AI Search
June 2, 2026
Pixel Diffusion: Free 4K Image Upscaler by Nvidia
TL;DR
Nvidia's Pixel Diffusion (PD) is a free, open-source AI model that upscales images to 4K resolution in under 5 seconds, delivering sharper details than competitors like RealESRGAN while remaining lightweight and fast.
Key Takeaways
- 1Pixel Diffusion works in pixel space rather than latent space, producing sharper 4K results with fewer artifacts than traditional upscaling methods
- 2The model is significantly faster and lighter than competitors—upscaling images 5.9x faster than RealESRGAN while being only 2.7 GB
- 3You can run PD locally for free using Comfy UI with three main workflows: image upscaling, image generation with upscaling, and text-to-image generation
- 4Three model variants are available based on base generators (Flux, Stable Diffusion 3), with options to upscale 512→2K or 1K→4K resolutions
- 5The entire setup requires downloading text encoders (Gemma 2 or Quen), VAE models, and the PD diffusion model, with installation guided through Comfy UI
Notable Quotes
“Pixel Diffusion because it works in what is called pixel space. You see, how normal image generators work is they use a decoder that turns the image data from a compressed latent space back into pixels that you and I can see. But how this PD works is that it does the decoding in pixel space instead.”
“This is definitely one of the best and fastest methods for you to generate 2K or 4K resolution images.”
“It only took less than 10 seconds to generate an image using Zephyr Image. And then for the upscaler, it's even faster. This only took like 3 seconds.”
Chapters
1. Introduction & Demo Examples
Overview of Pixel Diffusion as a state-of-the-art open-source model for 4K image generation. Live demonstrations showing before-and-after upscaling of tiger, cityscape, portrait, and night sky images, highlighting dramatic improvements in detail and sharpness.
2. How Pixel Diffusion Works
Technical explanation of the pixel space approach versus latent space decoding used by traditional image generators. Comparison with competitors like RealESRGAN (RealESRGAN) showing PD's superior consistency, sharpness, and faithfulness to source material details.
3. Performance Metrics & Comparison
Analysis of latency and win rates comparing Pixel Diffusion to other upscaling methods. PD achieves upscaling in less than one second and wins in the majority of comparisons, proving its efficiency and quality superiority.
4. Installation & Setup Prerequisites
Introduction to Comfy UI as the platform for running open-source image generators offline. Steps to update Comfy UI to the latest version and accessing the three available workflows from the provided GitHub page.
5. Workflow 1: Image Upscaling
Detailed walkthrough of uploading an existing image and upscaling it to 2K or 4K using Pixel Diffusion. Installation of Gemma 2B text encoder, PD diffusion model variants, and VAE, with dimension adjustment for 1K→4K conversion.
6. Workflow 2: Image Generation + Upscaling
Complete tutorial on using Zephyr Image Turbo to generate an image first, then piping it through Pixel Diffusion for upscaling. Configuration of prompts, resolution settings, sampler parameters (seed, steps, CFG), and comparison nodes for before/after visualization.
7. Workflow 3: Text-to-Image Generation
Overview of the DIT text-to-image model generating 1K resolution images directly from prompts. Noted as less impressive than alternatives like Zephyr Image or Flux, suitable mainly as an alternative rather than primary use case.
8. Performance & Practical Recommendations
Summary of PD's speed and efficiency (image generation in under 10 seconds, upscaling in 3 seconds). Recommendations for ideal workflows: upscaling existing images or generating with Zephyr Image then upscaling, rather than using text-to-image alone.
9. Sponsor: Higsfield
Overview of Higsfield as an all-in-one AI creation platform featuring video generation (Coherence 2.0), image creation (GPT Image 2), and workflow tools like Supercomputer, Marketing Studio, and Cinema Studio 3.5 for content creators.
Key People & Entities
- Nvidia
- Developer of Pixel Diffusion, an open-source state-of-the-art image upscaling model
- Comfy UI
- Popular open-source platform for running AI image and video generators locally
- Zephyr Image / Zephyr Image Turbo
- State-of-the-art image generator model compatible with Pixel Diffusion for generation + upscaling workflows
- Flux / Flux 2 / Flux Klein
- Advanced image generation models that can be used as alternatives to Zephyr Image in PD workflows
- RealESRGAN / RealESRGAN 2
- Previous leading image upscaling method; outperformed by Pixel Diffusion in speed and quality comparisons
- Stable Diffusion 3
- Image generation model compatible with Pixel Diffusion for text-to-image generation workflows
- Higsfield
- All-in-one AI creation platform featuring video generation, image creation, and workflow tools for content creators (video sponsor)
Glossary
- Pixel Diffusion (PD)
- Nvidia's open-source AI model that upscales images to 4K resolution by working in pixel space rather than latent space, enabling faster generation with fewer artifacts
- Latent Space
- A compressed representation of image data used by traditional generators; requires decoding to convert back to visible pixels
- Pixel Space
- Direct representation of image data as pixels; used by Pixel Diffusion to denoise images at high resolution without latent encoding/decoding
- Comfy UI
- A popular open-source platform for running image and video generators offline, supporting various AI models through node-based workflows
- VAE (Variational Autoencoder)
- A model component that encodes and decodes image data; used in Comfy UI workflows to handle image format conversions
- Text Encoder
- A model that converts text prompts into numerical representations that AI image generators can understand (e.g., Gemma 2, Quen 34B)
- CFG (Classifier-Free Guidance)
- A parameter controlling how strictly the AI follows the input prompt; higher values = more literal adherence
- Sampler
- The algorithm used to generate images through iterative denoising steps (e.g., Euler sampler)
- Seed
- A starting point value for image generation; identical settings with different seeds produce varied but related outputs