Instructions to use FudanCVL/EffectErase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FudanCVL/EffectErase with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FudanCVL/EffectErase", 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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# EffectErase: Joint Video Object Removal and Insertion for High-Quality Effect Erasing
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[](https://henghuiding.com/EffectErase/) [](https://cvpr.thecvf.com/virtual/2026/papers.html) [](https://github.com/FudanCVL/EffectErase) [](http://arxiv.org/) [](https://huggingface.co/datasets/FudanCVL/
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This repository provides the checkpoint `EffectErase.ckpt` for **EffectErase**.
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# EffectErase: Joint Video Object Removal and Insertion for High-Quality Effect Erasing
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[](https://henghuiding.com/EffectErase/) [](https://cvpr.thecvf.com/virtual/2026/papers.html) [](https://github.com/FudanCVL/EffectErase) [](http://arxiv.org/) [](https://huggingface.co/datasets/FudanCVL/EffectErase)
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This repository provides the checkpoint `EffectErase.ckpt` for **EffectErase**.
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