ComfyUI-MiniMax-Remover is a custom node for ComfyUI that enables fast and efficient object removal using minimax optimization. It works in two stages: first, it trains a remover with a simplified DiT model; then it distills a robust version using CFG guidance and fewer inference steps.
See the codeA ComfyUI custom node for fast and effective video/image object removal using MiniMax optimization. Remove objects from videos and images with high quality results using only 6-12 inference steps.
cd ComfyUI/custom_nodes
git clone https://github.com/1038lab/ComfyUI-MiniMax-Remover.git
cd ComfyUI-MiniMax-Remover
For Portable ComfyUI (Windows):
..\..\..\python_embeded\python.exe -m pip install -r requirements.txt
For Standard Python Installation:
pip install -r requirements.txt
Models will be automatically downloaded from HuggingFace when first used.
If automatic download fails or you prefer manual installation:
huggingface-cli download zibojia/minimax-remover --include vae transformer scheduler --local-dir ./models/MiniMax-Remover
Or download manually from HuggingFace:
vae, transformer, and scheduler foldersComfyUI/models/MiniMax-Remover/Verify installation:
ComfyUI/models/MiniMax-Remover/ contains:
vae/ foldertransformer/ folderscheduler/ folder| Node Name | Display Name | Purpose |
|---|---|---|
MinimaxImageRemover | MiniMax Image Object Remover | Remove objects from single images (requires external model loader) |
MinimaxVideoRemover | MiniMax Video Object Remover | Remove objects from videos (requires external model loader) |
MinimaxModelLoader | MiniMax Model Loader | Load VAE, Transformer, and Scheduler for separate nodes |
MinimaxVideoLoader | MiniMax Video Loader | Load video files for processing |
ImageSizeAdjuster | MiniMax Image Size Adjuster | Adjust image dimensions for compatibility |
FlowMatchEulerDiscreteScheduler (recommended) or UniPCMultistepSchedulerfloat16 (GPU recommended) or float32 (CPU/compatibility)auto (automatic detection)float16 precision (reduces memory by ~50%)FlowMatchEulerDiscreteScheduler is typically faster than UniPCMultistepSchedulertemporal_frames=3 provides best speed/quality balanceImageSizeAdjuster node to optimize image dimensions for better performanceAll dependencies are automatically installed via requirements.txt:
torch>=1.13.0
diffusers>=0.21.0
decord>=0.6.0
einops>=0.6.0
scipy>=1.9.0
opencv-python>=4.5.0
huggingface_hub>=0.16.0
accelerate>=0.20.0
Model Download Fails
ComfyUI/models/MiniMax-Remover/CUDA Out of Memory
float16 instead of float32num_inference_stepsTensor Dimension Errors
fix_dimensions in the All-in-One node for automatic adjustmentImageSizeAdjuster node for manual dimension controlDimension Warning Messages
fix_dimensions=TruePoor Removal Quality
num_inference_steps (try 16-20)mask_dilation_iterations based on mask precision
This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.
Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
If you find this project helpful, please consider giving it a star on GitHub! It helps others discover the project and motivates continued development.
Made with ❤️ for the ComfyUI community
22 commits
Python
100.0%
ComfyUI-MiniMax-Remover is a custom node for ComfyUI that enables fast and efficient object removal using minimax optimization. It works in two stages: first, it trains a remover with a simplified DiT model; then it distills a robust version using CFG guidance and fewer inference steps.
See the codeA ComfyUI custom node for fast and effective video/image object removal using MiniMax optimization. Remove objects from videos and images with high quality results using only 6-12 inference steps.
cd ComfyUI/custom_nodes
git clone https://github.com/1038lab/ComfyUI-MiniMax-Remover.git
cd ComfyUI-MiniMax-Remover
For Portable ComfyUI (Windows):
..\..\..\python_embeded\python.exe -m pip install -r requirements.txt
For Standard Python Installation:
pip install -r requirements.txt
Models will be automatically downloaded from HuggingFace when first used.
If automatic download fails or you prefer manual installation:
huggingface-cli download zibojia/minimax-remover --include vae transformer scheduler --local-dir ./models/MiniMax-Remover
Or download manually from HuggingFace:
vae, transformer, and scheduler foldersComfyUI/models/MiniMax-Remover/Verify installation:
ComfyUI/models/MiniMax-Remover/ contains:
vae/ foldertransformer/ folderscheduler/ folder| Node Name | Display Name | Purpose |
|---|---|---|
MinimaxImageRemover | MiniMax Image Object Remover | Remove objects from single images (requires external model loader) |
MinimaxVideoRemover | MiniMax Video Object Remover | Remove objects from videos (requires external model loader) |
MinimaxModelLoader | MiniMax Model Loader | Load VAE, Transformer, and Scheduler for separate nodes |
MinimaxVideoLoader | MiniMax Video Loader | Load video files for processing |
ImageSizeAdjuster | MiniMax Image Size Adjuster | Adjust image dimensions for compatibility |
FlowMatchEulerDiscreteScheduler (recommended) or UniPCMultistepSchedulerfloat16 (GPU recommended) or float32 (CPU/compatibility)auto (automatic detection)float16 precision (reduces memory by ~50%)FlowMatchEulerDiscreteScheduler is typically faster than UniPCMultistepSchedulertemporal_frames=3 provides best speed/quality balanceImageSizeAdjuster node to optimize image dimensions for better performanceAll dependencies are automatically installed via requirements.txt:
torch>=1.13.0
diffusers>=0.21.0
decord>=0.6.0
einops>=0.6.0
scipy>=1.9.0
opencv-python>=4.5.0
huggingface_hub>=0.16.0
accelerate>=0.20.0
Model Download Fails
ComfyUI/models/MiniMax-Remover/CUDA Out of Memory
float16 instead of float32num_inference_stepsTensor Dimension Errors
fix_dimensions in the All-in-One node for automatic adjustmentImageSizeAdjuster node for manual dimension controlDimension Warning Messages
fix_dimensions=TruePoor Removal Quality
num_inference_steps (try 16-20)mask_dilation_iterations based on mask precision
This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.
Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
If you find this project helpful, please consider giving it a star on GitHub! It helps others discover the project and motivates continued development.
Made with ❤️ for the ComfyUI community
22 commits
Python
100.0%