This repository provides a collection of custom nodes for ComfyUI and SD WebUI (reForge) that implement various guidance techniques for diffusion models. It includes implementations of Perturbed-Attention Guidance (PAG), Smoothed Energy Guidance (SEG), Sliding Window Guidance (SWG), PLADIS, Normalized Attention Guidance (NAG), Token Perturbation Guidance (TPG), Frequency-Decoupled Guidance (FDG), Momentum Guidance (MG), and Sliding Mode Control CFG (SMC-CFG). The architecture consists of modular node files, shared utility functions, and backend compatibility layers. Key entry points are the ComfyUI extension registration in __init__.py and the reForge integration scripts in the scripts directory. The system modifies the denoising process of diffusion models by injecting guidance computations at specific U-Net blocks, enhancing image quality and control.
GraphDreamer is a PyTorch-based system for generating compositional 3D scenes from scene graphs. It implements a coarse-to-fine approach where objects are represented as implicit signed distance functions (SDFs) and optimized using gradient signals from 2D diffusion models (DeepFloyd IF for coarse stage, Stable Diffusion for refinement). The system takes text prompts describing objects and their relationships, constructs a scene graph, and generates 3D meshes representing the composed scene. Architecture follows the threestudio framework with modular components for data handling, geometry, materials, guidance, rendering, and system orchestration. Key entry points are in launch.py which supports training, validation, testing, and export modes via command-line interface.