Practical overview
When it fits
Use it when you want repository-level tools for training or applying learned artistic styles to image and video inputs, and can work with its Python, TensorFlow and media-processing requirements.
Who it is for
- Developers experimenting with neural style transfer in TensorFlow.
- Researchers or creative technologists who want to train or evaluate image-stylization networks.
- People exploring a frame-by-frame video stylization workflow.
Documented uses
- Train a style-transfer network using a chosen style image.
- Apply a trained checkpoint to an image or directory of images.
- Transform video frames with a selected style and combine them into a video.
Requirements & limits
- This is an implementation repository with scripts and models, not a hosted editing interface.
- Video transformation requires ffmpeg; the README also lists moviepy in its environment example.
- The README lists conflicting TensorFlow versions across its setup and requirements sections, so environment selection needs care.
- Research-use terms require attribution and retention of the copyright notice; commercial and other non-academic use requires contacting the author.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Evaluate a trained style-transfer model on images
Apply an existing style-transfer checkpoint to images and save the results to an output directory.
Before you start
- Python environment with the repository's required dependencies.
- A style-transfer checkpoint; the README links to models for evaluation.
- Input image or directory and an output directory.
Steps
Install the required project environment and run setup.sh as directed by the repository documentation.
Run evaluate.py to view its available parameters.
Run evaluate.py with a checkpoint, input path and output path, following the documented example.
Expected outcome
Stylized image results are written to the specified output location.
Guide limitations
- The README's environment guidance lists different TensorFlow versions in different sections; verify requirements against the code and documentation.
- The README reports several seconds per frame on CPU and 100 ms per frame at batch size 1 on a Maxwell Titan X; actual performance depends on hardware.
- The README describes research-use terms and requests contact for commercial or other non-academic use.
Prepare the documented video-stylization workflow
Set up the listed environment and prerequisites for the repository's video transformation script.
Before you start
- Anaconda environment on Windows or Linux.
- TensorFlow GPU 2.1.0, JupyterLab and moviepy 1.0.2 are listed in the environment example.
- ffmpeg is required for video stylization.
Steps
Create and activate the documented tf-gpu Conda environment with tensorflow-gpu=2.1.0.
Install JupyterLab and moviepy 1.0.2 as shown in the README.
Install ffmpeg, then consult the video documentation and run transform_video.py without assuming parameters beyond those documented there.
Expected outcome
The listed environment and video dependency are prepared for the repository's video-stylization workflow.
Guide limitations
- The README's general requirements list TensorFlow 0.11.0, which differs from its environment example's TensorFlow GPU 2.1.0.
- The documented setup example includes Windows/Linux, but the GPU example is specific to listed hardware and software.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- lengstrom/fast-style-transfer · Official homepage title · retained in its source language