Introducing Paris

Optimal and Diffusion Transports in Machine Learning

Slides for a general introduction to the use of Optimal Transport methods in learning, with an emphasis on diffusion models, flow matching, training 2 l…

What are Diffusion Models? | IBM

Diffusion models are generative models that “diffuse” training data with random noise, then learn to reverse the diffusion process to output new images.

DiffusionGemma as Jev: Open-Source vLLM Patch (2026) | explainx.ai Blog

A vLLM contributor built an open-source Jev clone using DiffusionGemma — roughly matching TypeSafe's proprietary model on accuracy.

Computational OT #4 - Gradient flow and diffusion models

A course on computational Optimal Transport

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.

Diffusion model

In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable generative models. A diffusion model consists of two major components: the forward diffusion process, and the reverse sampling process.[1] The goal of diffusion models is to learn a diffusion process for a given dataset, such that the process can generate new elements that are distributed similarly as the original dataset. A diffusion model models data as generated by a diffusion process, whereby a new datum performs a random walk with drift through the space of all possible data.[2] A trained diffusion model can be sampled in many ways, with different efficiency and quality.

Some notes on the Stable Diffusion safety filter

How deep learning models are moderated

darkshapes/divisor

Divisor: Interactive diffusion framework

Research – Inception

We are leveraging diffusion technology to develop a new generation of LLMs. Our dLLMs are much faster and more efficient than traditional autoregressive LLMs.

How Open Weights Are Changing the AI Race | Serverless Guru

Open Weight models: what they are, why you should care, and how the work of the open-source fine-tuning community around those models is reshaping daily life, both for developers and for everyday AI users.

Blacksky Algorithms - Open Collective

Decentralized social media built for community power, culture, and collective freedom.

Introducing Mercury 2.5 – Inception

Mercury 2.5 is the most capable diffusion LLM on the market. It runs at 1,107 tokens/sec and offers a 40% increase in intelligence over Mercury 2, comparable to cost-optimized frontier models.