Examples in the MLX framework. Contribute to ml-explore/mlx-examples development by creating an account on GitHub.
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.
Release v0.29.0 · ml-explore/mlx
Highlights Support for mxfp4 quantization (Metal, CPU) More performance improvements, bug fixes, features in CUDA backend mx.distributed supports NCCL back-end for CUDA What's Changed [CUDA]...
huggingface/ml-intern
🤗 ml-intern: an open-source ML engineer that reads papers, trains models, and ships ML models
How Stable Diffusion works
Understand in a simple way how Stable Diffusion transforms a few words into a spectacular image.
The physics behind diffusion models
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.
Awni Hannun on Twitter / X
The latest mlx-lm is out and it has continuous batching with mlx_lm.server! Added by @angeloskath Check-out the video of 4 simultaneous requests running with Qwen3 30B on the same M2 Ultra: https://t.co/o9sFC3k4DN— Awni Hannun (@awnihannun) December 3, 2025
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.
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…
Improving LM Studio's MLX Engine for Agentic Workflows
mlx-engine v1.8.5 dramatically improves performance for repeated, long-context agentic workflows by checkpointing your KV cache.
Unsloth AI on Twitter / X
Introducing Unsloth Desktop 🦥The first desktop app to run and train models locally.• Open-source. Runs on Mac, Windows and Linux• Supports MLX, diffusion image/video, audio, GGUF• Connect Claude Code and Codex to local LLMs• 50% more accurate, self-healing tool calls +… pic.twitter.com/vjTFB1e5IQ— Unsloth AI (@UnslothAI) August 11, 2026
Complete guide to samplers in Stable Diffusion
Dive into the world of Stable Diffusion samplers and unlock the potential of image generation.
Some notes on the Stable Diffusion safety filter
How deep learning models are moderated
