---
title: WeavePrompt
emoji: 🎨
colorFrom: blue
colorTo: purple
sdk: docker
sdk_version: 5.44.1
pinned: false
license: mit
app_file: app.py
app_port: 7860
---

# WeavePrompt

Iterative prompt refinement for text-to-image models.
Given a target image, WeavePrompt automatically generates and refines text prompts to make a model's output resemble the target image, using vision-language models and perceptual metrics.

## Introduction

**WeavePrompt** is a research and development project designed to evaluate and refine text-to-image generation prompts across multiple state-of-the-art image generation models. The primary goal is to optimize prompts such that the generated images align closely with a given reference image, improving both fidelity and semantic consistency.

The process involves generating images from identical prompts using various image generation models, comparing the results to a reference image through a recognition and similarity evaluation pipeline, and iteratively adjusting the prompt to minimize perceptual differences. This feedback loop continues for a set number of iterations, progressively enhancing prompt effectiveness.

To achieve this, WeavePrompt integrates advanced tools:

Image recognition is powered by meta-llama/Llama-4-Scout-17B-16E-Instruct.

Similarity evaluation uses the LPIPS (alex) metric for perceptual comparison.

Image generation models under evaluation include:
- FLUX family: FLUX.1 [pro], [dev], [schnell], and FLUX.1 with LoRAs
- Google models: Imagen 4, Imagen 4 Ultra, and Gemini 2.5 Flash Image
- Other models: Stable Diffusion 3.5 Large and Qwen Image

By systematically combining prompt optimization with multi-model evaluation, WeavePrompt aims to advance the understanding of cross-model prompt effectiveness and improve controllability in image generation tasks.

## Features
- Upload a target image
- Step-by-step prompt optimization
- View prompt and generated image at each iteration
- Full optimization history

## Installation

1. Clone the repository:
	```bash
	git clone <repo-url>
	cd WeavePrompt
	```
2. Install dependencies:
	```bash
	uv venv
	uv sync
    source .venv/bin/activate
	```
3. Setup `.env`
Put the following inside `.env`:
- API keys `WANDB_API_KEY` and `FAL_KEY`
- Weave project name `WEAVE_PROJECT`

## Usage

Run the demo app:
```bash
streamlit run app.py
```

Follow the instructions in the browser to upload an image and step through the optimization process.


## Architecture Diagram

![diagram](./diagram.png)