casterpollux-MiniMax-bmo/README.md

MiniMax-Remover: Taming Bad Noise Helps Video Object Removal

Bojia Zi^*, Weixuan Peng^*, Xianbiao Qi^†, Jianan Wang, Shihao Zhao, Rong Xiao, Kam-Fai Wong
^* Equal contribution. ^† Corresponding author.

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🚀 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.

📧 Contact

Feel free to send an email to 19210240030@fudan.edu.cn if you have any questions or suggestions.

MiniMax-Remover BMO - ComfyUI Integration Brendan@casterpollux.com if need assistance on comfy section

🎉 High-Quality Video Object Removal

This is the BMO implementation of MiniMax-Remover for ComfyUI that delivers natural, high-quality inpainting results. Based on the official MiniMax-Remover implementation with proper VAE normalization and dimension handling.

🔥 Key Features

• ✅ Proper VAE Normalization: Uses official latents_mean and latents_std from VAE config
• ✅ Optimized Scheduler: Properly configured UniPCMultistepScheduler for flow prediction
• ✅ Dimension Compatibility: Perfect VAE output vs transformer input alignment
• ✅ Official Parameters: Uses optimal defaults (12 steps, 6 iterations)
• ✅ Natural Results: Produces solid, realistic inpainting with clean edges

📥 Installation

Method 1: Automatic Setup (Recommended)

1. Run the setup script:

python setup_comfyui_integration_bmo.py

2. Follow the prompts to specify your ComfyUI path
3. Restart ComfyUI completely

Method 2: Manual Installation

1. Copy these files to your ComfyUI custom_nodes directory:

ComfyUI/custom_nodes/minimax-remover-bmo/
├── __init__.py
├── minimax_mask_node_bmo.py
├── pipeline_minimax_remover_bmo.py
└── transformer_minimax_remover.py

2. Restart ComfyUI

🚀 Usage

In ComfyUI:

1. Add the node: Look for "MiniMax-Remover (BMO)" in the MiniMax-Remover category

2. Connect inputs:

   • images: Your video frames as IMAGE type
   • masks: Your binary masks as MASK type

3. Set parameters:

   • num_inference_steps: 12 (official default, good quality/speed balance)
   • iterations: 6 (mask expansion iterations, official default)
   • seed: Any number for reproducible results
   • model_path: Path to your MiniMax models (default: "models/")

4. Run: The output will be clean, natural-looking inpainting!

Model Setup

Place your MiniMax-Remover models in this structure and paste the path into the comfy ui node:

ComfyUI/custom_nodes/minimax-remover-bmo/models
├── vae/
│   ├── config.json
│   └── diffusion_pytorch_model.safetensors
├── transformer/
│   ├── config.json
│   └── diffusion_pytorch_model.safetensors
└── scheduler/
    └── scheduler_config.json

📊 Performance

• Processing Time: ~1-2 seconds for 5 frames at 288x528
• Memory Usage: Efficient with proper tensor management
• Quality: Natural, solid inpainting results

🐛 Troubleshooting

Poor quality results:

• Ensure you're using the BMO node (latest version)
• Try different seeds
• Adjust mask expansion (iterations parameter)

Memory issues:

• Use smaller input resolutions
• Enable model offloading in ComfyUI

📝 Example Workflow

1. Load Video: Use VHS nodes to load your input video
2. Create Masks: Use masking tools or load pre-made masks
3. Process: Connect to "MiniMax-Remover (BMO)" node
4. Output: Save or preview the cleaned video

🎯 Best Practices

• Resolution: Works best with resolutions divisible by 16
• Mask Quality: Clean, binary masks work best
• Iterations: 6-10 for most cases, higher for larger objects
• Steps: 12 is optimal, 8-20 range depending on quality needs

🔗 Links

• Original MiniMax-Remover Paper
• Official Implementation
• ComfyUI

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Original Project Description