outputs, and that adding a diversity-preserving loss back during that stage more than doubled the paper's coverage measure without giving up the quality gain.
Diffusion Distillation Lesson
Diffusion distillation trains a fast student model to reproduce in a handful of steps what a slow diffusion model does in dozens, cutting image and video generation from many neural-network passes down to one to four, which is what makes real-time generative rendering possible.
A video generator, repurposed as a perception model, matches specialists with up to 500x less data News
GenCeption repurposes a pre-trained video generative diffusion model as a feed-forward perception system, matching specialist vision models on depth, surface normals, pose and segmentation while using 7x to 500x less training data - and generalizing from synthetic-only training to real footage.