Autonomous multi-agent framework for self-correcting image synthesis using 'Think → Generate → Critique → Refine' cycles with Gemini 3.1 and SDXL.
<div align="center"> # 🎨 IRG: Iterative Reasoning-Generation ### *The Autonomous Multi-Agent Framework for Self-Correcting Image Synthesis* **Standard T2I models are static. IRG is dynamic. It thinks, critiques, and refines until perfection.** [](https://huggingface.co/spaces) [](https://www.python.org/) [](https://fastapi.tiangolo.com/) [](https://ai.google.dev/) [](https://stability.ai/) [](docs/IRG_Thesis_Paper.pdf) <br> https://github.com/user-attachments/assets/0182f60d-0d8b-4cdd-a648-3194bda74b92 *(Watch IRG autonomously diagnose and fix lighting/composition issues in real-time)* [**Showcase**](#showcase) • [**How it Works**](#architecture) • [**Quickstart**](#installation) • [**Research**](#research) </div> --- ## The Value Proposition **The Problem:** Modern Text-to-Image (T2I) systems are "one-shot" black boxes. Users must manually guess new prompts when the output fails to match their intent or suffers from technical artifacts (overexposure, poor binding). **The Solution:** **IRG** introduces a **closed-loop feedback system**. Inspired by human artistic workflows, it employs a multi-agent hierarchy to perform autonomous **Think → Generate → Critique → Refine** cycles. ### Impact at a Glance - ⚡ **Zero-Manual Prompting**: Describe once; let the agents handle the refinement. - 🎯 **Technical Precision**: Automatically fixes `Blown-Highs`, `Low-Contrast`, and `Semantic Drift`. - 🧠 **Context Awareness**: Uses RAG (Retrieval-Augmented Generation) to
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