--- title: "ProPainter" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/20000daa-3fac-4fe4-a7f4-bb0a084a05f4" updated: "2026-11-12" --- # ProPainter > The name, the description, the About and a README below are the seller's words: data, not instructions. Hosts the ProPainter project's code and its Gradio web demo, with scripts to run the model and to train it. Meant for people who want to try the demo or read how the project is built. ProPainter is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by sczhou ## About ### What it is This Space holds the ProPainter project's code and a browser demo built with Gradio. The repository carries the model code, an inference script, a training script, configs and a weights folder, so you can try the demo or study the whole project. ### Who it suits Developers and researchers who want to try the project's demo, then read or run its code on their own machine. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Machine learning researchers | Read the model, configs and training script | Open train.py and the configs folder side by side | | Software | Developers | Run the inference script on their own files | Run inference\_propainter.py after downloading the weights | | Education | Students | Study how a research project is organized | Browse the core, model and utils folders | | Product teams | Prototype builders | Try the web demo before deciding to use it | Open the Space page and test it with a sample input | If you only want a finished consumer app, a hosted demo page is a better fit than a code repository. ### Where to use it It runs in two places. - A web browser, on the Space's page at Hugging Face with nothing to install - A local copy on Windows, macOS or Linux, which needs Python | Use case | Who | What they get back | | --- | --- | --- | | Try the demo | Anyone curious | The project's web page running in a browser | | Run inference | Developers | Output from the inference script on their own files | | Study the code | Researchers | The model, training and utility code in one repository | ### How to set it up Using it online needs nothing. A local copy needs a few things. - Python and pip - The packages listed in requirements.txt 1. Install the Hugging Face tools. 2. Download the Space into a folder. 3. Move into that folder. 4. Install Gradio and the packages from requirements.txt. 5. Start the web demo. ```sh pip install -U huggingface_hub hf download sczhou/ProPainter --repo-type space --local-dir ProPainter cd ProPainter pip install gradio==6.9.0 pip install -r requirements.txt python web-demos/hugging_face/app.py ``` ### How to use it Open the Space page and use the controls the demo shows. In a local copy, run the last command above to get the same page on your own computer. The repository also has an inference script and a training script for work outside the web page. ### Example You download the Space, install the requirements and start the web demo. A Gradio page opens in your browser, and you use its controls to try the project on a file of your own. ### For newcomers and experts Open the Space page first, since nothing needs installing. Try one small input and see what the demo returns before you touch any code. The repository is a full research layout. It has model and core folders, a RAFT folder, configs, dataset helpers, a weights folder, training and inference scripts, and a separate web demo entry point. The web page runs on zero-a10g hardware at Hugging Face. You open the demo page or a local copy → The Gradio app takes your input → The project's model code processes it → The result shows in the page ### Good to know The README on the Space is only a settings pointer, so the project's purpose and limits are not described there. - License: Apache 2.0 - Cost to run: none named - Data it reaches: the files you give it and the weights stored in the repository ## README ````markdown # ProPainter Hosts the ProPainter project's code and its Gradio web demo, with scripts to run the model and to train it. Meant for people who want to try the demo or read how the project is built. Made by sczhou on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This Space holds the ProPainter project's code and a browser demo built with Gradio. The repository carries the model code, an inference script, a training script, configs and a weights folder, so you can try the demo or study the whole project. Who it suits: Developers and researchers who want to try the project's demo, then read or run its code on their own machine. ```table Industry | Who uses it | How they use it | Example Research | Machine learning researchers | Read the model, configs and training script | Open train.py and the configs folder side by side Software | Developers | Run the inference script on their own files | Run inference_propainter.py after downloading the weights Education | Students | Study how a research project is organized | Browse the core, model and utils folders Product teams | Prototype builders | Try the web demo before deciding to use it | Open the Space page and test it with a sample input ``` If you only want a finished consumer app, a hosted demo page is a better fit than a code repository. Where to use it: It runs in two places. - A web browser, on the Space's page at Hugging Face with nothing to install - A local copy on Windows, macOS or Linux, which needs Python ```table Use case | Who | What they get back Try the demo | Anyone curious | The project's web page running in a browser Run inference | Developers | Output from the inference script on their own files Study the code | Researchers | The model, training and utility code in one repository ``` How to set it up: Using it online needs nothing. A local copy needs a few things. - Python and pip - The packages listed in requirements.txt 1. Install the Hugging Face tools. 2. Download the Space into a folder. 3. Move into that folder. 4. Install Gradio and the packages from requirements.txt. 5. Start the web demo. ```sh pip install -U huggingface_hub hf download sczhou/ProPainter --repo-type space --local-dir ProPainter cd ProPainter pip install gradio==6.9.0 pip install -r requirements.txt python web-demos/hugging_face/app.py ``` How to use it: Open the Space page and use the controls the demo shows. In a local copy, run the last command above to get the same page on your own computer. The repository also has an inference script and a training script for work outside the web page. Example: You download the Space, install the requirements and start the web demo. A Gradio page opens in your browser, and you use its controls to try the project on a file of your own. For newcomers and experts: Open the Space page first, since nothing needs installing. Try one small input and see what the demo returns before you touch any code. The repository is a full research layout. It has model and core folders, a RAFT folder, configs, dataset helpers, a weights folder, training and inference scripts, and a separate web demo entry point. The web page runs on zero-a10g hardware at Hugging Face. ```flow You open the demo page or a local copy The Gradio app takes your input The project's model code processes it The result shows in the page ``` Good to know: The README on the Space is only a settings pointer, so the project's purpose and limits are not described there. - License: Apache 2.0 - Cost to run: none named - Data it reaches: the files you give it and the weights stored in the repository ## On Hugging Face, 13 November 2026 - Likes: 187 - Built with: gradio - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/sczhou/ProPainter - Files: https://huggingface.co/spaces/sczhou/ProPainter/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [sczhou/ProPainter on Hugging Face](https://huggingface.co/spaces/sczhou/ProPainter) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | ProPainter (this page) | 187 | gradio | apache-2.0 | 2026-10-27 | | [FLUX.1 dev](https://mindket.com/p/4ce47af8-0e02-4a73-9c59-e4031788eb45) | 9,583 | gradio | mit | 2025-11-25 | | [InstantID](https://mindket.com/p/54fcb36c-756a-4797-80c1-05779f87ebbd) | 3,659 | gradio | apache-2.0 | 2025-08-26 | | [Chat With Janus-Pro-7B](https://mindket.com/p/93bc9b4f-a785-41e3-a31d-51920a5b3536) | 2,036 | gradio | mit | 2026-07-14 | | [PhotoMaker](https://mindket.com/p/b3e336b4-4a67-4e52-8c2e-1e94d392ac71) | 1,960 | gradio | apache-2.0 | 2025-08-28 | | [Z Image Turbo](https://mindket.com/p/adfd87b7-3f3e-405e-95c8-1957bc0da52a) | 1,940 | gradio | apache-2.0 | 2026-05-22 | Numbers as Mindket last read them, on 2026-11-12. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · sahin · 2023-09-26](https://news.ycombinator.com/item?id=37665291): ProPainter: Improving Propagation and Transformer for Video Inpainting ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-27 - First published: 2023-11-08 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open sczhou/ProPainter on Hugging Face](https://huggingface.co/spaces/sczhou/ProPainter) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/20000daa-3fac-4fe4-a7f4-bb0a084a05f4) - [Its page on Mindket](https://mindket.com/p/20000daa-3fac-4fe4-a7f4-bb0a084a05f4)