Mindket

Image Upscaler

Newversion 1 · updated

Overview

Processes images with a set of Phips models on a Gradio page, with model names that point to JPEG, H264 and noise clean-up. For people who want cleaner, sharper pictures in the browser.

Image Upscaler is a kit in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page, a zip of its files. Nothing in it runs by itself.

At a glance

You need
  • Python and pip
  • Python packages from requirements.txt
  • Windows, macOS or Linux
Runs in
  • A web browser, on the Space's page at Hugging Face, which runs on ZeroGPU
  • Your own Windows, macOS or Linux computer with Python
Start with
pip install -U huggingface_hub
hf download Phips/Upscaler --repo-type space --local-dir Upscaler
cd Upscaler

Platform

WindowsOperating system
macOSOperating system
LinuxOperating system

About

What it is

Image Upscaler is a Gradio page from Phips for processing pictures with image models. It names six models, and their names mention JPEG, H264 and noise clean-up. You can open it in the browser with nothing to install.

Who it suits

It suits people who want to improve a compressed or noisy picture, and developers who want to see how a small image model demo is built.

IndustryWho uses itHow they use itExample
PhotographyPhotographersClean up compressed or noisy photosUpload a heavily compressed JPEG
MediaVideo editorsImprove a still taken from compressed videoTry a frame grab
DesignDesignersTidy a low quality source pictureUpload a small web image
SoftwarePython developersRead a compact Gradio demo for image modelsLook through app.py

Skip it if you need controls and documentation, since the source gives no README text.

Where to use it

  • A web browser, on the Space's page at Hugging Face, which runs on ZeroGPU
  • Your own Windows, macOS or Linux computer with Python
Use caseWhoWhat they get back
Clean up a compressed picturePhotographersA processed version of the image
Try several models on one imageResearchersResults from the named models to compare
Study the demoDevelopersA small Gradio app of a few files

How to set it up

The online page needs nothing. A local copy needs Python and the packages in requirements.txt.

  • Python and pip
  • Python packages from requirements.txt
  • Windows, macOS or Linux
  1. Install the Hugging Face hub library.
  2. Download the Space's files into a folder named Upscaler.
  3. Move into that folder.
  4. Install Gradio 4.44.1, then the packages from requirements.txt.
  5. Start app.py.
pip install -U huggingface_hub
hf download Phips/Upscaler --repo-type space --local-dir Upscaler
cd Upscaler
pip install gradio==4.44.1
pip install -r requirements.txt
python app.py

How to use it

Open the Space's page, or run the last command above for a local copy and open the address it prints. Give the page an image and let it process it.

python app.py

Example

You open the page and load the sample picture that ships with the repository, input_example1.png. The page processes it and shows you the result next to what you gave it.

For newcomers and experts

Start with the sample picture in the repository, so you can see what the page does before you try your own file.

The app is built with Gradio 4.44.1, and its main file is app.py. The Hugging Face copy runs on ZeroGPU hardware. The six model names carry a 1x prefix, and their parts mention realplksr, HAT, OmniSR and SRFormer. The record gives no README, so what each model does is for you to confirm by trying it or reading the code.

  1. Open the page
  2. Give it an image
  3. A model runs on a ZeroGPU
  4. Look at the result

Good to know

  • Hardware: The Hugging Face copy runs on ZeroGPU hardware.
  • Gaps: The source has no README, so model roles and limits are not described here.
  • License: apache-2.
  • Cost to run: none named.
  • Data it reaches: The images you give it.

README and files

README

Image Upscaler

Processes images with a set of Phips models on a Gradio page, with model names that point to JPEG, H264 and noise clean-up. For people who want cleaner, sharper pictures in the browser.

Made by Phips 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: Image Upscaler is a Gradio page from Phips for processing pictures with image models. It names six models, and their names mention JPEG, H264 and noise clean-up. You can open it in the browser with nothing to install.

Who it suits: It suits people who want to improve a compressed or noisy picture, and developers who want to see how a small image model demo is built.

Industry | Who uses it | How they use it | Example
Photography | Photographers | Clean up compressed or noisy photos | Upload a heavily compressed JPEG
Media | Video editors | Improve a still taken from compressed video | Try a frame grab
Design | Designers | Tidy a low quality source picture | Upload a small web image
Software | Python developers | Read a compact Gradio demo for image models | Look through app.py

Skip it if you need controls and documentation, since the source gives no README text.

Where to use it:

  • A web browser, on the Space's page at Hugging Face, which runs on ZeroGPU
  • Your own Windows, macOS or Linux computer with Python
Use case | Who | What they get back
Clean up a compressed picture | Photographers | A processed version of the image
Try several models on one image | Researchers | Results from the named models to compare
Study the demo | Developers | A small Gradio app of a few files

How to set it up: The online page needs nothing. A local copy needs Python and the packages in requirements.txt.

  • Python and pip
  • Python packages from requirements.txt
  • Windows, macOS or Linux
  1. Install the Hugging Face hub library.
  2. Download the Space's files into a folder named Upscaler.
  3. Move into that folder.
  4. Install Gradio 4.44.1, then the packages from requirements.txt.
  5. Start app.py.
pip install -U huggingface_hub
hf download Phips/Upscaler --repo-type space --local-dir Upscaler
cd Upscaler
pip install gradio==4.44.1
pip install -r requirements.txt
python app.py

How to use it: Open the Space's page, or run the last command above for a local copy and open the address it prints. Give the page an image and let it process it.

python app.py

Example: You open the page and load the sample picture that ships with the repository, input_example1.png. The page processes it and shows you the result next to what you gave it.

For newcomers and experts: Start with the sample picture in the repository, so you can see what the page does before you try your own file.

The app is built with Gradio 4.44.1, and its main file is app.py. The Hugging Face copy runs on ZeroGPU hardware. The six model names carry a 1x prefix, and their parts mention realplksr, HAT, OmniSR and SRFormer. The record gives no README, so what each model does is for you to confirm by trying it or reading the code.

Open the page
Give it an image
A model runs on a ZeroGPU
Look at the result

Good to know:

  • Hardware: The Hugging Face copy runs on ZeroGPU hardware.
  • Gaps: The source has no README, so model roles and limits are not described here.
  • License: apache-2.
  • Cost to run: none named.
  • Data it reaches: The images you give it.

On Hugging Face, 13 November 2026

  • Likes: 430
  • Built with: gradio
  • License: apache-2.0

Links

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.

Files
README.md  4 KB

1 file, 4 KB, downloaded as a zip.

What’s new

  • Version 1 · 2026-11-12

Compare with similar

Listed Hugging Face Spaces in the same category that share the most tags with this one.

ProductLikesBuilt withLicenseLast change
Image Upscaler This page430gradioapache-2.0
MusicGenShares gradio5,116gradiocc-by-nc-4.0
Kokoro TTSShares gradio3,556gradioapache-2.0
Background RemovalShares gradio2,988gradiomit
Omni Image EditorShares gradio2,922gradiomit
Diffusers Image OutpaintShares gradio2,565gradioapache-2.0
Numbers as Mindket last read them, on 2026-11-12.

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