#diffusion-models

DİFÜZYON MODELLERİ-II

Part I’de alegorik olarak “difüzyon” nedir ondan bahsetmiştim.

Diffusion Language Models Explained: From Theory to Training Pipeline

Think of solving a jigsaw puzzle. Traditional language models place one puzzle piece after another until the picture is complete. Diffusion…

Two Towers, One Breakthrough: How NVIDIA Made Diffusion Language Models Fast Without Sacrificing…

Inside Nemotron Labs TwoTower, an architecture that clones a pretrained model into a frozen memory tower and a trainable generator to…

Diffusion U-Net — Architecture and Building Blocks

The Reign of Diffusion Models is Nearing Its End: Why They Will Soon Be Replaced as the Primary…

If we evaluate the essence of diffusion models with cold objectivity, they do not actually “paint” images. Rather, they function as highly…

What exactly is a Diffusion Model?

We slowly drown one number in noise, then teach a tiny network to name that noise and walk back to the original. That is the entire engine…

Day 11: Diffusion Models — How AI Dreams in Pictures and Video

For the first ten days of this challenge, we focused heavily on text. We learned how AI reads, searches, and writes. But over the last two…

DiffusionGemma : Non-Sequential Block Denoising Inside Open Model

Introduction

Optimizing Images for the Brain: From fMRI Encoding Models to Brain-Guided Generation

In standard neural encoding, we train a model that maps an image to predicted brain activity. But once this model is differentiable, we can…

Are Weight-Generating Models Actually Learning, or Just Memorizing?

There’s a direction that’s been getting a lot of attention lately: using generative models to directly produce neural network weights. A…

Discover Shocking Truths

How Text-to-Image Diffusion Models Work: The Full Pipeline

Type “a ferret reading a book by lantern light” into Stable Diffusion and, a few seconds later, it returns an image that did not previously…

Deriving DDIM Reverse Sampling from First Principles, with a TensorFlow Implementation

A math-first walkthrough of DDPM/DDIM sampling, velocity prediction, and CIFAR-10 experiments in TensorFlow/Keras.

Text Diffusion Model? A Toy Implementation of Embedding-Space Diffusion!!

misc/Text_Diffusion at main · GitGhidorah/misc

Diffusion Model Predictive Control (D-MPC)

Receding-Horizon Control with Diffusion-Based World Models

Explain diffusion models used in image generation.

Diffusion models are a type of generative AI model that create images by learning how to gradually remove noise from random data until a…

Google’s Revolutionary DiffusionGemma

For a decade, large language models (LLMs) have been locked into a single, unyielding paradigm: autoregressive generation. The model…

Paper Insights: DIFF4SPLAT: Repurposing Video Diffusion Models for Dynamic Scene Generation

I recently came across a GitHub repository with many papers on physically plausible video generation, and I found this paper. This paper…

Why Diffusion Models don’t Memorize.

In our Explore NeurIPS’s Best 2025, we explore the paper, Why Diffusion Models Don’t Memorize.

How Image Generation Actually Works

The real process behind Midjourney, Gemini, FLUX, and ChatGPT

Diffusion Models: From Noise to Art

A Deep Dive into Denoising Diffusion Probabilistic Models (DDPM)

DiT: How Transformers Are Replacing U-Nets in Diffusion Models

Replacing U-Nets with Vision Transformers for high-quality, compute-scalable image generation

How I learned diffusion models faster with AI coding

From tiny 2D implementations to a clearer mental model of diffusion models

DiffusionBlocks: Finally Understanding the Skeleton Argument

DiffusionBlocks(4)

DiffusionBlocks: Here is where I find it confusing.

Sakana AI has published a great explanation, so if you want to understand the paper, please check it out. If you’d like to share some…

Mindcraft: Text-Conditioned Infinite Worlds

Infinite worlds are super fun to explore. In particular, I have always loved walking around Minecraft’s endless worlds, where I can find…

Why ADVIDS integrates Stable Video Diffusion directly into our production pipeline for fine-tuned…

There is a fundamental tension in professional creative production between flexibility and consistency. You want tools that can do…

Statistical Evaluation Metrics for Generative AI: Assessing Synthetic Tabular Data, Text…

Generative AI models play a substantial role in generating content such as text, image, and synthetic tabular data. The quality of the…

Top-Tier Video Models Cost Millions. Open-Sora Built One for $200K.

An 11B open-source model now goes toe-to-toe with 30B giants. The secret isn’t a clever new architecture — it’s what they didn’t build…

DiT Is Not an Image GPT

Diffusion Transformers learn how noisy images move toward structure.

The Core of DiffusionBlocks: Why Block-Independent Training Works

Taught to me by AI (Claude)

Energy-Based Transformers

What the architecture inherits from classical energy models, and where that leaves it

I Finally Learned How AI Generates Images (And It’s Not Magic)

Moving past prompt engineering: My deep dive into the Hugging Face Diffusion Models Course.

What Surprised Me While Learning Diffusion Models

Initially, when I learned about the diffusion model concept, I thought of nothing but yet another form of neural networks like CNN and GAN…

Explained: Rectified Flow for Everyone

Fundamentals behind diffusion-based text-to-image AI models

How AI Learned to Style Hair: The Tech Evolution Behind Virtual Try-Ons (And What’s Next)

From minute-long GAN optimizations to real-time, language-aware diffusion engines. Here’s how academic research finally became a product…

DiffusionBlocks: Why I Couldn’t Understand It

https://arxiv.org/abs/2506.14202 DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation

Virtual Try-On -Everything You Need to Know and Should be thinking about

Why Virtual Try-On is One of Fashion’s Most Consequential Technology Right Now

Why GPT’s image generator keeps giving you the same picture

Image generators do not invent imagery. They sample a learned distribution, and the distribution has gravity wells. What reads on r/ChatGPT…

Overview of Reinforcement Learning Methods for Enhancing VLA Robot Training

The Vision Language Action (VLA) Models have gained tremendous attention in the past couple of years, thanks to the great progress made in…

The Mathematics of Imagination: How Robots Learn to Think in Distributions

Physical AI Field Notes — Episode 3 | From an Enterprise AI Architect’s Notebook

Revolutionizing Real-Time Video Generation: Meet Q-ARVD, The Quantization Breakthrough for…

How researchers solved the unique challenges of quantizing autoregressive video diffusion models to make real-time AI video practical.

Diffusion Models Explained (How AI Generates An Image)

I am sure you created the Ghibli style image using image generating AI models.

Diffusion Bee jetzt installieren: So gelingt der Start auf dem Mac

Diffusion Bee ist eine pragmatische Mac-App für lokale Bildgenerierung auf Basis von Stable Diffusion. Der große Vorteil liegt darin, dass…

Why AI Images Look Real But They Aren’t

The AI-generated look strange because reality is harder than appearance. Most discussions about these images focus on visual mistakes.

I Finally Understood Research After Getting a 75/100

This semester, I worked on a machine learning project that taught me more than any class ever has. Not a tutorial. Not a Kaggle notebook…

Continuous Diffusion Language Models Were Held Back by a Habit, Not a Limitation

A new MIT paper argues the gap between continuous and discrete diffusion language models came from an unexamined design choice — pulling…

Deep Generative Models: From Noise to Structure

I have been watching the CS236 Stanford lectures on YouTube, and this post is a structured set of notes on deep generative models, with a…

To Understand Diffusion Models, One Must Realize That All Images Fit Onto Thin Origami in…

It feels like natural images in the world are infinite — and indeed they are — but that infinity is actually of a ‘lower tier.’ It is…

DFlash: Diffusion Couldn’t Beat Transformers. So It Made Them 6x Faster.

A new paper from University of California San Diego’s Z Lab turns block diffusion into a drafting engine that accelerates autoregressive…