What is fastphotostyle?

nvidia/fastphotostyle — explained in plain English

Analysis updated 2026-06-24

★ 11,181PythonAudience · researcherComplexity · 3/5LicenseSetup · moderate

Mindmap

mindmap
  root((fastphotostyle))
    What it does
      Photo style transfer
      Photorealistic output
      Blends two images
    Inputs
      Content photo
      Style photo
    Tech Stack
      Python
      PyTorch 0.4.0
    Use Cases
      Photography editing
      Creative projects
      Research reproduction
    Audience
      AI researchers
      Computer vision devs

mindmap root((fastphotostyle)) What it does Photo style transfer Photorealistic output Blends two images Inputs Content photo Style photo Tech Stack Python PyTorch 0.4.0 Use Cases Photography editing Creative projects Research reproduction Audience AI researchers Computer vision devs

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What do people build with it?

USE CASE 1

Transfer the golden-hour lighting and color palette from a reference photo onto your own daytime photograph.

USE CASE 2

Apply the visual style of a specific movie still or artwork to a set of your own photos for creative projects.

USE CASE 3

Reproduce or extend the photorealistic style transfer research from the ECCV 2018 paper in your own experiments.

What is it built with?

PythonPyTorch

How does it compare?

	nvidia/fastphotostyle	nvlabs/stylegan2	the-pr-agent/pr-agent
Stars	11,181	11,185	11,189
Language	Python	Python	Python
Setup difficulty	moderate	hard	moderate
Complexity	3/5	5/5	3/5
Audience	researcher	researcher	developer

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 1h+

Requires PyTorch 0.4.0 specifically, a separate older release exists for PyTorch 0.3.0, and modern PyTorch versions are not supported.

Free for non-commercial research use with attribution, any work built on it must carry the same non-commercial license terms.

So what is it?

FastPhotoStyle is a Python project from NVIDIA that transfers the visual style of one photograph onto another photograph. You give it two images: a content photo (the scene you want to keep) and a style photo (the look you want to borrow), and it produces a result that blends the content of the first with the colors, tones, and textures of the second. The goal is photorealistic output, meaning the result should look like a real photo rather than an obvious digital painting. The technique was developed by researchers from NVIDIA and UC Merced and published at the ECCV 2018 conference. The project README is brief and points to a separate tutorial file for three different ways to use the algorithm. The code is written in Python and was built using PyTorch, a widely used machine learning framework. The README notes it was updated to work with PyTorch 0.4.0, with an older release available for users on version 0.3.0. The license is CC BY-NC-SA 4.0, which allows non-commercial use with attribution and requires that any work built on top of it carries the same license terms. Commercial use is not permitted under this license.

Copy-paste prompts

Prompt 1

Using NVIDIA's FastPhotoStyle with PyTorch 0.4.0, transfer the style of style.jpg onto content.jpg and save the result, show me the exact command from the tutorial.

Prompt 2

I have a content photo and a style photo. Walk me through installing FastPhotoStyle's dependencies and running the style transfer pipeline end to end.

Prompt 3

How does FastPhotoStyle differ from neural style transfer, what makes the output photorealistic rather than painterly? Explain based on the ECCV 2018 paper it references.

Prompt 4

I want to batch-process 50 content photos with the same style reference using FastPhotoStyle, how do I write a loop around the main command?

Frequently asked questions