Landmark 2 stops to get here · leads to 1
Diffusion Model
A generative model that learns to denoise data, achieving state-of-the-art image generation (Stable Diffusion, DALL-E 2).
Your route here
2 stops · basics first
- Machine Learning ✓ understood
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
- Neural Network ✓ understood
A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.
- Diffusion Model · you are here ✓ understood
Picture it
Forward (noising)
- Fixed, no learning
- Add a little Gaussian noise
- Repeat until pure noise
- Makes training examples
Reverse (denoising)
- Learned neural network
- Predict and remove noise
- Repeat step by step
- Pure noise becomes an image
Where it sits
Explore nearby
Vision & Multimodal Stable Diffusion A latent diffusion model for text-to-image generation that operates in compressed latent space for efficiency. Vision & Multimodal Image Generation Creating new images from scratch or from text descriptions using generative models (GANs, diffusion models, VAEs). Vision & Multimodal Generative Adversarial Network A framework where two networks (generator and discriminator) compete, with the generator learning to create realistic data. Neural Networks Variational Autoencoder A generative model that learns a probabilistic latent space, allowing sampling of new data points similar to training data. Vision & Multimodal U-Net A CNN architecture with encoder-decoder structure and skip connections, widely used for image segmentation tasks.
In the research
All papers →20 papers that build on Diffusion Model ; showing 5, canon first.
Canon · 2020 Denoising Diffusion Probabilistic Models DDPM is the recipe behind Stable Diffusion, DALL·E, Midjourney and today’s video models. 
Canon · 2021 High-Resolution Image Synthesis with Latent Diffusion Models It’s the architecture behind Stable Diffusion, which put high-quality text-to-image generation on ordinary GPUs. 
Frontier · Aug 2025 · 875 citations Qwen-Image Technical Report Legible, editable text in generated images was a long-standing weakness; this open model largely fixes it. 
Frontier · Sep 2025 · 226 citations Seedream 4.0: Toward Next-generation Multimodal Image Generation It set the bar for commercial image generation and editing in one fast system. 
Frontier · Oct 2025 · 221 citations Diffusion Transformers with Representation Autoencoders It argues the generation and understanding sides of vision should share one latent space.