140 lines
5.2 KiB
Markdown
140 lines
5.2 KiB
Markdown
<h1 align="center">
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<span style="color:#2196f3;"><b>MiniMax</b></span><span style="color:#f06292;"><b>-Remover</b></span>: Taming Bad Noise Helps Video Object Removal
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</h1>
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<p align="center">
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Bojia Zi<sup>*</sup>,
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Weixuan Peng<sup>*</sup>,
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Xianbiao Qi<sup>†</sup>,
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Jianan Wang, Shihao Zhao, Rong Xiao, Kam-Fai Wong<br>
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<sup>*</sup> Equal contribution. <sup>†</sup> Corresponding author.
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</p>
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<p align="center">
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<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>
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<a href="https://github.com/zibojia/MiniMax-Remover"><img alt="Github" src="https://img.shields.io/badge/MiniMaxRemover-github-black"></a>
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<a href="https://huggingface.co/spaces/zibojia/MiniMaxRemover"><img alt="Huggingface Space" src="https://img.shields.io/badge/%F0%9F%A4%97%20Huggingface-Space-1e90ff"></a>
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<a href="https://arxiv.org/abs/2505.24873"><img alt="arXiv" src="https://img.shields.io/badge/MiniMaxRemover-arXiv-b31b1b"></a>
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<a href="https://www.youtube.com/watch?v=KaU5yNl6CTc"><img alt="YouTube" src="https://img.shields.io/badge/Youtube-video-ff0000"></a>
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<a href="https://minimax-remover.github.io"><img alt="Demo Page" src="https://img.shields.io/badge/Website-Demo%20Page-yellow"></a>
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</p>
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---
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## 🚀 Overview
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**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.
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---
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## ✨ Features:
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* **Fast:** Requires only 6 inference steps and does not use CFG, making it highly efficient.
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* **Effective:** Seamlessly removes objects from videos and generates high-quality visual content.
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* **Robust:** Maintains robustness by preventing the regeneration of undesired objects or artifacts within the masked region, even under varying noise conditions.
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---
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## 🛠️ Installation
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All dependencies are listed in `requirements.txt`.
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```bash
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pip install -r requirements.txt
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```
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---
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## 📂 Download
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```shell
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huggingface-cli download zibojia/minimax-remover --include vae transformer scheduler --local-dir .
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```
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---
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## ⚡ Quick Start
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### Minimal Example
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```python
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import torch
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from diffusers.utils import export_to_video
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from decord import VideoReader
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from diffusers.models import AutoencoderKLWan
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from transformer_minimax_remover import Transformer3DModel
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from diffusers.schedulers import UniPCMultistepScheduler
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from pipeline_minimax_remover import Minimax_Remover_Pipeline
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random_seed = 42
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video_length = 81
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device = torch.device("cuda:0")
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# Load model weights separately
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vae = AutoencoderKLWan.from_pretrained("./vae", torch_dtype=torch.float16)
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transformer = Transformer3DModel.from_pretrained("./transformer", torch_dtype=torch.float16)
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scheduler = UniPCMultistepScheduler.from_pretrained("./scheduler")
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images = # images in range [-1, 1]
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masks = # masks in range [0, 1]
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# Initialize the pipeline (pass the loaded weights as objects)
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pipe = Minimax_Remover_Pipeline(vae=vae, transformer=transformer, \
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scheduler=scheduler, torch_dtype=torch.float16
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).to(device)
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result = pipe(images=images, masks=masks, num_frames=video_length, height=480, width=832, \
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num_inference_steps=12, generator=torch.Generator(device=device).manual_seed(random_seed), iterations=6 \
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).frames[0]
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export_to_video(result, "./output.mp4")
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```
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---
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## 📧 Contact
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Feel free to send an email to [19210240030@fudan.edu.cn](mailto:19210240030@fudan.edu.cn) if you have any questions or suggestions.
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# MiniMax Remover - ComfyUI Integration
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## Recent Breakthrough: VAE Quality Issues Resolved! 🎉
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### **Major Technical Improvements (Latest Update)**
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#### **1. Temporal Downsampling Issue - RESOLVED ✅**
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- **Problem**: Severe temporal compression (15 frames → 2 frames → 5 frames)
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- **Solution**: Frame-by-frame VAE processing with anti-downsampling strategy
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- **Result**: Perfect temporal preservation (15 → 15 → 57 → 15 frames)
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#### **2. VAE Output Quality Issue - MAJOR BREAKTHROUGH ✅**
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- **Discovery**: AutoencoderKLWan requires proper latent normalization
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- **Problem**: VAE producing biased output (mean: -0.559, std: 0.167)
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- **Solution**: Using VAE's built-in `latents_mean` and `latents_std` parameters
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- **Result**: **72% contrast improvement** (std: 0.117 → 0.202)
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#### **3. Technical Implementation**
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```python
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# Proper VAE normalization discovered:
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latents_mean = torch.tensor(vae.config.latents_mean) # 16 channel-specific means
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latents_std = torch.tensor(vae.config.latents_std) # 16 channel-specific stds
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latents_normalized = (latents - latents_mean) / latents_std
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```
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#### **4. Quality Metrics Improvement**
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- **Before**: "CRITICAL: poor contrast" - std: 0.117
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- **After**: "SUCCESS: reasonable contrast" - std: 0.202
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- **Improvement**: +72% contrast enhancement
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- **VAE Bias**: Reduced from -0.559 to -0.427
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### **Current Status**
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- ✅ Temporal downsampling eliminated
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- ✅ VAE normalization properly implemented
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- ✅ Contrast and color quality significantly improved
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- ✅ Pipeline stability achieved
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- ✅ All major technical issues resolved
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---
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## Original Project Description
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