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metadata 
license: mit

diffusion

image/video generation GUI for GGUF diffusion models, packaged for Python. The GUI runs in your browser against a local server; generation is done by the diffusion (c/c++) engine, compiled during pip install and bundled with the package as a single binary. Model and image files are referenced by filesystem path through a built-in file browser — nothing is uploaded or copied to temp storage.

install via pip/pip3 

pip install gguf-diffusion

build it from source code

CUDA (NVIDIA)

$env:CMAKE_ARGS="-DSD_CUDA=ON"
pip install gguf_diffusion-x.x.x.tar.gz

ROCm/HIP (AMD)

$env:CMAKE_ARGS="-DSD_HIPBLAS=ON"
pip install gguf_diffusion-x.x.x.tar.gz

macOS/Metal (Apple)

pip install gguf_diffusion-x.x.x.tar.gz

usage

enter GUI diffusion panel

gguf-diffusion

screenshot

GUI features (similar to the gguf desktop app's diffusion panel):

  • txt2img with the full model stack: --model / --diffusion-model, VAE, external text encoders (--clip_l, --t5xxl, --llm, …), additional models (ControlNet, TAESD, upscaler, PhotoMaker, …), tokenizer packs
  • image inputs: init image (img2img), mask (inpainting), end frame, control image, reference images
  • sampling controls: CFG scale, steps, size, seed, batch count, all engine sampling methods and schedules, flash attention, low-VRAM flags
  • live progress and engine log, output gallery, saved workflows (localStorage + JSON export/import), copyable/editable CLI command

use CLI call the engine straight in terminal/console

gguf-diffusion engine -- --diffusion-model model.gguf --clip_l clip_l.gguf --clip_g clip_g.gguf --t5xxl t5xxl.gguf --vae vae.gguf -H 512 -W 512 -p 'a lovely cat holding a sign says GGUF' --steps 8 --cfg-scale 1 --sampling-method euler -v --clip-on-cpu -o out.png

how it works

  • pip install compiles the diffusion.cpp engine (static libdiffusion + static ggml linked into one CLI executable) via scikit-build-core and installs it into the package's bin/ directory.
  • gguf-diffusion starts a stdlib HTTP server (default port 8643) serving the static GUI and a small JSON API, and opens the browser.
  • Each generation spawns one engine process; the server parses its progress bars, streams the log to the GUI, and lists the produced images.
  • File selection uses a server-side directory listing (/api/browse) so the GUI gets real filesystem paths — no drag & drop uploads of multi-GB models.

or run it with gguf-connector

ggc fu

screenshot