WorldGen is a Python library and demo application for generating 3D scenes from text prompts or single images in seconds. It orchestrates a pipeline of state-of-the-art models: a fine-tuned FLUX diffusion model to generate 360° panoramic images, DA-2 for monocular depth estimation on equirectangular images, and optionally OneFormer for semantic segmentation and LaMa for inpainting to handle background occlusions. The 3D scene is produced as a Gaussian splatting point cloud (via a custom RGBD-to-Gaussian conversion) or as a textured mesh. An experimental mode uses Apple's Sharp model for higher-quality Gaussian splats by processing cubemap faces. The codebase demonstrates strong engineering practices for a research project: modular pipeline design, use of submodules for dependencies, custom diffusers pipelines with VAE tiling modifications for seamless panorama generation, and a Viser-based interactive viewer. While the core value is the integrated pipeline, several utility modules (equirectangular projection utilities, splat file handling) are cleanly isolated and reusable in other 360° or 3D vision projects.