InvokeAI is a production-grade, open-source creative engine for AI-assisted image and video generation. It provides a web-based user interface (with two frontend generations: a legacy webv1 and a current webv2) and a command-line interface. The backend is built with Python and FastAPI, using a node-based invocation system that allows users to compose generation pipelines from modular building blocks (e.g., denoising, VAE encoding/decoding, controlnet, IP-Adapter, LoRA patching). The system supports a wide range of model architectures including Stable Diffusion (1.5, 2.0, XL, 3.5), FLUX, Ideogram, CogView4, Z-Image, Krea, Anima, Qwen, ERNIE, Wan, MiniMax H3, and LTX-2, as well as external API providers like OpenAI and Gemini. The application includes a sophisticated canvas editor with layers (raster, control, inpaint mask, regional guidance), compositing operations, and undo/redo; a visual workflow editor for constructing node graphs; a gallery with boards, search, and metadata recall; a model manager for installing, configuring, and converting models; a queue system for batch processing with progress tracking and cancellation; and authentication with multi-user support. Persistence is handled via SQLite (with a custom migration system) or external MySQL/MariaDB. The frontend is built with React, Chakra UI, and nanostores/Redux for state management, with extensive internationalization scaffolding. While primarily an application, the repository contains significant research-oriented code for model architecture definitions, quantization (bitsandbytes, GGUF, SDNQ), custom PyTorch module autocasting, and image processing pipelines (Canny, depth estimation, face detection, segment anything, upscaling). The codebase demonstrates high engineering craftsmanship with abstract service layers, registry patterns, and a node-based architecture that cleanly separates concerns. However, it carries maintenance complexity from two parallel frontend codebases, vendored dependencies, and a large migration chain.
UniTEX is a research-focused repository implementing a two-stage framework for high-fidelity 3D texture generation from arbitrary 3D shapes. Stage 1 uses LoRA-adapted Diffusion Transformers for multi-view texture synthesis, while Stage 2 employs a transformer-based Large Texturing Model (LTM) to predict Texture Functions in unified 3D space, bypassing UV mapping limitations. The repository integrates super-resolution (TSD_SR) for detail enhancement and provides tools for mesh processing, rendering, and texture reprojection. It includes training scripts, model architectures, and utility libraries, indicating its primary purpose is to support experimentation and reproduction of the CVPR 2026 paper's methodology rather than serving as a standalone end-user application.