From 512×512 to 1024×1024: How Latent Diffusion Broke the Resolution Barrier
How Latent Space solved the memory explosion problem of pixel-space diffusion. Complete analysis from VAE compression to Stable Diffusion architecture.

Latent Diffusion Models: The Core Principles of Stable Diffusion
TL;DR: Latent Diffusion performs diffusion in a compressed latent space instead of pixel space. This made high-resolution image generation practically possible and became the foundation technology for Stable Diffusion.
1. Why Latent Space?
1.1 The Problem with Pixel Space
Limitations of DDPM/DDIM:
Related Posts

Models & Algorithms
TurboQuant in Practice — KV Cache Compression with llama.cpp and HuggingFace
Build llama.cpp with turbo3, HuggingFace integration, memory calculator, config guide. 536K context on 70B models.

Models & Algorithms
TurboQuant Explained — Google's Extreme KV Cache Compression Algorithm
Compress KV cache to 3-bit with PolarQuant + Lloyd-Max. 4.6x memory savings with zero accuracy loss, no retraining.

Models & Algorithms
Qwen 3.5 Fine-Tuning Practical Guide — Build Your Own Model with LoRA
Complete guide to fine-tuning Qwen 3.5 with LoRA/QLoRA. From 8GB GPU QLoRA setup to Unsloth optimization, GGUF conversion, and Ollama deployment.
