stable-diffusion.cpp

DwarfStar is a self-contained, model-specific native inference engine for running DeepSeek V4 Flash, GLM 5.2, and, on high-memory systems, DeepSeek V4 PRO locally. It combines model loading, prompt rendering, tool calls, persistent KV state, an HTTP server, and a coding agent rather than serving as a general GGUF runner. The engine supports Metal on Macs, CUDA including multi-GPU systems, and ROCm on Strix Halo hardware; it also provides SSD streaming for machines with insufficient RAM, distributed tensor and pipeline parallelism, and server-side micro-batching for multi-user inference. The repository includes tooling and data for GGUF, imatrix, quality, and speed testing, and acknowledges code and design contributions from llama.cpp and GGML.

kimodo.cpp is a C++ and GGML implementation of NVIDIA's Kimodo text-to-motion model for local CPU or Vulkan inference. It accepts UTF-8 prompts or precomputed 4096-value F32 LLM2Vec embeddings and generates root translations plus local XYZW rotations for several control skeletons, including SMPL-X, SOMA, and Unitree G1 formats. The project includes GGUF model loading with validation, Safetensors conversion, DDIM sampling, conditioned multi-prompt transitions, C and C++ APIs, CPU/Vulkan parity tests, skeleton-only GLB export, and a local browser demo. Its native API returns the joints predicted by the selected model; features such as general constraint input, SOMA's 77-joint expansion, skinned-mesh GLB export, and quantized models are not implemented.

Colite is an experimental C++17 structure-from-motion and multi-view stereo system that reconstructs 3D scenes from overlapping images. Its pipeline performs incremental structure-from-motion with support for multiple models, continuation, and Sim(3) merging; feature extraction and matching; PatchMatch stereo with geometric consistency; stereo fusion; meshing; simplification; and texture-atlas generation. It produces COLMAP-compatible sparse models, RGB and normal dense point clouds, Poisson or Delaunay meshes, and textured OBJ models with MTL and atlas PNG files. CPU and optional CUDA implementations are provided for SIFT, descriptor matching, and PatchMatch Stereo, and resumable stage manifests allow processing to continue from saved stages. The repository describes the current build as alpha-quality, primarily developed and tested on Ubuntu 22.04 and WSL2; native Windows builds are not yet validated as a supported release target.

BlazeDiff is an open-source diff ecosystem for JavaScript applications and other runtimes. It provides pixel-perfect image comparison, Rust and SIMD-based native and WebAssembly diff engines, structured image-diff interpretation, object diffing, perceptual metrics including SSIM, MS-SSIM, Hitchhiker's SSIM, and GMSD, and a from-scratch Rust PNG codec. The ecosystem includes JavaScript, Rust, Python, and command-line packages, along with framework-agnostic and React UI components for visualizing differences. Its agentic visual-regression tool discovers routes, captures deterministic screenshots, and sends ambiguous diffs to a coding agent for a verdict.

llama.cpp is a C/C++ implementation for running large language and vision-language model inference locally or in the cloud, including on CPUs, GPUs, laptops, Apple silicon devices, and small computers such as Raspberry Pis. The project is built on the ggml library and uses integer quantization from 1.5-bit through 8-bit to reduce memory use and accelerate inference, with CPU, CUDA, HIP, Metal, Vulkan, SYCL, OpenCL, and other hardware backends. It supports hybrid CPU-GPU inference for models larger than available VRAM and includes command-line tools, a built-in web interface, and an OpenAI-compatible REST API server. The project is distributed as source code, Docker images, and pre-built binaries, with installation instructions at llama.app.