Instructions to use BirdL/FancyVideo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BirdL/FancyVideo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BirdL/FancyVideo", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 746 Bytes
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license: apache-2.0
pipeline_tag: text-to-video
---
Unoffical mirror of FancyVideo, requires SD-V1.5 or another other base model to be required seperately.
Inference and training code is located at https://github.com/360CVGroup/FancyVideo
8/20/24: An offical repo has been created at huggingface.co/qihoo360/FancyVideo
@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},
} |