DreamDojo is an application repository for training and deploying world foundation models, particularly focused on robotics applications. The system provides end-to-end capabilities for training Cosmos Predict2 models, generating text/video-to-video outputs, action-conditioned video generation for robot control, and model distillation for efficient inference. It features a modular architecture with Hydra-based configuration, distributed training support (FSDP, tensor parallelism, context parallelism), and interactive Gradio demonstrations. The codebase includes sophisticated safety filtering mechanisms and is designed for deployment via Docker containers.
This repository implements an end-to-end 3D object generation application that takes natural language descriptions and produces 3D mesh models (GLB format). It orchestrates three generative AI models: an LLM (Qwen3 or Llama) for scene planning, a diffusion model (SANA Sprint) for 2D image generation, and Microsoft TRELLIS for 3D generation. The system presents a Gradio web interface for user interaction and includes a Blender addon for direct import of generated assets. It supports two deployment modes: native PyTorch models (for local development) and NIM containers (for scalable inference). Key architectural patterns include a singleton GPU memory manager that coordinates model loading/unloading across VRAM-constrained GPUs, a guardrail service for content safety, and a conditional service abstraction that transparently switches between native and NIM backends based on configuration. The repository is well-structured with modular UI components and comprehensive installation scripts, though it is Windows-only and has complex event chains in the main UI file. The code is actively maintained with recent commits and pinned dependencies, but the heavy reliance on patched versions of TRELLIS and a specific Gradio version introduces some maintenance risk.