Distributed training can be applied to diffusion models by splitting computational workloads across multiple GPUs or machines, enabling faster training and scaling to larger datasets and model sizes. The key approaches include data parallelism, model parallelism, and specialized strategies tailored to diffusion processes. These methods address the high computational demands of diffusion models, which involve iterative denoising steps over many timesteps and large neural networks like U-Nets.
One common approach is data parallelism, where each GPU processes a subset of the training data. For example, a batch of images can be split across GPUs, with each device computing gradients for its portion. Frameworks like PyTorch Distributed or Horovod synchronize gradients across devices to update the model consistently. In diffusion models, each training step involves sampling a random timestep and applying noise, so synchronization must account for the varying timestep distributions across GPUs. Libraries like Hugging Face’s Diffusers use PyTorch’s DistributedDataParallel to handle this, ensuring that gradients from all timesteps are aggregated correctly. This approach works well when the model fits on a single GPU but scales poorly if the model itself is too large.
For larger models, model parallelism splits the network across devices. For instance, a U-Net’s encoder and decoder layers can be placed on separate GPUs, with activations passed between them during forward and backward passes. NVIDIA’s EDM framework uses this strategy for training massive diffusion models, splitting the U-Net into segments to reduce per-GPU memory usage. Pipeline parallelism, a variant of model parallelism, divides the model into stages (e.g., processing different timesteps sequentially) to overlap computation and communication. However, this requires careful management to avoid bottlenecks, especially in diffusion models where timesteps are interdependent.
Finally, specialized strategies address unique aspects of diffusion. For example, since diffusion involves many timesteps, distributing the denoising process itself can help. One approach assigns subsets of timesteps to different GPUs, with each device handling a range of noise levels. Another method uses parameter-efficient techniques like distributing the noise prediction network across devices. Frameworks like DeepSpeed or FairScale can optimize memory usage via Zero Redundancy Optimizer (ZeRO), which partitions optimizer states across GPUs. Stability AI’s Stable Diffusion, for instance, likely combines these techniques to train on large clusters, though implementation details are not public. Developers should balance communication overhead, memory constraints, and model architecture when choosing a strategy.