Official PyTorch implementation of One-Minute Video Generation with Test-Time Training
Official PyTorch implementation of One-Minute Video Generation with Test-Time Training
TTT-Video is a repository for finetuning diffusion transformers for style transfer and context extension. We use Test-Time Training (TTT) layers to handle long-range relationships across the global context, while reusing the original pretrained model's attention layers for local attention on each three second segment.
In this repository, we include training and inference code for 63 second video generation. We finetune our model first at the original pretrained 3 second video length for style transfer and incorporating TTT layers. Then, we train in stages at video lengths of 9 sec, 18 sec, 30 sec, and 63 sec for context extension.
Our architecture adapts the CogVideoX 5B model, a diffusion transformer for text-to-video generation, by incorporating TTT layers. The original pretrained attention layers are retained for local attention on each 3-second segment and its corresponding text. In addition, TTT layers are inserted to process the global sequence as well as its reversed version, with their outputs gated through a residual connection.
To extend context beyond the pretrained 3-second segments, each segment is interleaved with text and video embeddings.
For a more detailed explanation of our architecture, please refer to our paper.
You can install dependencies needed for this project with conda (recommended) or a virtual environment.
conda env create -f environment.yaml
conda activate ttt-videopip install -e .After installing the dependencies, you must install the TTT-MLP kernel.
git submodule update --init --recursive
(cd ttt-tk && python setup.py install)Note: You must have cuda toolkit (12.3+) and gcc11+ installed to build the TTT-MLP kernel. We only support training on H100s for TTT-MLP. You can install cuda toolkit here.
Please follow the instructions here to get the VAE and T5 encoder. To get the pretrained weights, download the diffusion_pytorch_model-00001-of-00002.safetensors and diffusion_pytorch_model-00002-of-00002.safetensors files from HuggingFace.
Note: We only use the 5B weights, not the 2B weights. Make sure you are downloading the correct model.
Note: These are the pretrained model weights, not the final model weights used to generate our demonstration videos.
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