How do you implement class-conditional diffusion models?

To implement class-conditional diffusion models, you start by integrating class information into the diffusion process. Diffusion models work by gradually adding noise to data and then learning to reverse this process. For class conditioning, you modify the model to accept class labels as input alongside the noisy data. This is typically done by embedding the class label into a vector and injecting it into the neural network at multiple stages. For example, in a U-Net architecture (commonly used for diffusion), you might concatenate the class embedding with timestep embeddings or use cross-attention layers to condition the model on the class. The key idea is to ensure the model uses the class label to guide the denoising process, generating data that aligns with the specified class.

Next, the training process requires careful setup. During training, you feed pairs of noisy data samples and their corresponding class labels into the model. The loss function measures how well the model predicts the noise added to the data, conditioned on the class. For instance, if you’re training on CIFAR-10, each image’s class label (e.g., “airplane” or “dog”) is embedded and combined with the timestep information. This conditioning is applied at every denoising step. A common approach is to use a simple projection layer to map class labels into embeddings, which are then added to the timestep embeddings or fed into residual blocks. It’s important to ensure the class information is consistently available throughout the network, as missing this can lead to poor conditioning and blurry outputs.

Finally, during sampling, you generate data by reversing the diffusion process while conditioning on a target class. Starting with pure noise, the model iteratively denoises the sample over multiple timesteps, using the class label to steer the output. For example, if you want to generate an image of a cat, you’d pass the class label “cat” at every denoising step. To improve quality, techniques like classifier-free guidance can be used. This involves training the model to sometimes ignore the class label (by randomly dropping it during training) and then interpolating between conditioned and unconditioned predictions during sampling. Implementing this requires a slight tweak to the training loop: randomly replacing class labels with a null token (e.g., 10% of the time) and adjusting the sampling logic to blend predictions based on a guidance scale hyperparameter. This balances fidelity to the class with sample diversity.