# Latte  [Latte: Latent Diffusion Transformer for Video Generation](https://arxiv.org/abs/2401.03048) from Monash University, Shanghai AI Lab, Nanjing University, and Nanyang Technological University. The abstract from the paper is: *We propose a novel Latent Diffusion Transformer, namely Latte, for video generation. Latte first extracts spatio-temporal tokens from input videos and then adopts a series of Transformer blocks to model video distribution in the latent space. In order to model a substantial number of tokens extracted from videos, four efficient variants are introduced from the perspective of decomposing the spatial and temporal dimensions of input videos. To improve the quality of generated videos, we determine the best practices of Latte through rigorous experimental analysis, including video clip patch embedding, model variants, timestep-class information injection, temporal positional embedding, and learning strategies. Our comprehensive evaluation demonstrates that Latte achieves state-of-the-art performance across four standard video generation datasets, i.e., FaceForensics, SkyTimelapse, UCF101, and Taichi-HD. In addition, we extend Latte to text-to-video generation (T2V) task, where Latte achieves comparable results compared to recent T2V models. We strongly believe that Latte provides valuable insights for future research on incorporating Transformers into diffusion models for video generation.* **Highlights**: Latte is a latent diffusion transformer proposed as a backbone for modeling different modalities (trained for text-to-video generation here). It achieves state-of-the-art performance across four standard video benchmarks - [FaceForensics](https://arxiv.org/abs/1803.09179), [SkyTimelapse](https://arxiv.org/abs/1709.07592), [UCF101](https://arxiv.org/abs/1212.0402) and [Taichi-HD](https://arxiv.org/abs/2003.00196). To prepare and download the datasets for evaluation, please refer to [this https URL](https://github.com/Vchitect/Latte/blob/main/docs/datasets_evaluation.md). Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.md) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. ### Inference Use [`torch.compile`](https://huggingface.co/docs/diffusers/main/en/tutorials/fast_diffusion#torchcompile) to reduce the inference latency. First, load the pipeline: ```python import torch from diffusers import LattePipeline pipeline = LattePipeline.from_pretrained( "maxin-cn/Latte-1", torch_dtype=torch.float16 ).to("cuda") ``` Then change the memory layout of the pipelines `transformer` and `vae` components to `torch.channels-last`: ```python pipeline.transformer.to(memory_format=torch.channels_last) pipeline.vae.to(memory_format=torch.channels_last) ``` Finally, compile the components and run inference: ```python pipeline.transformer = torch.compile(pipeline.transformer) pipeline.vae.decode = torch.compile(pipeline.vae.decode) video = pipeline(prompt="A dog wearing sunglasses floating in space, surreal, nebulae in background").frames[0] ``` The [benchmark](https://gist.github.com/a-r-r-o-w/4e1694ca46374793c0361d740a99ff19) results on an 80GB A100 machine are: ``` Without torch.compile(): Average inference time: 16.246 seconds. With torch.compile(): Average inference time: 14.573 seconds. ``` ## LattePipeline [[autodoc]] LattePipeline - all - __call__