Mindket

DALL·E mini

Newversion 1 · updated

Overview

A static web page front end for the dalle-mini model, which by its name is an image generator driven by text. The README gives no description, so its exact controls are not documented.

DALL·E mini 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, for the download step
  • A web browser
Runs in
  • A web browser, on the Space's page at Hugging Face
  • A browser on Windows, macOS or Linux, after you download the files
Start with
pip install -U huggingface_hub
hf download dalle-mini/dalle-mini --repo-type space --local-dir dalle-mini
cd dalle-mini

Platform

WindowsOperating system
macOSOperating system
LinuxOperating system

About

What it is

This Space is a static web page tied to the dalle-mini model. By the name and that model, it looks like a small image generator that turns text into pictures. The README gives no description, so treat that as a reasonable reading rather than a documented fact.

Who it suits

It suits people who want to try a small text to image model in a browser, and web developers who want a short static page to read.

IndustryWho uses itHow they use itExample
Creative workIllustrators, writersTry picture ideas from a short textTyping a scene and looking at what comes back
EducationStudents, teachersShow what an image model doesRunning one prompt in front of a class
Web developmentBeginnersRead a page made of HTML and one scriptOpening index.html next to html2canvas.js

Skip it if you need documented settings or output sizes, because the source gives none.

Where to use it

It runs in a browser, on Hugging Face or from a downloaded copy.

  • A web browser, on the Space's page at Hugging Face
  • A browser on Windows, macOS or Linux, after you download the files
Use caseWhoWhat they get back
Generate an image from textAnyoneA picture made from your words
Read the page's codeBeginnersA small example with one HTML file and one script

How to set it up

Online use needs nothing. A local copy needs Python only to fetch the files, because the page is static.

  • Python and pip, for the download step
  • A web browser
  1. Install the Hugging Face hub package.
  2. Download the Space into a folder named dalle-mini.
  3. Move into that folder.
  4. Open index.html in your browser.
pip install -U huggingface_hub
hf download dalle-mini/dalle-mini --repo-type space --local-dir dalle-mini
cd dalle-mini

How to use it

Open the page and look for a place to type a text description. The README shows no commands or options, so follow whatever the page offers. In a local copy, double click index.html once the download is done.

Example

You could type a short description of a scene and wait for pictures to appear. The README gives no sample prompt or output, so the speed, the number of images and their quality are not documented.

For newcomers and experts

If you are new, open the online page and try one short description. Keep it simple, a single object in a single place, and see what you get.

If you know the field, the repository has no Python app and only a page plus html2canvas.js, so the image model itself must run somewhere else, most likely reached from the browser. The source does not say where, which hardware serves it or how requests are queued.

  1. Open the page
  2. Type a description
  3. Page sends it to the model
  4. Images come back

Good to know

The page is a front end and the README is silent on the rest.

  • License: apache-2
  • Cost to run: none named
  • Data it reaches: the model it names is dalle-mini/dalle-mini, and the source names no account or file access
  • Not for: the source states no limits, so check the page before using its pictures for anything important

README and files

README

DALL·E mini

A static web page front end for the dalle-mini model, which by its name is an image generator driven by text. The README gives no description, so its exact controls are not documented.

Made by dalle-mini 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 is a static web page tied to the dalle-mini model. By the name and that model, it looks like a small image generator that turns text into pictures. The README gives no description, so treat that as a reasonable reading rather than a documented fact.

Who it suits: It suits people who want to try a small text to image model in a browser, and web developers who want a short static page to read.

Industry | Who uses it | How they use it | Example
Creative work | Illustrators, writers | Try picture ideas from a short text | Typing a scene and looking at what comes back
Education | Students, teachers | Show what an image model does | Running one prompt in front of a class
Web development | Beginners | Read a page made of HTML and one script | Opening index.html next to html2canvas.js

Skip it if you need documented settings or output sizes, because the source gives none.

Where to use it: It runs in a browser, on Hugging Face or from a downloaded copy.

  • A web browser, on the Space's page at Hugging Face
  • A browser on Windows, macOS or Linux, after you download the files
Use case | Who | What they get back
Generate an image from text | Anyone | A picture made from your words
Read the page's code | Beginners | A small example with one HTML file and one script

How to set it up: Online use needs nothing. A local copy needs Python only to fetch the files, because the page is static.

  • Python and pip, for the download step
  • A web browser
  1. Install the Hugging Face hub package.
  2. Download the Space into a folder named dalle-mini.
  3. Move into that folder.
  4. Open index.html in your browser.
pip install -U huggingface_hub
hf download dalle-mini/dalle-mini --repo-type space --local-dir dalle-mini
cd dalle-mini

How to use it: Open the page and look for a place to type a text description. The README shows no commands or options, so follow whatever the page offers. In a local copy, double click index.html once the download is done.

Example: You could type a short description of a scene and wait for pictures to appear. The README gives no sample prompt or output, so the speed, the number of images and their quality are not documented.

For newcomers and experts: If you are new, open the online page and try one short description. Keep it simple, a single object in a single place, and see what you get.

If you know the field, the repository has no Python app and only a page plus html2canvas.js, so the image model itself must run somewhere else, most likely reached from the browser. The source does not say where, which hardware serves it or how requests are queued.

Open the page
Type a description
Page sends it to the model
Images come back

Good to know: The page is a front end and the README is silent on the rest.

  • License: apache-2
  • Cost to run: none named
  • Data it reaches: the model it names is dalle-mini/dalle-mini, and the source names no account or file access
  • Not for: the source states no limits, so check the page before using its pictures for anything important

On Hugging Face, 13 November 2026

  • Likes: 5,750
  • Built with: static
  • 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
DALL·E mini This page5,750staticapache-2.0
Kokoro Text-to-Speech (WebGPU)Shares static369staticapache-2.0
DALL·E mini by flax-communityShares static342staticapache-2.0
Supertonic 3 (TTS)Shares static291staticopenrail
XortronDesktopShares static30staticwtfpl
AI Image UpscalerShares static21staticmit
Numbers as Mindket last read them, on 2026-11-12.

Reviews

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What people say

Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search and on Trendshift. Mindket doesn’t write or check them. Open one to read it where it was posted.

itaygabbWordalle – Guess the prompt used to generate a set of images from DalleMini137 points · 15 replies❄️ Reyny! ❄️AI hate wasnt really prevalent much back when all it was was like DallE mini or w/e with everyone writing stuff like "Trail camera footage of Mario"3 likesdazhbogAsk HN: What is the best AI text-to-art generator?Hi friends, I would like to generate some art from text for mostly personal use (similar to Dall-E or Imagen). I tried joining the openAI waiting list and tried the first results in Google, but I would like to know your favourites! It can be a webapp or a cmd line app, preferably without requiring…1 point · 4 repliesitaygabbWordalle – Guess the prompt used to generate a set of images from DalleMini3 pointstheahuraShow HN: (Almost) Every dalle-mini image on Twitter2 pointsrossjillianBorisdayma/Dalle-Mini – Run Dalle Mini with an API on Replicate2 points__rito__Ask HN: What is the most impactful thing you've built?Co-created Dall-E Mini (now named Craiyon, and I am not involved anymore). It was among the first text to language models created independently. And it was fully open source. It also got covered by New York Times in the article covering Dall-E 2 by Cade Metz. Links: - GitHub:…astrangeThe Illustrated Stable DiffusionIt's not just size but also model architecture. DALLE mini (craiyon.com) has the opposite priority because of its different architecture; you can enter a complex prompt and it will follow it, but it's much slower and the image quality is a lot worse. SD prefers to make aesthetic pictures over…m00xDid GoogleAI just snooker one of Silicon Valley’s sharpest minds?They actually use the reverse test to train the generator, and to score which image is most relevant to the prompt from the many images given by the generator. Dall-E does this using the OpenAI CLIP model. You can see the mini version here using this exact logic…

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