Instructions to use qihoo360/FancyVideo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use qihoo360/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("qihoo360/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
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license: apache-2.0
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license: apache-2.0
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language:
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- en
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tags:
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- fancyvideo
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- video-generation
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- text-to-video
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# FancyVideo
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This repository is the official implementation of [FancyVideo](https://360cvgroup.github.io/FancyVideo/).
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**[FancyVideo: Towards Dynamic and Consistent Video Generation via Cross-frame Textual Guidance](https://arxiv.org/abs/2408.08189)**
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</br>
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Jiasong Feng*, Ao Ma*, Jing Wang*, Bo Cheng, Xiaodan Liang, Dawei Leng†, Yuhui Yin(*Equal Contribution, ✝Corresponding Author)
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</br>
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[](https://arxiv.org/abs/2408.08189)
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[](https://360cvgroup.github.io/FancyVideo/)
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Our code builds upon [AnimateDiff](https://github.com/guoyww/AnimateDiff), and we also incorporate insights from [CV-VAE](https://github.com/AILab-CVC/CV-VAE), [Res-Adapter](https://github.com/bytedance/res-adapter), and [Long-CLIP](https://github.com/beichenzbc/Long-CLIP) to enhance our project. We appreciate the open-source contributions of these works.
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