The simulation visualizes a 2D probability landscape as a vector field of score arrows, showing how particles injected with Gaussian noise drift and jitter along the score gradient until their positions settle into the true shape of the underlying distribution rather than piling up at a single peak.
Choose a target distribution, drag the step-size (epsilon) and noise-scale sliders to see how drift versus jitter changes the trajectories, then press play to release a cloud of random particles and watch annealed Langevin steps sculpt them into the distribution's shape.
Target distribution select, step-size (epsilon) slider, noise-scale slider, particle count, play/pause, rebuild
The equations were first written down in 1908 by Paul Langevin to explain Brownian motion, the random jiggling of pollen grains in water — more than a century later, the same drift-plus-noise mathematics became the sampling engine inside image-generating diffusion models like Stable Diffusion.
The simulation visualizes a 2D probability landscape as a vector field of score arrows, showing how particles injected with Gaussian noise drift and jitter along the score gradient until their positions settle into the true shape of the underlying distribution rather than piling up at a single peak.
The simulation visualizes a 2D probability landscape as a vector field of score arrows, showing how particles injected with Gaussian noise drift and jitter along the score gradient until their positions settle into the true shape of the underlying distribution rather than piling up at a single peak.
Choose a target distribution, drag the step-size (epsilon) and noise-scale sliders to see how drift versus jitter changes the trajectories, then press play to release a cloud of random particles and watch annealed Langevin steps sculpt them into the distribution's shape.
The equations were first written down in 1908 by Paul Langevin to explain Brownian motion, the random jiggling of pollen grains in water — more than a century later, the same drift-plus-noise mathematics became the sampling engine inside image-generating diffusion models like Stable Diffusion.