Reference-Based Video Colorization
Dual implementation of reference-based video colorization featuring ColorMNet (2024) with DINOv2 and Deep Exemplar (2019). Includes 4 nodes (2 video, 2 image), multiple feature encoders (VGG19, DINOv2, CLIP), advanced post-processing (color-matcher, WLS, guided, bilateral), and auto-installer for dependencies.
How much VRAM does Reference-Based Video Colorization require?
Direct Answer: The ComfyUI node Reference-Based Video Colorization requires a minimum base VRAM of 4096MB and is optimized for GPUs with at least 8GB of VRAM. Low VRAM mode is not supported for this node.
- Base VRAM:
- 4096MB (4.0GB)
- Recommended GPU:
- 8GB+ VRAM
- Low VRAM Mode:
- ✗ Not supported
- Estimation Confidence:
- MEDIUM
Interactive VRAM Compatibility Estimator
Your GPU has plenty of headroom. You can run this node safely with your active configurations!
Deploy on High-Performance GPUs
Need more VRAM to run ComfyUI with this node? Rent low-cost, high-performance GPUs on Vast.ai instantly.
Deploy on Cloud GPUs
Need more VRAM to run ComfyUI with this node? Rent low-cost, high-performance cloud GPUs on RunPod instantly.
What Python packages are required for Reference-Based Video Colorization?
Direct Answer: Running Reference-Based Video Colorization requires installing the following Python package dependencies: einops>=0.7.0, gdown# For downloading model checkpoints from Google Drive, numpy>=1.24.0, opencv-python>=4.8.0, pillow>=9.5.0, progressbar2>=4.2.0, scikit-image>=0.21.0, torch>=2.0.0, torchvision>=0.15.0, tqdm>=4.65.0. This node specifically requires PyTorch version 2.0.0 or newer.
einops>=0.7.0
gdown# For downloading model checkpoints from Google Drive
numpy>=1.24.0
opencv-python>=4.8.0
pillow>=9.5.0
progressbar2>=4.2.0
scikit-image>=0.21.0
torch>=2.0.0
torchvision>=0.15.0
tqdm>=4.65.0Interactive Setup & Dependency Resolver
# Loading command...Special Environment Requirements:
Requires PyTorch version 2.0.0+.
# Loading PyTorch command...Frequently Asked Questions
How much VRAM does Reference-Based Video Colorization require?
Reference-Based Video Colorization requires a minimum of 4096MB (4.0GB) of VRAM for base operation. For optimal performance, a GPU with at least 8GB of VRAM is recommended. Low VRAM mode is not supported for this node.
Can I run Reference-Based Video Colorization on an RTX 3060, RTX 4070, or RTX 4090?
✅ RTX 3060 (12GB): Yes, fully compatible with 6.8GB headroom. ✅ RTX 4070 (12GB): Yes, fully compatible with 6.8GB headroom. ✅ RTX 4070 Ti (16GB): Yes, fully compatible with 10.4GB headroom. ✅ RTX 4090 (24GB): Yes, fully compatible with 17.6GB headroom
What PyTorch version does Reference-Based Video Colorization need?
Reference-Based Video Colorization requires the following PyTorch-related packages: torch>=2.0.0, torchvision>=0.15.0. Ensure your ComfyUI environment has these installed. Ensure your PyTorch installation matches your CUDA version (use torch.version.cuda to check).
What Python packages are required for Reference-Based Video Colorization?
To run Reference-Based Video Colorization, you need to install: einops>=0.7.0, gdown# For downloading model checkpoints from Google Drive, numpy>=1.24.0, opencv-python>=4.8.0, pillow>=9.5.0, progressbar2>=4.2.0, scikit-image>=0.21.0, torch>=2.0.0, torchvision>=0.15.0, tqdm>=4.65.0. You can install these using pip or add them to your requirements.txt file.
How do I install Reference-Based Video Colorization in ComfyUI?
To install Reference-Based Video Colorization: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/jonstreeter/ComfyUI-Reference-Based-Video-Colorization, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.