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", torch_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
metadata
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}, }