PyramidalWan: On Making Pretrained Video Model Pyramidal for Efficient Inference
Recently proposed pyramidal models decompose the conventional forward and backward diffusion processes into multiple stages operating at varying resolutions. These models handle inputs with higher noise levels at lower resolutions, while less noisy inputs are processed at higher resolutions. This hierarchical approach significantly reduces the computational cost of inference in multi-step denoising models. However, existing open-source pyramidal video models have been trained from scratch and tend to underperform compared to state-of-the-art systems in terms of visual plausibility. In this work, we present a pipeline that converts a pretrained diffusion model into a pyramidal one through low-cost finetuning, achieving this transformation without degradation in quality of output videos. Furthermore, we investigate and compare various strategies for step distillation within pyramidal models, aiming to further enhance the inference efficiency. Our results are available at https://qualcomm-ai-research.github.io/PyramidalWan.
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Denis Korzhenkov
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Adil Karjauv
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Animesh Karnewar
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Mohsen Ghafoorian
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Amirhossein Habibian
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2026-01-09T15:45:49.633Z
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Amir Habibian
habibian
user
habibian
PyramidalWan: On Making Pretrained Video Model Pyramidal for Efficient Inference
Recently proposed pyramidal models decompose the conventional forward and backward diffusion processes into multiple stages operating at varying resolutions. These models handle inputs with higher noise levels at lower resolutions, while less noisy inputs are processed at higher resolutions. This hierarchical approach significantly reduces the computational cost of inference in multi-step denoising models. However, existing open-source pyramidal video models have been trained from scratch and tend to underperform compared to state-of-the-art systems in terms of visual plausibility. In this work, we present a pipeline that converts a pretrained diffusion model into a pyramidal one through low-cost finetuning, achieving this transformation without degradation in quality of output videos. Furthermore, we investigate and compare various strategies for step distillation within pyramidal models, aiming to further enhance the inference efficiency. Our results are available at https://qualcomm-ai-research.github.io/PyramidalWan.
Pyramidal diffusion models reduce computational cost through hierarchical resolution processing, with pretrained models converted via low-cost fine-tuning maintaining output quality while enabling efficient inference.
pyramidal models diffusion models forward diffusion process backward diffusion process multi-step denoising models inference efficiency step distillation pretrained diffusion model low-cost fine-tuning