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MiniMax-Remover

(★ 593)

This is the official implementation of our paper: "MiniMax-Remover: Taming Bad Noise Helps Video Object Removal"

파일 탐색기

최종 버전 다운로드 (.zip)

- - 0.mp4
  - 1.mp4
  - 2.mp4
  - 3.mp4
  - 4.mp4
- - 0.mp4
  - 1.mp4
  - 2.mp4
  - 3.mp4
  - 4.mp4
  - 5.mp4
  - 6.mp4
- - __init__.py
  - automatic_mask_generator.py
  - build_sam.py
  - sam2_image_predictor.py
  - sam2_video_predictor.py
- image_demo.py
- pipeline_minimax_remover.py
- test.py
- transformer_minimax_remover.py
- gradio_demo.gif
pipeline_minimax_remover.py
README.md
requirements.txt
test_minimax_remover.py
transformer_minimax_remover.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/zibojia/MiniMax-Remover

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd MiniMax-Remover

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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<h1 align="center">
  <span style="color:#2196f3;"><b>MiniMax</b></span><span style="color:#f06292;"><b>-Remover</b></span>: Taming Bad Noise Helps Video Object Removal
</h1>

<p align="center">
  Bojia Zi<sup>*</sup>,
  Weixuan Peng<sup>*</sup>,
  Xianbiao Qi<sup>†</sup>,
  Jianan Wang, Shihao Zhao, Rong Xiao, Kam-Fai Wong<br>
  <sup>*</sup> Equal contribution. <sup>†</sup> Corresponding author.
</p>

<p align="center">
  <a href="https://huggingface.co/zibojia/minimax-remover"><img alt="Huggingface Model" src="https://img.shields.io/badge/%F0%9F%A4%97%20Huggingface-Model-brightgreen"></a>
  <a href="https://github.com/zibojia/MiniMax-Remover"><img alt="Github" src="https://img.shields.io/badge/MiniMaxRemover-github-black"></a>
  <a href="https://huggingface.co/spaces/zibojia/MiniMax-Remover"><img alt="Huggingface Space" src="https://img.shields.io/badge/%F0%9F%A4%97%20Huggingface-Space-1e90ff"></a>
  <a href="https://arxiv.org/abs/2505.24873"><img alt="arXiv" src="https://img.shields.io/badge/MiniMaxRemover-arXiv-b31b1b"></a>
  <a href="https://www.youtube.com/watch?v=KaU5yNl6CTc"><img alt="YouTube" src="https://img.shields.io/badge/Youtube-video-ff0000"></a>
  <a href="https://minimax-remover.github.io"><img alt="Demo Page" src="https://img.shields.io/badge/Website-Demo%20Page-yellow"></a>
  <a href="https://replicate.com/ayushunleashed/minimax-remover"><img alt="Replicate" src="https://replicate.com/cjwbw/i2vgen-xl/badge"></a>
</p>

---

## 🚀 Overview

**MiniMax-Remover** is a fast and effective video object remover based on minimax optimization. It operates in two stages: the first stage trains a remover using a simplified DiT architecture, while the second stage distills a robust remover with CFG removal and fewer inference steps.

---

## ✨ Features:

* **Fast:** Requires only 6 inference steps and does not use CFG, making it highly efficient.

* **Effective:** Seamlessly removes objects from videos and generates high-quality visual content.

* **Robust:** Maintains robustness by preventing the regeneration of undesired objects or artifacts within the masked region, even under varying noise conditions.

---

## 🛠️ Installation

All dependencies are listed in `requirements.txt`.

```bash
pip install -r requirements.txt
```

---

## 🏃‍♂️ Gradio Demo

<p align="center">
  <a href="https://youtu.be/1V7Ov4vmnBc" target="_blank">
    <img src="./imgs/gradio_demo.gif" alt="firstpage" style="width:80%;" />
  </a>
</p>

You can use this gradio demo to remove objects. Note that you don't need to compile the sam2.
```bash
cd gradio_demo
python3 test.py
```

---

## 📂 Download

```shell
huggingface-cli download zibojia/minimax-remover --include vae transformer scheduler --local-dir .
```

---

## ⚡ Quick Start

### Minimal Example

```python
import torch
from diffusers.utils import export_to_video
from decord import VideoReader
from diffusers.models import AutoencoderKLWan
from transformer_minimax_remover import Transformer3DModel
from diffusers.schedulers import UniPCMultistepScheduler
from pipeline_minimax_remover import Minimax_Remover_Pipeline

random_seed = 42
video_length = 81
device = torch.device("cuda:0")

# Load model weights separately
vae = AutoencoderKLWan.from_pretrained("./vae", torch_dtype=torch.float16)
transformer = Transformer3DModel.from_pretrained("./transformer", torch_dtype=torch.float16)
scheduler = UniPCMultistepScheduler.from_pretrained("./scheduler")

images = # images in range [-1, 1]
masks = # masks in range [0, 1]

# Initialize the pipeline (pass the loaded weights as objects)
pipe = Minimax_Remover_Pipeline(vae=vae, transformer=transformer, \
    scheduler=scheduler, torch_dtype=torch.float16
).to(device)

result = pipe(images=images, masks=masks, num_frames=video_length, height=480, width=832, \
    num_inference_steps=12, generator=torch.Generator(device=device).manual_seed(random_seed), iterations=6 \
).frames[0]
export_to_video(result, "./output.mp4")
```
---

## 📧 Contact

Feel free to send an email to [19210240030@fudan.edu.cn](mailto:19210240030@fudan.edu.cn) if you have any questions or suggestions.