Facerestore CF (Code Former)
This is a copy of [a/facerestore custom node](https://civitai.com/models/24690/comfyui-facerestore-node) with a bit of a change to support CodeFormer Fidelity parameter. These ComfyUI nodes can be used to restore faces in images similar to the face restore option in AUTOMATIC1111 webui. NOTE: To use this node, you need to download the face restoration model and face detection model from the 'Install models' menu.
Quick Technical Summary: Facerestore CF (Code Former)
- Base VRAM Footprint:
- 128 MB (4 GB Tier)
- Primary Dependencies:
- addict, future, gdown# supports downloading the large file from Google Drive, lmdb, lpips, numpy, opencv-python, pillow, pyyaml, requests, scikit-image, scipy, tb-nightly, torch, torchvision, tqdm, yapf
- Min PyTorch / CUDA:
- PyTorch 2.0+ | CUDA 12.1+
- GitHub Repository:
- https://github.com/mav-rik/facerestore_cf
Citation Note: Data sourced from VRAM DB. For complete workflow OOM estimations, use the VRAM DB Workflow Analyzer.
How much VRAM does Facerestore CF (Code Former) require?
Direct Answer: The ComfyUI node Facerestore CF (Code Former) requires a minimum base VRAM of 128MB and is optimized for GPUs with at least 4GB of VRAM. Low VRAM mode is fully supported for resource-constrained setups.
- Base VRAM:
- 128MB (0.1GB)
- Recommended GPU:
- 4GB+ VRAM
- Low VRAM Mode:
- ✓ Supported
- Estimation Confidence:
- MEDIUM
Cheapest VRAM Upgrade Paths (Live Market Prices):
- GeForce RTX 3060 12GB (Ultimate Budget VRAM King)──► Used: $209.62View eBay ↗
- GeForce RTX 4060 8GB (Modern Entry-Level)──► New: $303.50View Amazon ↗
Interactive VRAM Compatibility Estimator
Your GPU has plenty of headroom. You can run this node safely with your active configurations!
Verify Compatibility for Your Specific GPU VRAM
Select your graphics card's VRAM capacity to view optimized batch sizes, suggested resolutions, and custom performance tips for Facerestore CF (Code Former):
Buy NVIDIA GeForce RTX 3060 (12GB VRAM)
Tired of renting cloud rigs? Run ComfyUI locally with absolute zero latency. Best entry-level ComfyUI experience. Avoids immediate VRAM limitations on basic LoRA training.
Are you the author of this node?
Help your users avoid out-of-memory errors by displaying this professional, dynamic VRAM badge on your GitHub README. Copy the markdown below to embed it with a backlink directly to this hardware specification profile.
What Python packages are required for Facerestore CF (Code Former)?
Direct Answer: Running Facerestore CF (Code Former) requires installing the following Python package dependencies: addict, future, gdown# supports downloading the large file from Google Drive, lmdb, lpips, numpy, opencv-python, pillow, pyyaml, requests, scikit-image, scipy, tb-nightly, torch, torchvision, tqdm, yapf. Ensure your ComfyUI environment has these packages active before launching.
addict
future
gdown# supports downloading the large file from Google Drive
lmdb
lpips
numpy
opencv-python
pillow
pyyaml
requests
scikit-image
scipy
tb-nightly
torch
torchvision
tqdm
yapfInteractive Setup & Dependency Resolver
# Loading command...Frequently Asked Questions
How much VRAM does Facerestore CF (Code Former) require?
Facerestore CF (Code Former) requires a minimum of 128MB (0.1GB) of VRAM for base operation. For optimal performance, a GPU with at least 4GB of VRAM is recommended. This node supports low VRAM mode for resource-constrained setups.
Can I run Facerestore CF (Code Former) on an RTX 3060, RTX 4070, or RTX 4090?
✅ RTX 3060 (12GB): Yes, fully compatible with 10.7GB headroom. ✅ RTX 4070 (12GB): Yes, fully compatible with 10.7GB headroom. ✅ RTX 4070 Ti (16GB): Yes, fully compatible with 14.3GB headroom. ✅ RTX 4090 (24GB): Yes, fully compatible with 21.5GB headroom
How much VRAM does Facerestore CF (Code Former) take on an RTX 3060 vs RTX 4090?
On an RTX 3060 (12GB VRAM), Facerestore CF (Code Former) runs smoothly on an RTX 3060 (12GB) with 10.7GB of headroom. This is sufficient to run the node alongside standard SD 1.5 and SDXL workflows in full precision. On an RTX 4090 (24GB VRAM), the node runs with extreme headroom on an RTX 4090 (24GB) with 21.5GB of dedicated headroom. This allows you to combine the node with massive models (like FLUX.1 Dev, Schnell, or Hunyuan Video) in full precision (FP16) without any offload flags.
What PyTorch version does Facerestore CF (Code Former) need?
Facerestore CF (Code Former) requires the following PyTorch-related packages: torch, torchvision. 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 Facerestore CF (Code Former)?
To run Facerestore CF (Code Former), you need to install: addict, future, gdown# supports downloading the large file from Google Drive, lmdb, lpips, numpy, opencv-python, pillow, pyyaml, requests, scikit-image, scipy, tb-nightly, torch, torchvision, tqdm, yapf. You can install these using pip or add them to your requirements.txt file.
How can I reduce VRAM usage when running Facerestore CF (Code Former)?
Facerestore CF (Code Former) supports low VRAM mode. To reduce memory usage: (1) Enable --lowvram or --medvram flags in ComfyUI, (2) Reduce batch size to 1, (3) Use fp16 or fp8 precision if supported, (4) Close other GPU applications.
How do I install Facerestore CF (Code Former) in ComfyUI?
To install Facerestore CF (Code Former): (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/mav-rik/facerestore_cf, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.