Divisor: Interactive diffusion framework
How Stable Diffusion works
Understand in a simple way how Stable Diffusion transforms a few words into a spectacular image.
Complete guide to samplers in Stable Diffusion
Dive into the world of Stable Diffusion samplers and unlock the potential of image generation.
Stanford CS25: V5 I Transformers in Diffusion Models for Image Generation and Beyond
The physics behind diffusion models
Observing Information Diffusion Structure in Japanese Bluesky: A 48-Hour Path-Tracking Experiment - Nightflight
mlx-examples/stable_diffusion at main · ml-explore/mlx-examples
Examples in the MLX framework. Contribute to ml-explore/mlx-examples development by creating an account on GitHub.
Continuous diffusion language models
Fully discrete methods dominated for a few years, but language models based on continuous diffusion are making a comeback.
Continuous diffusion language models
Fully discrete methods dominated for a few years, but language models based on continuous diffusion are making a comeback.
Compositing & Blending • Niklas Gadermann
Exploring the math and intuition behind blend modes in the browser
What Is Bluesky?—A Landscape Revealed by the Diffusion Experiment - Nightflight
Twitter Rival Bluesky Has a Nudes Problem
In its chaotic early days, the platform’s algorithm shared naked pictures in its What’s Hot feed.
Evaluating and Sampling Glinty NDFs in Constant Time
Geometric features between the micro and macro scales produce an expressive family of visual effects grouped under the term 'glints'. Efficiently rendering these effects amounts to finding the highlights caused by the geometry under each pixel. To allow for fast rendering, we represent our faceted geometry as a 4D point process on an implicit multiscale grid, designed to efficiently find the facets most likely to cause a highlight. The facets' normals are generated to match a given micro-facet normal distribution such as Trowbridge-Reitz (GGX) or Beckmann, to which our model converges under increasing surface area. Our method is simple to implement, memory-and-precomputation-free, allows for importance sampling and covers a wide range of different appearances such as anisotropic as well as individually colored particles. We provide a base implementation as a standalone fragment shader.
drift/CONTRIBUTING.md at main · darkshapes/drift
Autonomic decentralized peer-to-peer deep learning - darkshapes/drift