CrawLLM: An LLM-Based Pipeline for Game Asset Generation

Procedural Content Generation (PCG) systems typically struggle to generate cohesive content across multiple domains. Large Language Models (LLMs) understand semantic relationships of game elements, at least when they are described in natural language. This paper investigates how LLMs and text-to-image models can generate narrative, visual, and gameplay contentcoherently. We leverage LLMs as a scaffold for an automated, theme-driven asset generation pipeline, enabling unique and scalable game experiences with no developer input besides setting up a game template. This paper introduces CrawLLM, a dungeon crawler with card combat mechanics, as a testbed for an LLM-driven game generation pipeline. A Mixtral 8x7B model generates game themes, guiding the creation of narrative content. Visual assets are produced via Stable Diffusion XL, using ControlNet and IP-Adapter modules to achieve game-ready formats. A user study conducted on snapshots of fully generated games indicates that the underlying semantic themes remain clearly discernible in many cases, although intended visual styles were less clear. This work demonstrates the potential of LLM-driven pipelines for PCG, while highlighting areas for improvement in content specificity.