To implement class-conditional diffusion models, you first need to understand the basic structure of a diffusion model. A diffusion model is a type of generative model that transforms a simple distribution (like Gaussian noise) into a complex distribution (like images) through a series of gradual denoising steps. In the case of class-conditional diffusion models, the process is designed to generate samples that correspond to specific classes or categories. The key is to integrate class information into both the noise process and the denoising network.
The first step in implementation is to prepare your dataset appropriately. Each data point should be associated with a label that indicates its class. This could be images of different categories (like cats and dogs) where you have labels for each image. You will need to preprocess your data by normalizing the images and potentially augmenting them to improve diversity. After labeling your dataset, you can build the noise model, which typically involves a multi-step process of adding Gaussian noise to your images in a controlled manner. This noise addition simulates a diffusion process through multiple time steps.
Next, focus on the training of the model. You will use a denoising neural network, often a convolutional neural network (CNN), to predict the original data from the noisy version. Here, conditioning on the class label becomes essential. One common method is to concatenate the class label with the image data at each time step to provide the model with context about which class it is working on. During training, the loss function will be calculated based on how well the neural network predicts the original image from its noisy counterpart, adjusted for the specific class. This process allows the model to learn to generate class-specific samples effectively. Finally, during inference, you can generate new samples by feeding random noise and the desired class label into the trained model, yielding images that reflect the characteristics of that class.