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A team of researchers from NVIDIA, University of Chicago, and University of Maryland has unveiled PYoCo, a large-scale text-to-video diffusion model built upon the foundations of eDiff-I, a cutting-edge image generation model, with the addition of a novel video noise prior.
According to the developers, the model incorporates various effective techniques from prior studies, such as temporal attention, joint image-video fine-tuning, a cascaded generation architecture, and an ensemble of expert denoisers, surpassing other methods on numerous benchmark datasets. The paper shared by the team also highlighted the model's ability to achieve high-quality zero-shot video synthesis, boasting superior photorealism and temporal consistency.
"We propose a video diffusion noise prior tailored for fine-tuning text-to-image diffusion models for text-to-video synthesis," comments the team. "We show that fine-tuning a text-to-image diffusion model with this prior leads to better knowledge transfer and efficient training. On the small-scale unconditional generation benchmark, we achieve a new state-of-the-art with a 10× smaller model and 14× less training time. On the zero-shot MSR-VTT evaluation, our model achieves a new state-of-the-art FID of 9.73."
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