anaksor-ocr
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Restores detail in noisy, soft or heavily compressed photos at the same size or 2x larger, using the MegaPotato model on a static page. Free demo for logged-in Hugging Face users.
Photo Enhancer 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.
pip install -U huggingface_hub hf download MegaPotatoAI/Photo-Enhancer --repo-type space --local-dir Photo-Enhancer cd Photo-Enhancer
Photo Enhancer is a static web page that cleans up grainy, soft or compressed photos with the MegaPotato model. It can keep the original size or double it. It aims to work from what is already in the photo and not to draw new faces or text. The model runs on the maker's own service.
It suits anyone with old, noisy or over-compressed photos who wants to see a cleaner version beside the original.
| Industry | Who uses it | How they use it | Example |
|---|---|---|---|
| Photography | Photographers | Clean up grainy or soft shots | Enhance a noisy low-light photo |
| Archives | Archivists | Improve scanned historical pictures | Open one of the examples taken from public archives |
| Real estate | Agents | Refresh compressed listing photos | Run a heavily compressed picture at the original size |
| Software | Developers | Call the same model from code through the maker's REST API | Read the API notes in the README |
Skip it if you need to run the model offline, since the weights are not published.
| Use case | Who | What they get back |
|---|---|---|
| Clean up one photo | Photographers | A restored version at the same size or 2x larger |
| Compare before and after | Everyone | Two views side by side with zoom |
| Try it without logging in | Newcomers | Real example results that anyone can open |
The online page needs a browser and, for your own photos, a Hugging Face login. A local copy needs Python only to fetch the files.
pip install -U huggingface_hub hf download MegaPotatoAI/Photo-Enhancer --repo-type space --local-dir Photo-Enhancer cd Photo-Enhancer
Work through the page in order.
You open one of the real examples, a grainy old photograph. You compare the before and after views side by side and zoom in to check the fine detail.
Open an example first. Examples need no login and show what the enhancer does before you spend a daily run on your own photo.
The page is static HTML. Uploads go to a Cloudflare service that holds the API key, enforces the demo limits and forwards the image to the maker's API. The proprietary model runs on the maker's GPUs. The README reports the maker's own blind test of 18 upscalers, in which none of 67 faces turned into a different person. It notes that this is its own evaluation and that its product is one of those compared.
Restores detail in noisy, soft or heavily compressed photos at the same size or 2x larger, using the MegaPotato model on a static page. Free demo for logged-in Hugging Face users.
Made by MegaPotatoAI 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.
What it is: Photo Enhancer is a static web page that cleans up grainy, soft or compressed photos with the MegaPotato model. It can keep the original size or double it. It aims to work from what is already in the photo and not to draw new faces or text. The model runs on the maker's own service.
Who it suits: It suits anyone with old, noisy or over-compressed photos who wants to see a cleaner version beside the original.
Industry | Who uses it | How they use it | Example
Photography | Photographers | Clean up grainy or soft shots | Enhance a noisy low-light photo
Archives | Archivists | Improve scanned historical pictures | Open one of the examples taken from public archives
Real estate | Agents | Refresh compressed listing photos | Run a heavily compressed picture at the original size
Software | Developers | Call the same model from code through the maker's REST API | Read the API notes in the README
Skip it if you need to run the model offline, since the weights are not published.
Where to use it:
Use case | Who | What they get back
Clean up one photo | Photographers | A restored version at the same size or 2x larger
Compare before and after | Everyone | Two views side by side with zoom
Try it without logging in | Newcomers | Real example results that anyone can open
How to set it up: The online page needs a browser and, for your own photos, a Hugging Face login. A local copy needs Python only to fetch the files.
pip install -U huggingface_hub
hf download MegaPotatoAI/Photo-Enhancer --repo-type space --local-dir Photo-Enhancer
cd Photo-Enhancer
How to use it: Work through the page in order.
Example: You open one of the real examples, a grainy old photograph. You compare the before and after views side by side and zoom in to check the fine detail.
For newcomers and experts: Open an example first. Examples need no login and show what the enhancer does before you spend a daily run on your own photo.
The page is static HTML. Uploads go to a Cloudflare service that holds the API key, enforces the demo limits and forwards the image to the maker's API. The proprietary model runs on the maker's GPUs. The README reports the maker's own blind test of 18 upscalers, in which none of 67 faces turned into a different person. It notes that this is its own evaluation and that its product is one of those compared.
You choose a photo
A Cloudflare service checks the demo limits
The maker's model restores it on its GPUs
You compare before and after
Good to know:
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.
README.md 5 KB
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| Photo Enhancer This page | 11 | static | mit | |
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| AI Face RestorationShares image-restoration · super-resolution · image-to-image | 4 | static | mit | |
| 4K UpscalerShares super-resolution · image-to-image · static | 11 | static | mit | |
| Seedream 5.0 Pro EditShares image-to-image | 2 | docker | mit | |
| DALL·E miniShares static | 5,750 | static | apache-2.0 |
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