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# MiniMax-Remover BMO - ComfyUI Integration
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🎉 **High-Quality Video Object Removal**
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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.
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## 🔥 Key Features
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- ✅ **Proper VAE Normalization**: Uses official `latents_mean` and `latents_std` from VAE config
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- ✅ **Optimized Scheduler**: Properly configured UniPCMultistepScheduler for flow prediction
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- ✅ **Dimension Compatibility**: Perfect VAE output vs transformer input alignment
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- ✅ **Official Parameters**: Uses optimal defaults (12 steps, 6 iterations)
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- ✅ **Natural Results**: Produces solid, realistic inpainting with clean edges
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## 📥 Installation
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***CLONE THE REPO***
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### Method 1: Automatic Setup (Recommended)
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1. Run the setup script: this will move all the files you need directly into you comfy ui custom nodes section for you.
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```bash
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python setup_comfyui_integration_bmo.py
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```
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2. Follow the prompts to specify your ComfyUI path
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3. Restart ComfyUI completely
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### Method 2: Manual Installation
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1. Copy these files to your ComfyUI custom_nodes directory:
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```
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ComfyUI/custom_nodes/minimax-remover-bmo/
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├── __init__.py
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├── minimax_mask_node_bmo.py
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├── pipeline_minimax_remover_bmo.py
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└── transformer_minimax_remover.py
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└──Models
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├── vae/
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│ ├── config.json
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│ └── diffusion_pytorch_model.safetensors
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├── transformer/
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│ ├── config.json
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│ └── diffusion_pytorch_model.safetensors
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└── scheduler/
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└── scheduler_config.json
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```
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2. Restart ComfyUI
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## 🚀 Usage
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### In ComfyUI:
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1. **Add the node**: Look for "MiniMax-Remover (BMO)" in the MiniMax-Remover category
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2. **Connect inputs**:
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- `images`: Your video frames as IMAGE type
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- `masks`: Your binary masks as MASK type
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3. **Set parameters**:
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- `num_inference_steps`: 12 (official default, good quality/speed balance)
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- `iterations`: 6 (mask expansion iterations, official default)
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- `seed`: Any number for reproducible results
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- `model_path`: Path to your MiniMax models (default: "models/")
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4. **Run**: The output will be clean, natural-looking inpainting!
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### Model Setup (also shown above with main files)
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Place your MiniMax-Remover models in the same custom nodes section as your main files from installation above:
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```
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ComfyUI/custom_nodes/minimax-remover-bmo/models
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├── vae/
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│ ├── config.json
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│ └── diffusion_pytorch_model.safetensors
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├── transformer/
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│ ├── config.json
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│ └── diffusion_pytorch_model.safetensors
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└── scheduler/
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└── scheduler_config.json
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```
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## 📊 Performance
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- **Processing Time**: ~1-2 seconds for 5 frames at 288x528
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- **Memory Usage**: Efficient with proper tensor management
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- **Quality**: Natural, solid inpainting results
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## 🐛 Troubleshooting
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### Poor quality results:
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- Ensure you're using the BMO node (latest version)
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- Try different seeds
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- Adjust mask expansion (iterations parameter)
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### Memory issues:
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- Use smaller input resolutions
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- Enable model offloading in ComfyUI
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## 📝 Example Workflow
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1. **Load Video**: Use VHS nodes to load your input video
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2. **Create Masks**: Use masking tools or load pre-made masks
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3. **Process**: Connect to "MiniMax-Remover (BMO)" node
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4. **Output**: Save or preview the cleaned video
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## 🎯 Best Practices
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- **Resolution**: Works best with resolutions divisible by 16
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- **Mask Quality**: Clean, binary masks work best
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- **Iterations**: 6-10 for most cases, higher for larger objects
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- **Steps**: 12 is optimal, 8-20 range depending on quality needs
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## 🔗 Links
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- [Original MiniMax-Remover Paper](https://arxiv.org/abs/2412.09940)
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- [Official Implementation](https://github.com/miraikan-research/MiniMax-Remover)
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- [ComfyUI](https://github.com/comfyanonymous/ComfyUI)
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---
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**Happy inpainting!** 🎨✨
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@@ -1,8 +0,0 @@
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import torch
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print("✅ CUDA available:", torch.cuda.is_available())
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if torch.cuda.is_available():
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print("🚀 GPU:", torch.cuda.get_device_name(0))
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else:
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print("�� Running on CPU")
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@@ -1,29 +0,0 @@
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# BMO MiniMax-Remover Requirements for ComfyUI
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# High-quality video object removal dependencies
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# NOTE: Install PyTorch with CUDA support first:
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# pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
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# Core ML/AI Libraries
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diffusers>=0.28.0
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transformers>=4.40.0
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accelerate>=0.21.0
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# Tensor Operations
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einops>=0.7.0
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scipy>=1.11.0
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# Image/Video Processing
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numpy>=1.24.0
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opencv-python>=4.8.0
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pillow>=10.0.0
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# Utilities
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tqdm>=4.65.0
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# Optional (for enhanced functionality)
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xformers # For memory efficiency (optional)
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bitsandbytes # For model quantization (optional)
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# Note: ComfyUI should already provide most of these dependencies
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# This file ensures compatibility for standalone usage
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