Latent Diffusion Models Explained: How Stable Diffusion, SDXL, SD3 & Flux Generate Images

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Издатель Jun 4, 2026

Latent Diffusion Models are the foundation behind Stable Diffusion, SDXL, SD3, Flux, and many modern image generation systems.

Instead of generating high-resolution images directly, these models compress images into a smaller latent space using a Variational Autoencoder, then train a diffusion model to denoise that compressed representation. This makes image generation faster, cheaper, and practical enough to run on consumer hardware.

In this video, we break down how latent diffusion works, why VAEs matter, how text prompts guide image generation through cross-attention, and why newer models are moving from U-Net architectures to Diffusion Transformers.

You will learn:

↳ How Stable Diffusion uses latent space
↳ Why VAEs reduce compute cost
↳ How diffusion denoising works
↳ What CFG does during generation
↳ How cross-attention connects text and images
↳ Why SDXL, SD3, and Flux use more scalable architectures
↳ How quantization helps run large models locally

This is a practical technical guide for anyone learning generative AI, diffusion models, multimodal AI, or modern image generation systems.

How Stable Diffusion Really Works: Latent Diffusion, VAEs, CFG & Cross-Attention

Latent Diffusion Explained: The Architecture Behind Stable Diffusion and Flux

From Stable Diffusion to Flux: The Evolution of Latent Diffusion Models