Tagged “diffusion-models”

5 posts.

All Model Architecture19 Training & Alignment22 Inference & Serving18 Agents & Orchestration11 Reasoning & Evaluation26 Safety, Security & Governance7 Platforms & Practice20

Inference & Serving 21 min

Diffusion Language Models: Writing Text by Denoising, Not Predicting the Next Token

Autoregressive models write left to right, one token at a time. Diffusion language models reveal a whole sequence at once and sharpen it over a handful of steps. That single change rewrites the latency math, and in 2025 it stoppe…

diffusion-models llm parallel-decoding generative-ai ∑ ◫

Model Architecture 42 min

From Nano Banana to Vision Banana: How Google DeepMind Turned an Image Generator into a Generalist Vision System

Training a model to generate photorealistic images teaches it geometry, semantics, depth, and object relationships. Google DeepMind's Vision Banana proves that a lightweight instruction-tuning pass over an image generator can bea…

nano-banana vision-banana image-generation computer-vision ∑ ◫

Model Architecture 24 min

From Normalising Flows to Flow Matching: How the Change-of-Variables Idea Survived Its Own Architecture

For five years, normalising flows bent every layer around one number: the log-determinant of a Jacobian. Flow matching kept the invertible transport map, stopped computing that number during training, and ended up inside Stable D…

variational-and-flow-models flow-matching diffusion-models generative-ai ∑ ◫

Model Architecture 24 min

The Score Is All You Need: How Energy-Based Models, Langevin Dynamics and Diffusion Became One Theory

For decades the normalising constant made energy-based models nearly impossible to train at scale. Between 2019 and 2022 the field stopped computing it and learned its gradient instead, and score matching, Langevin sampling and d…

energy-based-and-score-models diffusion-models diffusion generative-ai ∑ ◫

Model Architecture 24 min

Why GANs Lost Image Generation, and Why Adversarial Losses Are Everywhere Anyway

In 2021 a diffusion model beat BigGAN-deep on ImageNet while nearly doubling its recall, and GANs stopped being the default way to generate images. Yet latent autoencoders, SDXL-Turbo, HiFi-GAN and the neural audio codecs behind …

adversarial-generative-models gans image-generation diffusion-models ∑ ◫