Flux 2 in Pure C: Zero‑Dependency Image Generation

Flux 2 is the new wave of latent‑diffusion models that can turn prompts into stunning images in four sampling steps. Black Forest Labs released the small “Klein” variant with just 4 B parameters, but the inference code remains large—16 GB of weights. Flux 2‑c gives you a fast, lightweight, pure C implementation that runs on macOS or Linux without requiring Python, CUDA, or a full ML stack.

Why a C implementation?

• Zero external runtime – Only the C standard library is required, making the binary portable across environments that can compile C.
• Memory‑mapped weights – The default --mmap mode keeps the 16 GB safetensors on‑disk and loads them on‑demand, bringing peak memory down to ~4–5 GB.
• GPU acceleration (optional) – Apple Metal (MPS) on Macs or BLAS on Intel/Linux gives a ~30× speedup compared to the pure‑C baseline.

Quick Start

# 1. Clone the repo
git clone https://github.com/antirez/flux2.c
cd flux2.c

# 2. Choose a backend
make mps          # Apple Silicon (fastest)
# make blas      # Intel Mac / Linux with OpenBLAS
# make generic   # Pure C, no dependencies

# 3. Download the 16 GB model
./download_model.sh          # shell script (curl)
# or: pip install huggingface_hub && python download_model.py

# 4. Run the demo
./flux -d flux-klein-model \
       -p "A woman wearing sunglasses" \
       -o output.png

The image appears immediately in the terminal if it supports the Kitty protocol.

Features at a Glance

Building From Source

Prerequisites – a C compiler, optionally OpenBLAS for the blas target or Apple Accelerate for mps.

# On macOS
clang -framework Accelerate -o flux flux.c flux_h*.c ...
# On Linux via OpenBLAS
gcc -fopenmp -L/usr/lib -lopenblas -o flux flux.c flux_h*.c ...

Run make to list available backends. Each target builds a binary that automatically selects the fastest path for your platform.

Using the Library in Your Own Project

#include "flux.h"
#include <stdio.h>
int main(void) {
    flux_ctx *ctx = flux_load_dir("flux-klein-model");
    if (!ctx) { printf("Failed: %s\n", flux_get_error()); return 1; }

    flux_params p = FLUX_PARAMS_DEFAULT;
    p.width = 512; p.height = 512; p.seed = 42;

    flux_image *img = flux_generate(ctx, "A fluffy orange cat", &p);
    if (!img) { printf("Error: %s\n", flux_get_error()); return 1; }

    flux_image_save(img, "cat.png");
    flux_image_free(img);
    flux_free(ctx);
    return 0;
}

Compile with gcc -o myapp myapp.c -L. -lflux -lm -framework Accelerate (macOS) or the OpenBLAS equivalent.

Debugging & Compare With Python

The repo ships a debug/ folder with Python scripts that dump the exact tensors from the Black Forest reference implementation. Run the C binary with --debug-py to verify pixel‑perfect parity.

./flux -d flux-klein-model --debug-py -o c_debug.png

This is invaluable for sanity checks when tweaking model weights or tokenizers.

FAQ

Conclusion

Flux 2‑c brings state‑of‑the‑art image generation to developers who want performance without the bloat of a Python runtime or CUDA. Its tiny footprint, optional GPU support, and the ability to embed the library in C/C++ projects make it ideal for embedded systems, edge computing, or just a fun CLI tool for artists. Clone, build, and start creating—your terminal can now paint masterpieces for free.