Votepurchase Multiple Model (SD1.5/SDXL Text-to-Image)

Votepurchase Multiple Model (SD1.5/SDXL Text-to-Image)

Generates images from text prompts with a choice of Stable Diffusion 1.5 and SDXL models on one browser page. It suits people who want to try several image models without setting each one up.

Made by John6666 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 text-to-image page built with Gradio that lets you pick among several Stable Diffusion 1.5 and SDXL models. Its list of models also names FLUX.1 variants and community models. It runs on zero-a10g hardware at Hugging Face.

Who it suits: It suits people who make pictures from prompts and want one place to compare image models, and developers who want to study a multi-model Gradio app.

Industry | Who uses it | How they use it | Example
Digital art | Illustrators and hobbyists | Try different image models on one prompt | Run the same prompt on two models and compare
Content creation | Social media and blog creators | Make pictures from text | Generate an image to go with a post
Software | Developers | Study a Gradio app that switches between models | Read model_dict.json and app.py

You can skip it if you need one fixed model with a documented workflow, since the source says little about usage.

Where to use it: You can use it in these places:

• A browser: open the Space's page on Hugging Face and nothing needs installing.
• Windows, macOS or Linux: run your own copy with Python 3.12.

Use case | Who | What they get back
Text to image | Anyone making pictures | An image generated from a text prompt
Model comparison | Hobbyists | Pictures from different models for the same prompt

How to set it up: You need these first for a local copy:

• Python 3.12 and pip
• The system packages listed in packages.txt, which Hugging Face installs with apt-get

The online page needs none of this. For your own copy, follow the steps below.

1. Install the Hugging Face tools.
2. Download the Space's files into a folder.
3. Move into that folder.
4. Install Gradio at the version the Space uses, then the other packages.
5. Start the app.

pip install -U huggingface_hub
hf download John6666/votepurchase-multiple-model --repo-type space --local-dir votepurchase-multiple-model
cd votepurchase-multiple-model
pip install gradio==6.10.0
pip install -r requirements.txt
python app.py

How to use it: Open the Space in a browser, or start your own copy with the command below. Then type a prompt, choose a model and generate.

python app.py

Tool | What it does
python app.py | Starts the Gradio app on your own computer
hf download | Copies the Space's files to a local folder

Example: You open the Space, type a text prompt and choose one of the listed models. After you run it, the page returns an image made from your prompt.

For newcomers and experts: If you are new to this, open the Space, write one short prompt and keep the first model it offers. Change only the model on the next try to see how much it matters.

If you know the field, the repository holds JSON lists of models, LoRAs and textual inversions, a tagger folder and a folder for turning Japanese into Danbooru tags. The Space names VAEs, FLUX.1 models and two datasets, one of them a negative-prompt embedding set.

You type a prompt
You pick a model
The Space loads it on a GPU
The model makes the image
You see the result

Good to know: The README gives almost no usage notes, so the steps here come from the repository files and Hugging Face's own record.

• License: mit
• Cost to run: none named
• Data it reaches: model files and datasets downloaded from Hugging Face

On Hugging Face, 13 November 2026

• Likes: 147
• Built with: gradio
• License: mit

Links

• Space: https://huggingface.co/spaces/John6666/votepurchase-multiple-model
• Files: https://huggingface.co/spaces/John6666/votepurchase-multiple-model/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.