How do you implement data preprocessing for diffusion models?

Implementing data preprocessing for diffusion models involves three key stages: data preparation, noise handling, and input formatting. First, prepare your dataset by normalizing and resizing inputs to ensure consistency. For image data, this typically means scaling pixel values to a [-1, 1] or [0, 1] range using libraries like PyTorch’s transforms.Normalize and resizing images to a fixed resolution (e.g., 256x256) with cropping or padding. Augmentations like random flips or rotations can improve generalization. For non-image data (e.g., audio or text), convert raw inputs into standardized tensors—for example, spectrograms for audio or tokenized embeddings for text.

Next, handle noise scheduling, a core aspect of diffusion models. During training, noise is incrementally added to data samples across timesteps. Precompute a noise schedule (e.g., linear or cosine-based) to determine how much noise is added at each step. For each batch, generate random noise tensors matching the data dimensions and apply them using the schedule. For example, in PyTorch, you might create a function that takes a clean image x, a timestep t, and returns x_t = sqrt(alpha_t) * x + sqrt(1 - alpha_t) * epsilon, where alpha_t is derived from the schedule and epsilon is random noise. Store timestep values as embeddings or positional encodings for the model to use during training.

Finally, structure the data pipeline for efficiency. Use frameworks like PyTorch’s Dataset and DataLoader to batch and shuffle data. For example, a custom dataset class might load images, apply preprocessing, generate noise and timesteps on the fly, and return tuples of (noisy_data, timestep, clean_data). Ensure the pipeline scales to large datasets by leveraging parallel loading and prefetching. If working with limited resources, consider caching preprocessed data or using mixed precision. Validate the pipeline by visualizing samples—for images, check that noise increases correctly across timesteps, and for text, ensure tokenized outputs align with the model’s vocabulary.