3 SD Pipeline How it Works

vladmandic edited this page 2026-02-11 11:15:50 +01:00

Table of Contents

• Stable Diffusion Pipeline

Stable Diffusion Pipeline

This is probably the best end-to-end semi-technical article:
https://stable-diffusion-art.com/how-stable-diffusion-work/

And a detailed look at diffusion process: https://towardsdatascience.com/understanding-diffusion-probabilistic-models-dpms-1940329d6048

But this is a short look at the pipeline:

1. Encoder / Conditioning Text (via tokenizer) or image (via vision model) to semantic map
   (e.g CLiP text encoder)
2. Sampler Generate noise which is starting point to map to content
   (e.g. k_lms)
3. Diffuser Create vector content based on resolved noise + semantic map
   (e.g. actual stable diffusion checkpoint)
4. Autoencoder Maps between latent and pixel space (actually creates images from vectors)
   (e.g. typically some image-database trained GAN)
5. Denoising Get meaningful images from pixel signatures
   Basically, blends what autoencoder inserted using information from diffuser
   (e.g. U-NET)
6. Loop and repeat From step#3 with cross-attention to blend results
7. Run additional models as needed
   • Upscale (e.g. ESRGAN)

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