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As climate change drives more extreme wildfire behavior, accurate and computationally efficient fire spread modeling is increasingly critical for monitoring, mitigation, and risk assessment. Wildfires pose a particularly challenging modeling problem due to their complex interactions with fuels, terrain, and atmospheric conditions, as well as their potential to impact populated regions with severe environmental, economic, and human consequences. These challenges motivate the development of surrogate modeling approaches capable of emulating physics-based wildfire simulations at substantially reduced computational cost. In this work, we present a systematic comparison of two deep learning surrogate model architectures for spatiotemporal wildfire emulation: a convolutional neural network-based generative model and a conditional diffusion model. Both approaches are designed to be grid-invariant and trained to predict three key wildfire variables – time of arrival, flame length, and burn scar – at fixed 15-minute time steps. Model performance is evaluated using an autoregressive rollout procedure in which successive short-term predictions are recursively fed back as inputs to simulate wildfire evolution over 12-hour time horizons. The training data consists of wildfire simulations generated using a Rothermel-based fire spread model with realistic, satellite-derived fuel distributions over the western United States (California and Nevada). Evaluation is performed on geographically distinct fire scenarios to assess generalization across diverse fuel configurations. Both surrogate models are shown to produce stable and physically plausible wildfire dynamics over 12-hour autoregressive rollouts while reducing inference time relative to physics-based solvers. The results highlight the potential of deep generative surrogates to enable rapid ensemble-based risk assessment and support operational fire management workflows under diverse environmental conditions.