README.md

metadata

title: Prompt Search Engine
emoji: 🐠
colorFrom: pink
colorTo: purple
sdk: docker
pinned: false
short_description: Improve image quality with better prompts!

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Prompt Search Engine

Overview

This project implements a prompt search engine for Stable Diffusion models. The search engine allows users to input a prompt and returns the top n most similar prompts from a corpus of existing prompts. This helps in generating higher quality images by providing more effective prompts.

The search engine consists of two main components:

• Prompt Vectorizer: Converts prompts into numerical vectors using a pre-trained embedding model.
• Similarity Scorer: Measures the similarity between the input prompt and existing prompts using cosine similarity.

Setup Instructions

Requirements

• Python >= 3.9
• pip

Installation

1. Clone the repository

   git clone <repository-url>
   cd <repository-directory>
   

2. Create a virtual environment (optional)

   python -m venv venv
   source venv/bin/activate
   

3. Install dependencies

   pip install -r requirements.txt
   

Running the run.py script

The run.py script allows you to run the prompt search engine from the command line.

Usage

python run.py --query "Your query prompt here" --n 5 --model "all-MiniLM-L6-v2"

Arguments

• --query: The query prompt (required).
• --n: The number of similar prompts to return (default 5).
• --model: The name of the SBERT model to use (default "all-MiniLM-L6-v2").

Example

python run.py --query "A cat wearing glasses, sitting at a computer" --n 7