ComfyUI-RAVE Attention
ComfyUI nodes to use RAVE attention as a temporal attention mechanism. This differs from other implementations in that it does not concatenate the images together, but within the UNet's Self-Attention mechanism performs the RAVE technique. By not altering the images/latents throughout the UNet, this method does not affect other temporal techniques, style mechanisms, or other UNet modifications. For example, it can be combined with AnimateDiff, ModelScope/ZeroScope, or FLATTEN.
Quick Technical Summary: ComfyUI-RAVE Attention
- Base VRAM Footprint:
- 128 MB (4 GB Tier)
- Primary Dependencies:
- None (Pure Python/Torch)
- Min PyTorch / CUDA:
- PyTorch 2.0+ | CUDA 12.1+
- GitHub Repository:
- https://github.com/logtd/ComfyUI-RAVE_ATTN
Citation Note: Data sourced from VRAM DB. For complete workflow OOM estimations, use the VRAM DB Workflow Analyzer.
How much VRAM does ComfyUI-RAVE Attention require?
Direct Answer: The ComfyUI node ComfyUI-RAVE Attention 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!
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Select your graphics card's VRAM capacity to view optimized batch sizes, suggested resolutions, and custom performance tips for ComfyUI-RAVE Attention:
Buy NVIDIA GeForce RTX 3060 (12GB VRAM)
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Frequently Asked Questions
How much VRAM does ComfyUI-RAVE Attention require?
ComfyUI-RAVE Attention 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 ComfyUI-RAVE Attention 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 ComfyUI-RAVE Attention take on an RTX 3060 vs RTX 4090?
On an RTX 3060 (12GB VRAM), ComfyUI-RAVE Attention 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.
How can I reduce VRAM usage when running ComfyUI-RAVE Attention?
ComfyUI-RAVE Attention 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 ComfyUI-RAVE Attention in ComfyUI?
To install ComfyUI-RAVE Attention: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/logtd/ComfyUI-RAVE_ATTN, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.