TSDiff employs a stochastic denoising diffusion method, iteratively refining a TS geometry from an initial state of Gaussian noise. This denoising process is guided by a NN that has been trained to progressively enhance the noisy output based on the 2D reaction information. The denoising NN in TSDiff is built upon SchNet 122 and requires only graphical inputs of reactants and products, which are readily derived from reaction SMARTS. Trained on the isomerisation dataset, TSDiff achieved a D-MAE of 0.137 Å for the predicted structures compared to reference DFT structures. Furthermore, after subsequent TS optimisation, 53.2 % of the structures converged to the reference DFT geometry.