Tiny Dream is a header-only, dependency-free C++ implementation of Stable Diffusion, designed for efficient CPU inference with a low memory footprint (1.7-5.5 GB RAM). It targets developers needing to embed AI image generation capabilities into existing C++ applications without requiring specific GPU hardware.

How It Works

Tiny Dream leverages C++17 for its implementation, focusing on CPU efficiency through TBB threading and SSE/AVX vectorization. It uses the ncnn tensor library as its default backend, with plans to migrate to lighter libraries like SOD or GGML for further CPU performance optimization. The library is designed for straightforward integration, requiring only two header files (tinydream.hpp and stb_image_write.h) and pre-trained models.

Quick Start & Requirements

• Install/Run: Compile with a C++17 compiler (GCC 7+, Clang, MSVC) and link against ncnn. Example command: g++ -o tinydream boilerplate.cpp -funsafe-math-optimizations -Ofast -flto=auto -funroll-all-loops -pipe -march=native -std=c++17 -Wall -Wextra pkg-config --cflags --libs ncnn -lstdc++ -pthread -Wl -flto -fopt-info-vec-optimized
• Prerequisites: C++17 compiler, ncnn library, pre-trained models (2GB+).
• Resources: Requires 1.7-5.5 GB RAM.
• Docs: Official Documentation

Highlighted Details

• Header-only, dependency-free (except stb_image_write.h).
• Supports Real-ESRGAN upscaling.
• Features "Words Priority" for prompt weighting and output metadata linking.
• Can generate 512x512 or 2048x2048 images.

Maintenance & Community

• Developed by PixLab / Symisc Systems.
• Roadmap includes GUI, WebAssembly, and alternative format outputs.
• Related projects include SOD (Computer Vision), FACEIO (Facial Authentication), and UnQLite (Database).

Licensing & Compatibility

• License is not explicitly stated in the README, but PixLab projects are typically proprietary or have custom licensing. Further clarification is needed for commercial use.

Limitations & Caveats

The README does not explicitly state the license, which is crucial for commercial adoption. The project currently relies on ncnn, with plans to switch to potentially less mature libraries (SOD, GGML), which might introduce compatibility or stability changes.