AI: How does ai draw? Stable diffusion? (2023)

how does ai generate images.

how does ai generate images.

(ai answer) 2026

AI image generation (like DALL·E, Midjourney, Stable Diffusion, Flux, Grok's own image model, etc.) is mostly powered by diffusion models today. Here's a clear, step-by-step explanation of how it actually works.

1. The Core Idea: Learning from Noise

The modern approach is called diffusion (specifically, denoising diffusion probabilistic models or DDPMs).

• Training phase (done once, on millions of images):

  1. Take a real image.
  2. Gradually add Gaussian noise to it over hundreds or thousands of tiny steps until it becomes pure random noise. This is the "forward process."
  3. The neural network is trained to predict and remove that noise at each step — essentially learning to reverse the corruption.

• Generation phase (what happens when you type a prompt):

  1. Start with pure random noise.
  2. Run the model in reverse: at each step, it predicts what noise to remove to make the image slightly clearer and more coherent.
  3. After 20–100 steps (depending on the model), the noise turns into a recognizable, high-quality image that matches your prompt.

This process is why images appear to "emerge" from chaos — it's literally denoising step by step.

2. How Text Gets Connected to Images (Text-to-Image)

Raw diffusion alone doesn't understand English. So models add these components:

• Text encoder (usually CLIP, T5, or similar): Converts your prompt ("a cyberpunk cat wearing sunglasses") into a rich numerical representation called an embedding.
• Conditioning: The diffusion model uses this text embedding at every denoising step to guide the image toward what the text describes.
• Latent space (used in Stable Diffusion, SDXL, Flux, etc.): Instead of working on full 1024×1024 pixel images (very expensive), the model works in a compressed "latent" representation (much smaller). A separate autoencoder compresses/decompresses between latent and pixel space.

3. Architecture Under the Hood

Most modern models use:

• U-Net or Transformer-based backbones (like DiT — Diffusion Transformers) for the denoising network.
• Attention mechanisms (same idea as in ChatGPT) so the model can focus on different parts of the image and different parts of the prompt simultaneously.
• Cross-attention: This is what lets the text influence specific regions (e.g., "red hat" affects only the head area).

Evolution of Techniques

Quick Analogy

Imagine teaching someone to draw by:

1. Showing them thousands of photos and slowly covering them with static (noise).
2. Training them to always clean up the static correctly.
3. Then giving them pure static + a description ("draw a dragon") and having them gradually clean it up while constantly checking against the description.

That's basically what the AI does.

Other Methods (Less Common Now)

• GANs (Generative Adversarial Networks): Two networks fight — one generates fakes, one tries to spot fakes. Very sharp results but hard to control with text.
• VAEs (Variational Autoencoders): Good for compression and simple generation but blurry.
• Flow Matching / Consistency Models: Newer, faster alternatives to traditional diffusion.

Practical Note

When you use Grok's image generation (powered by Grok Imagine / Black Forest Labs tech or similar), your prompt gets encoded, fed into a huge diffusion transformer, and it denoises in latent space before decoding to pixels. The whole process usually takes 5–30 seconds depending on resolution and steps.

Would you like a deeper dive into any part (math of diffusion, how controlnets/LoRAs work, why some models are better at hands/faces, or even a simple code demo of the noise process)?

• Stable Diffusion

doesn't stable difusion just mean a general technique? or is there

Dreyfus 01/14/2023 9:35 PM

• diffusion is a general technique. "stable diffusion" is the network structure of network created by a company named StabilityAl.
• they are popular because they trained a network (cost millions of dollars), which can run on consumer GPU hardware (not need supercomputer), and released it publicly for everyone to use.
• then people have created fine-tuned models that are good at anime porn, that's what NovelAl is.
• the blog I posted is how to setup the NovelAl network using the GUI that works with all these types of network

• I use this python web GUI:
• seems to work with cuda gpu acceleration, very fast on my gpu, doesn't use as much vram as a few months ago.
• here is a guide that i used to set everything up:
• https://rentry.org/nai-speedrun