Training, stable diffusion model, neural network, deep learning, machine learning, algorithm, dataset, optimization, loss function, gradient descent, Adam optimizer, batch normalization, layer normalization, weight decay, learning rate schedule, warm-up, cosine annealing, step-wise learning rate, data augmentation, random cropping, color jittering, flipping, rotation, Gaussian noise, diffusion process, Markov chain, transition matrix, stationary distribution, convergence, stability, mode collapse, non-saturating, overfitting, underfitting, computational graph, GPU acceleration, parallel processing, batch processing, memory optimization, PyTorch, TensorFlow, Keras, Python.