Significant advancements have been achieved in the realm of large-scale pre-trained text-to-video Diffusion Models (VDMs). However, previous methods either rely solely on pixel-based VDMs, which come with high computational costs, or on latent-based VDMs, which often struggle with precise text-video alignment. In this paper, we are the first to propose a hybrid model, dubbed as Show-1, which marries pixel-based and latent-based VDMs for text-to-video generation. Our model first uses pixel-based VDMs to produce a low-resolution video of strong text-video correlation. After that, we propose a novel expert translation method that employs the latent-based VDMs to further upsample the low-resolution video to high resolution. Compared to latent VDMs, Show-1 can produce high-quality videos of precise text-video alignment; Compared to pixel VDMs, Show-1 is much more efficient (GPU memory usage during inference is 15G vs 72G). We also validate our model on standard video generation benchmarks. Our code and model weights are publicly available at \url{https://github.com/showlab/Show-1}.

翻译：在大规模预训练文本到视频扩散模型领域已取得重大进展。然而，现有方法要么依赖计算成本高昂的像素级扩散模型，要么依赖难以实现精确文本-视频对齐的潜在扩散模型。本文首次提出混合模型Show-1，该模型融合了像素级和潜在级扩散模型用于文本到视频生成。我们的模型首先使用像素级扩散模型生成具有强文本-视频关联的低分辨率视频，随后提出创新的专家翻译方法，利用潜在级扩散模型将低分辨率视频进一步上采样至高分辨率。与潜在扩散模型相比，Show-1能生成文本-视频对齐精确的高质量视频；与像素扩散模型相比，Show-1大幅提升效率（推理时GPU内存占用15G vs 72G）。我们在标准视频生成基准上验证了模型性能。代码与模型权重已开源至\url{https://github.com/showlab/Show-1}。