DynamiCrafter: A Python repository from EMRAN19933

DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors

Jinbo Xing, Menghan Xia*, Yong Zhang, Haoxin Chen, Wangbo Yu,
Hanyuan Liu, Xintao Wang, Tien-Tsin Wong*, Ying Shan

(* corresponding authors)

From CUHK and Tencent AI Lab.

🔆 Introduction

🔥🔥 New Update Rolls Out for DynamiCrafter! Better Dynamic, Higher Resolution, and Stronger Coherence!

🤗 DynamiCrafter can animate open-domain still images based on text prompt by leveraging the pre-trained video diffusion priors. Please check our project page and paper for more information.
😀 We will continue to improve the model's performance.

👀 Seeking comparisons with Stable Video Diffusion and PikaLabs? Click the image below.

1.1. Showcases (576x1024)

1.2. Showcases (320x512)

1.3. Showcases (256x256)

"bear playing guitar happily, snowing"		"boy walking on the street"

2. Applications

2.1 Storytelling video generation (see project page for more details)

2.2 Looping video generation

2.3 Generative frame interpolation

Input starting frame	Input ending frame	Generated video

📝 Changelog

[2024.02.05]: 🔥🔥 Release high-resolution models (320x512 & 576x1024).
[2023.12.02]: Launch the local Gradio demo.
[2023.11.29]: Release the main model at a resolution of 256x256.
[2023.11.27]: Launch the project page and update the arXiv preprint.

🧰 Models

Model	Resolution	GPU Mem. & Inference Time (A100)	Checkpoint
DynamiCrafter1024	576x1024	18.3GB & 75s (`perframe_ae=True`)	Hugging Face
DynamiCrafter512	320x512	12.8GB & 20s (`perframe_ae=True`)	Hugging Face
DynamiCrafter256	256x256	11.9GB & 10s (`perframe_ae=False`)	Hugging Face

Currently, our DynamiCrafter can support generating videos of up to 16 frames with a resolution of 576x1024.

GPU memory consumed on RTX 4090 reported by @noguchis in Twitter: 18.3GB (576x1024), 12.8GB (320x512), 11.9GB (256x256).

⚙️ Setup

Install Environment via Anaconda (Recommended)

conda create -n dynamicrafter python=3.8.5
conda activate dynamicrafter
pip install -r requirements.txt

💫 Inference

1. Command line

Download pretrained models via Hugging Face, and put the model.ckpt with the required resolution in checkpoints/dynamicrafter_[1024|512|256]_v1/model.ckpt.
Run the commands based on your devices and needs in terminal.

  # Run on a single GPU:
  # Select the model based on required resolutions: i.e., 1024|512|320:
  sh scripts/run.sh 1024
  # Run on multiple GPUs for parallel inference:
  sh scripts/run_mp.sh 1024

2. Local Gradio demo

Download the pretrained models and put them in the corresponding directory according to the previous guidelines.
Input the following commands in terminal (choose a model based on the required resolution: 1024, 512 or 256).

  python gradio_app.py --res 1024

👨‍👩‍👧‍👦 Crafter Family

VideoCrafter1: Framework for high-quality video generation.

ScaleCrafter: Tuning-free method for high-resolution image/video generation.

TaleCrafter: An interactive story visualization tool that supports multiple characters.

LongerCrafter: Tuning-free method for longer high-quality video generation.

MakeYourVideo, might be a Crafter:): Video generation/editing with textual and structural guidance.

StyleCrafter: Stylized-image-guided text-to-image and text-to-video generation.

😉 Citation

@article{xing2023dynamicrafter,
  title={DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors},
  author={Xing, Jinbo and Xia, Menghan and Zhang, Yong and Chen, Haoxin and Yu, Wangbo and Liu, Hanyuan and Wang, Xintao and Wong, Tien-Tsin and Shan, Ying},
  journal={arXiv preprint arXiv:2310.12190},
  year={2023}
}

🙏 Acknowledgements

We would like to thank AK(@_akhaliq) for the help of setting up hugging face online demo, and camenduru for providing the replicate & colab online demo.

📢 Disclaimer

We develop this repository for RESEARCH purposes, so it can only be used for personal/research/non-commercial purposes.

EMRAN19933/DynamiCrafter