---
title: 'High-Resolution Image Synthesis with Latent Diffusion Models'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is arXiv non-exclusive distribution license (verified on arXiv abs page 2026-11-11); this pointer note is our own words and is CC BY 4.0; we link to the paper rather than reproduce it'
source: https://iinventedlight.com/library/notes/2112-10752-latent-diffusion-models.md
source_license: 'arXiv non-exclusive distribution license'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer, High-Resolution Image Synthesis with Latent Diffusion Models (arXiv non-exclusive distribution license). This note links to the paper rather than reproducing it.'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Pointer note only. Paper not reproduced. Abs page and license checked 2026-11-11.'
category: arxiv
topic_area: 'ai-research: foundations'
tags: arxiv, diffusion, latent-models, image-generation
arxiv_id: 2112.10752
alias: 2112.10752-latent-diffusion-models
type: pointer
format: pointer
sources:
  - id: S1
    title: 'High-Resolution Image Synthesis with Latent Diffusion Models'
    publisher: arXiv
    authors: 'Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer'
    url: https://arxiv.org/abs/2112.10752
    version_read: 'v2 (13 Apr 2022)'
    accessed: 2026-11-11
    license: 'arXiv non-exclusive distribution license'
---
# High-Resolution Image Synthesis with Latent Diffusion Models

## Summary
[High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) (2021) made high-resolution diffusion practical by moving the expensive generative process into a compressed latent space. That design is the basis of Stable Diffusion and of many later open image models.

What it introduced: latent diffusion — train a diffusion model in the latent space of a pretrained autoencoder, with optional text (or other) conditioning — so that high-resolution synthesis no longer requires operating in raw pixel space.

What it led to: Stable Diffusion and the open image-generation ecosystem that followed. Live notes: [hf-diffusers-pipeline-loading](https://iinventedlight.com/library/notes/hf-diffusers-pipeline-loading/) and [hf-diffusers-memory](https://iinventedlight.com/library/notes/hf-diffusers-memory/). Read it next to the denoising-diffusion pointer in this batch, which is the pixel-space predecessor.

Where to read it: https://arxiv.org/abs/2112.10752 (v2, last revised 13 Apr 2022). CVPR 2022.

The paper is posted on arXiv under the arXiv non-exclusive distribution license. This note links to it rather than reproducing it. The note itself is CC BY 4.0.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).