NanoBanana FaceSwap
Face/head replacement for ComfyUI via Google Nano Banana 2 (Gemini image editing). Five swap pathways (whole, crop, mask-inpaint, unbiased, painted-edit), mediapipe + YuNet + Gemini bbox detector cascade, LAB / grain / sharpness / Laplacian compositing, identity-ref obfuscation for celebrity-classifier evasion.
Quick Technical Summary: NanoBanana FaceSwap
- Base VRAM Footprint:
- 128 MB (4 GB Tier)
- Primary Dependencies:
- google-genai>=0.8.0, mediapipe>=0.10.13,<0.11, numpy, opencv-python>=4.6.0, pillow
- Min PyTorch / CUDA:
- PyTorch 2.0+ | CUDA 12.1+
- GitHub Repository:
- https://github.com/IxMxAMAR/ComfyUI-NanoBanana-FaceSwap
Citation Note: Data sourced from VRAM DB. For complete workflow OOM estimations, use the VRAM DB Workflow Analyzer.
How much VRAM does NanoBanana FaceSwap require?
Direct Answer: The ComfyUI node NanoBanana FaceSwap 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
Interactive VRAM Compatibility Estimator
Your GPU has plenty of headroom. You can run this node safely with your active configurations!
Struggling with VRAM Crashes or Out-of-Memory Errors?
Running heavy models like FLUX or SDXL with NanoBanana FaceSwap? If you experience torch.cuda.OutOfMemoryError or driver timeouts, read our comprehensive troubleshooting guide to configure launch flags, enable tiled decoding, and avoid crashes.
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 NanoBanana FaceSwap:
What Python packages are required for NanoBanana FaceSwap?
Direct Answer: Running NanoBanana FaceSwap requires installing the following Python package dependencies: google-genai>=0.8.0, mediapipe>=0.10.13,<0.11, numpy, opencv-python>=4.6.0, pillow. Ensure your ComfyUI environment has these packages active before launching.
google-genai>=0.8.0
mediapipe>=0.10.13,<0.11
numpy
opencv-python>=4.6.0
pillowInteractive Setup & Dependency Resolver
# Loading command...Universal Architecture Compatibility
NanoBanana FaceSwap operates as a general-purpose processor without binding directly to a single checkpoint architecture. It can be paired across Stable Diffusion 1.5, SDXL, FLUX, and video diffusion pipelines.
Frequently Asked Questions
How much VRAM does NanoBanana FaceSwap require?
NanoBanana FaceSwap 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 NanoBanana FaceSwap 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 NanoBanana FaceSwap take on an RTX 3060 vs RTX 4090?
On an RTX 3060 (12GB VRAM), NanoBanana FaceSwap 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 Python packages are required for NanoBanana FaceSwap?
To run NanoBanana FaceSwap, you need to install: google-genai>=0.8.0, mediapipe>=0.10.13,<0.11, numpy, opencv-python>=4.6.0, pillow. You can install these using pip or add them to your requirements.txt file.
How can I reduce VRAM usage when running NanoBanana FaceSwap?
NanoBanana FaceSwap 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 NanoBanana FaceSwap in ComfyUI?
To install NanoBanana FaceSwap: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/IxMxAMAR/ComfyUI-NanoBanana-FaceSwap, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.
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