Landmark 4 stops to get here · leads to 3

Image Generation

Creating new images from scratch or from text descriptions using generative models (GANs, diffusion models, VAEs).

Your route here

4 stops · basics first
  1. 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.

  2. 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.

  3. Deep Learning ✓ understood

    A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.

  4. Computer Vision ✓ understood

    The field of AI that gets computers to extract meaning from images and video: what is in them, where it is, and how it moves.

  5. Image Generation · you are here ✓ understood

Picture it

GAN

  • Generator turns random noise into an image
  • Discriminator tries to spot the fakes
  • One forward pass per image

Diffusion model

  • Starts from pure noise
  • Removes noise step by step
  • Text prompt guides each step

VAE

  • Encoder compresses images to a latent space
  • Sample a point in that latent space
  • Decoder turns it back into an image
Notice every approach starts from randomness and learns to shape it into an image; they differ in how that shaping is learned.

Where it sits

Before this

Computer Vision
Image Generation

Leads to

Image Inpainting Style Transfer Super-Resolution

Explore nearby

Vision & Multimodal Diffusion Model A generative model that learns to denoise data, achieving state-of-the-art image generation (Stable Diffusion, DALL-E 2). 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 Stable Diffusion A latent diffusion model for text-to-image generation that operates in compressed latent space for efficiency.

In the research

All papers →

9 papers that build on Image Generation ; showing 5, canon first.

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 · Nov 2025 · 206 citations Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer It shows state-of-the-art image generation does not require “scale at all costs”. Frontier · Nov 2025 · 197 citations SAM 3D: 3Dfy Anything in Images Breaking the 3D data bottleneck makes single-photo 3D practical for real images. Frontier · Aug 2025 · 90 citations Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning More stable RL for image generators, plus a better yardstick for them.