--- title: "Pixel CNN MNIST" kind: "Kit" category: "Education" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/8ee17b64-2d86-4856-a178-59db883dcd9a" updated: "2026-11-13" --- # Pixel CNN MNIST > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates small handwritten-digit images with a PixelCNN model from the Keras team in a Gradio page. For machine learning learners curious about image generation. Pixel CNN MNIST is a kit in the Education category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by keras-io ## About ### What it is Pixel CNN MNIST is a Gradio app that generates images with a PixelCNN model trained on MNIST, a set of handwritten digit pictures. The record names one model, pixel-cnn-mnist, from the keras-io group. A queue database file also sits in the repository. ### Who it suits It suits machine learning learners who want to watch a classic image generator work on a tiny dataset, and developers who want a small Gradio example around a Keras model. The record does not describe the controls. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Education | Machine learning students | See an autoregressive image model generate pictures | Open the Space and generate a few digits | | Research | Hobby researchers | Look at what a small generative model can draw | Compare several generated images | | Software | Python developers | Read a small Gradio demo around a Keras model | Open app.py and requirements.txt | You can skip it if you need generated photos or art, since MNIST holds small digit images. ### Where to use it - A web browser, on the Space's page on Hugging Face - Windows, macOS or Linux with Python, from a downloaded copy | Use case | Who | What they get back | | --- | --- | --- | | Generate digit images | Learners | New small digit-like pictures | | Study a PixelCNN demo | Students | A compact app around a Keras model | | Reuse as a base | Developers | A small Gradio app to adapt | ### How to set it up - Nothing at all if you use the online Space - Python on your computer, only if you run a copy - The packages listed in requirements.txt 1. Open the Space's page in a browser to use it with nothing to install. 2. To run a copy, install the Hugging Face Hub package and download the Space. 3. Move into the new folder, then install Gradio and the listed requirements. 4. Start the app with Python. ```sh pip install -U huggingface_hub hf download keras-io/pixelcnn-mnist-image-generation --repo-type space --local-dir pixelcnn-mnist-image-generation cd pixelcnn-mnist-image-generation pip install gradio pip install -r requirements.txt python app.py ``` ### How to use it Online, open the Space and use the controls on its page. On your own machine, run the main file from the Space folder, then use the page it serves. The record does not list the controls, so look at the page. ```sh python app.py ``` ### Example You open the Space and ask it to generate images. The app returns small pictures drawn by the model, which should look like handwritten digits. The record shows no sample output, so check the page for the controls. ### For newcomers and experts An image generator learns what pictures in a set look like and then makes new ones. Start by opening the Space and generating a handful of digits to see how they turn out. If you know the field, a PixelCNN predicts each pixel from the ones before it, so generation runs one pixel at a time and is slow compared with one-shot generators. The record gives no sampling settings or quality figures. You ask the page for images → The app calls the PixelCNN model → The model fills in pixels one by one → The page shows the generated digits ### Good to know - License: Apache 2, as listed on the Space - Cost to run: none named - Data it reaches: only what the page takes as input, plus the model it names - Not for: the record names no exclusions, and MNIST digits are tiny images ## README ````markdown # Pixel CNN MNIST Generates small handwritten-digit images with a PixelCNN model from the Keras team in a Gradio page. For machine learning learners curious about image generation. Made by keras-io 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: Pixel CNN MNIST is a Gradio app that generates images with a PixelCNN model trained on MNIST, a set of handwritten digit pictures. The record names one model, pixel-cnn-mnist, from the keras-io group. A queue database file also sits in the repository. Who it suits: It suits machine learning learners who want to watch a classic image generator work on a tiny dataset, and developers who want a small Gradio example around a Keras model. The record does not describe the controls. ```table Industry | Who uses it | How they use it | Example Education | Machine learning students | See an autoregressive image model generate pictures | Open the Space and generate a few digits Research | Hobby researchers | Look at what a small generative model can draw | Compare several generated images Software | Python developers | Read a small Gradio demo around a Keras model | Open app.py and requirements.txt ``` You can skip it if you need generated photos or art, since MNIST holds small digit images. Where to use it: - A web browser, on the Space's page on Hugging Face - Windows, macOS or Linux with Python, from a downloaded copy ```table Use case | Who | What they get back Generate digit images | Learners | New small digit-like pictures Study a PixelCNN demo | Students | A compact app around a Keras model Reuse as a base | Developers | A small Gradio app to adapt ``` How to set it up: - Nothing at all if you use the online Space - Python on your computer, only if you run a copy - The packages listed in requirements.txt 1. Open the Space's page in a browser to use it with nothing to install. 2. To run a copy, install the Hugging Face Hub package and download the Space. 3. Move into the new folder, then install Gradio and the listed requirements. 4. Start the app with Python. ```sh pip install -U huggingface_hub hf download keras-io/pixelcnn-mnist-image-generation --repo-type space --local-dir pixelcnn-mnist-image-generation cd pixelcnn-mnist-image-generation pip install gradio pip install -r requirements.txt python app.py ``` How to use it: Online, open the Space and use the controls on its page. On your own machine, run the main file from the Space folder, then use the page it serves. The record does not list the controls, so look at the page. ```sh python app.py ``` Example: You open the Space and ask it to generate images. The app returns small pictures drawn by the model, which should look like handwritten digits. The record shows no sample output, so check the page for the controls. For newcomers and experts: An image generator learns what pictures in a set look like and then makes new ones. Start by opening the Space and generating a handful of digits to see how they turn out. If you know the field, a PixelCNN predicts each pixel from the ones before it, so generation runs one pixel at a time and is slow compared with one-shot generators. The record gives no sampling settings or quality figures. ```flow You ask the page for images The app calls the PixelCNN model The model fills in pixels one by one The page shows the generated digits ``` Good to know: - License: Apache 2, as listed on the Space - Cost to run: none named - Data it reaches: only what the page takes as input, plus the model it names - Not for: the record names no exclusions, and MNIST digits are tiny images ## On Hugging Face, 13 November 2026 - Likes: 2 - Built with: gradio - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/keras-io/pixelcnn-mnist-image-generation - Files: https://huggingface.co/spaces/keras-io/pixelcnn-mnist-image-generation/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 [keras-io/pixelcnn-mnist-image-generation on Hugging Face](https://huggingface.co/spaces/keras-io/pixelcnn-mnist-image-generation) ## 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 | | --- | --- | --- | --- | --- | | Pixel CNN MNIST (this page) | 2 | gradio | apache-2.0 | 2022-02-19 | | [Unit 1 Quiz - AI Agent Fundementals](https://mindket.com/p/1b65056a-a2b2-4179-a81c-bdffa3636205) | 360 | gradio | apache-2.0 | 2025-06-24 | | [Unit 2.1 smolagents Code Quiz](https://mindket.com/p/c9f42604-0003-4117-9a4b-2b785f68ee99) | 63 | gradio | apache-2.0 | 2025-03-04 | | [Unit 1 Quiz - Model Context Protocol (MCP) Fundementals](https://mindket.com/p/f7210665-ca4c-423f-b45d-a1c6fa549eec) | 58 | gradio | apache-2.0 | 2025-06-03 | | [Tamazight Text-to-Speech](https://mindket.com/p/ab94f6a3-34fa-4c03-9fd0-751ff4f282eb) | 41 | gradio | cc-by-4.0 | 2023-11-25 | | [Embedding Draw Challenge](https://mindket.com/p/76690a14-5bc9-4d53-ad3e-d47e7218918f) | 30 | gradio | apache-2.0 | 2026-10-06 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2022-02-19 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open keras-io/pixelcnn-mnist-image-generation on Hugging Face](https://huggingface.co/spaces/keras-io/pixelcnn-mnist-image-generation) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/8ee17b64-2d86-4856-a178-59db883dcd9a) - [Its page on Mindket](https://mindket.com/p/8ee17b64-2d86-4856-a178-59db883dcd9a)