Stable Diffusion TensorFlow is an open-source implementation of Stable Diffusion written in TensorFlow and Keras, with weights ported from the original PyTorch release. With about 1,600 stars on GitHub, it lets the Keras ecosystem generate images with the landmark text-to-image model without leaving the TensorFlow stack.
The repository includes a full set of Colab notebooks — GPU and TPU variants covering text-to-image, image-to-image, inpainting and tile or texture generation — so you can experiment in a browser before installing anything locally. The implementation reports generation times of roughly ten seconds for a 512×512 image on a default Colab GPU without a drop in quality, and it supports mixed precision training settings as well as loading PyTorch checkpoints. For teams standardized on TensorFlow serving infrastructure, having a native Keras implementation made it far easier to integrate Stable Diffusion into existing pipelines when the model first appeared, and it remains a readable reference for how diffusion models are assembled.
The fast Colab results were publicly noted by Keras creator François Chollet around the time of release, which helped this port become the reference TensorFlow implementation during Stable Diffusion’s early days.
Stable Diffusion TensorFlow is free and open source; the code can be downloaded from GitHub at no cost. Running it requires your own GPU or TPU resources, for example through Colab or your own cloud account.
TensorFlow and Keras developers who want text-to-image generation inside their existing stack, and learners who find the Keras implementation a readable way to study how Stable Diffusion works. It targets the original Stable Diffusion release rather than the newest model versions.