Complete guide to samplers in Stable Diffusion

How Stable Diffusion works

Understand in a simple way how Stable Diffusion transforms a few words into a spectacular image.

Diffusion Models: A Comprehensive Survey of Methods and Applications

Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule...

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.

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.

Z-Image - Efficient Image Generation with Single-Stream Diffusion

Z-Image: A powerful and efficient 6B-parameter image generation model with support for bilingual instructions and fast inference.

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.

darkshapes/divisor

Divisor: Interactive diffusion framework

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.

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…

Computational OT #4 - Gradient flow and diffusion models

A course on computational Optimal Transport

Some notes on the Stable Diffusion safety filter

How deep learning models are moderated

The Curse of Recursion: Training on Generated Data Makes Models Forget

Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT introduced such...

antirez/iris.c

Flux 2 image generation model pure C inference