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README.md

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 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