Instructions to use 8ruceLi/VFXMaster with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use 8ruceLi/VFXMaster with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("8ruceLi/VFXMaster", 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
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README.md
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## VFXMaster: Unlocking Dynamic Visual Effect Generation via In-Context Learning
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> <br>
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> [Baolu Li\*](https://libaolu312.github.io/), [Yiming Zhang\*](https://zh1ym.github.io/), [Qinghe Wang\*β ](https://qinghew.github.io/), [Liqian Maβ](https://charliememory.github.io/), [Xiaoyu Shi](https://xiaoyushi97.github.io/), [Xintao Wang](https://xinntao.github.io/), [Pengfei Wan](https://magicwpf.github.io/), [Zhenfei Yin](https://yinzhenfei.github.io/), [Yunzhi Zhuge](https://daiqi1989.github.io/), [Huchuan Lu](https://scholar.google.com/citations?user=D3nE0agAAAAJ&hl=en), [Xu jiaβ ](https://stephenjia.github.io/),
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> <br>
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> \* equal contribution β project leader β corresponding author
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### Folder Structure
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```
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VFXMaster
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βββ training_weight
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βββ VFXMaster_Weight
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β βββ In-Context-Conditioning
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β βββ One-shot-Adaptation
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βββ CogVideoX-Fun-V1.1-2b-InP
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```
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###
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---
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license: apache-2.0
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---
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