StreamingSVD is an advanced autoregressive technique for text-to-video and image-to-video generation, generating long hiqh-quality videos with rich motion dynamics, turning SVD into a long video generator. Our method ensures temporal consistency throughout the video, aligns closely to the input text/image, and maintains high frame-level image quality. Our demonstrations include successful examples of videos up to 200 frames, spanning 8 seconds, and can be extended for even longer durations.
The effectiveness of the underlying autoregressive approach is not limited to the specific base model used, indicating that improvements in base models can yield even higher-quality videos. StreamingSVD is part of the StreamingT2V family. Another successful implementation is StreamingModelscope, which is turning Modelscope into a long-video generator. This approach enables to generate videos of up to 2 minutes length, featuring high motion amount and no stagnation.
* [11/28/2024] Memory optimized version released!* [08/30/2024] Code and model released! The model weights are available on 🤗HuggingFace
Detailed results can be found in the Project page.
Our code requires 60 GB of VRAM in the default configuration when generating 200 frames. The memory-optimized version significantly reduces this to 24 GB of VRAM but operates approximately 50% slower than the default. To further reduce memory usage, consider lowering the number of frames or enabling randomized blending. Our code was tested on linux, using Python 3.9 and CUDA >= 11.8.
- Clone this repository and install requirements using CUDA >= 11.8:
git clone https://github.com/Picsart-AI-Research/StreamingT2V.git
cd StreamingT2V/
virtualenv -p python3.9 venv
source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt- Make sure FFMPEG is installed.
To run the entire pipeline consisting of image-to-video, video enhancement (including our randomized blending) and video-frame interpolation do from the StreamingT2V folder:
cd code
python inference_i2v.py --input $INPUT --output $OUTPUT$INPUT must be the path to an image file or a folder containing images. Each image is expected to have the aspect ratio 16:9.
$OUTPUT must be the path to a folder where the results will be stored.
- number of generated frames
Add --num_frames $FRAMES to the call to define the number of frames to be generated. Default value: $FRAMES=200
- use randomized blending
Add --use_randomized_blending $RB to the call to define whether to use randomized blending. Default value: $RB=False. When using randomized blending, the recommended values for chunk_size and overlap_size parameters are --chunk_size 38 and --overlap_size 12, respectively. Please be aware that randomized blending will slow down the generation process, so try to avoid it if you have enough GPU memory.
- output FPS
Add --out_fps $FPS to the call to define the FPS of the output video. Default value: $FPS=24
Use --use_memopt to enable memory optimizations for hardware with 24GB VRAM. If using a previously cloned repository, update the environment and delete the code/checkpoint/i2v_enhance folder to ensure the correct version is used.
- Technical report describing StreamingSVD.
- Release of StreamingSVD for text-to-video.
VRAM memory reduction.
Our proposed Motion Aware Warp Error (see our paper) is provided here.
The code for the StreamingT2V model based on Modelscope, as described in our paper, can be now found here.
Our code and model is published under the MIT license.
We include codes and model weights of SVD, EMA-VFI and I2VGen-XL. Please refer to their original licenses regarding their codes and weights. Due to these dependencies, StreamingSVD can be used only for non-commercial, research purposes.
- SVD: An image-to-video method.
- Align your steps: A method for optimizing sampling schedules.
- I2VGen-XL: An image-to-video method.
- EMA-VFI: A state-of-the-art video-frame interpolation method.
- Diffusers: A framework for diffusion models.
If you use our work in your research, please cite our publication:
@article{henschel2024streamingt2v,
title={StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text},
author={Henschel, Roberto and Khachatryan, Levon and Hayrapetyan, Daniil and Poghosyan, Hayk and Tadevosyan, Vahram and Wang, Zhangyang and Navasardyan, Shant and Shi, Humphrey},
journal={arXiv preprint arXiv:2403.14773},
year={2024}
}