Instructions to use 360ZhiNao/FancyVideo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use 360ZhiNao/FancyVideo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("360ZhiNao/FancyVideo", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
language:
- en
tags:
- fancyvideo
- video-generation
- text-to-video
- image-to-video
FancyVideo
This repository is the official implementation of FancyVideo.
FancyVideo: Towards Dynamic and Consistent Video Generation via Cross-frame Textual Guidance
Jiasong Feng*, Ao Ma*, Jing Wang*, Bo Cheng, Xiaodan Liang, Dawei Leng†, Yuhui Yin(*Equal Contribution, ✝Corresponding Author)
We Are Hiring
We are seeking academic interns in the AIGC field. If interested, please send your resume to maao@360.cn.
BibTeX
@misc{feng2024fancyvideodynamicconsistentvideo,
title={FancyVideo: Towards Dynamic and Consistent Video Generation via Cross-frame Textual Guidance},
author={Jiasong Feng and Ao Ma and Jing Wang and Bo Cheng and Xiaodan Liang and Dawei Leng and Yuhui Yin},
year={2024},
eprint={2408.08189},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2408.08189},
}
License
This project is licensed under the Apache License (Version 2.0).