⚡ ComfyUI Flash Attention V100

A ComfyUI custom node enabling Flash Attention 1 on legacy NVIDIA GPUs (Tesla V100, T4) that lack Compute Capability 8.0+ required by FlashAttention-2.

🔋 Reduces memory usage by ~30-40% and improves generation speed on compatible GPUs without upgrading hardware.

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📋 Overview

Standard FlashAttention-2 requires sm_80 (Ampere/Ada Lovelace) or newer. This node patches ComfyUI's attention mechanism to use ai-bond/flash-attention-v100, maintaining compatibility with:

• Tesla V100 (sm_70)
• Tesla T4 (sm_75)
• Other Compute Capability 7.x GPUs

Features

• 🔍 Auto-detection: Only activates on compatible GPUs (< sm_80)
• 🎛️ Manual Control: Toggle on/off via node interface
• 🛡️ Safe Fallback: Automatically reverts to standard attention if kernel errors occur
• 📊 Status Monitoring: Real-time GPU architecture detection

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⚠️ Prerequisites

Important: This requires compiling FlashAttention from source. Ensure you have:

• Linux environment (Windows WSL2 supported, native Windows untested)
• CUDA Toolkit 11.6+ or 12.x (must match your PyTorch CUDA version)
• 15GB+ free RAM for compilation
• 20-30 minutes for building

Check Compatibility

python -c "import torch; print(f'Compute Capability: sm_{torch.cuda.get_device_capability()[0]}{torch.cuda.get_device_capability()[1]}')"

🚀 Installation

Step 1: Install the ComfyUI Node

cd ComfyUI/custom_nodes
git clone https://github.com/FearL0rd/ComfyUI-Flash-Attention_v100.git
cd ComfyUI-Flash-Attention_v100

Step 2: Install Flash Attention (V100 Fork) This is the heavy lifting step - compiling the CUDA kernels:

# Install build dependencies
pip install packaging ninja

# Clone and install the V100-compatible fork
git clone https://github.com/ai-bond/flash-attention-v100.git /tmp/flash-attn-v100
cd /tmp/flash-attn-v100

# Build and install (this takes 20-30 minutes)
python setup.py install

# Alternative if you have limited RAM (uses 2 parallel jobs instead of 4)
# MAX_JOBS=2 python setup.py install

Step 3: Verify Installation Restart ComfyUI. You should see in the console:

🔍 [FlashAttnV100] Checking GPU compatibility...

🎮 Usage

Method 1: Node-Based Control (Recommended)

Right-click → Add Node → attention → "⚡ Flash Attn V100 Controller"

<img width="919" height="291" alt="image" src="https://github.com/user-attachments/assets/4ad73007-8d5e-427f-88e0-e2a3bea614b1" />

Connect your MODEL output → Controller → Rest of workflow

Toggle enable_v100_opt to True/False as needed

Use "ℹ️ Flash Attn V100 Status" node to verify active state

Workflow Example:

[Load Checkpoint] → [FlashAttnV100Controller] → [KSampler] → [Save Image]
                       ↓
                 Status String (shows: "ACTIVE sm_70")

🧪 Technical Details

This node monkey-patches comfy.ldm.modules.attention.optimized_attention with a wrapper that:

Reshapes tensors from ComfyUI format (batch*heads, seq, dim) → Flash format (batch, heads, seq, dim) Calls flash_attn_func with causal=False (diffusion models aren't autoregressive) Reshapes back or falls back to sdpa/vanilla attention on CUDA OOM The patch is non-destructive - calling restore() returns ComfyUI to original behavior.

🤝 Credits

Dao-AILab/flash-attention - Original FlashAttention implementation ai-bond/flash-attention-v100 - V100/T4 compatibility maintenance ComfyUI - The node-based UI framework

📄 License

MIT License

Disclaimer: This modifies core attention mechanisms at runtime. While tested on V100/T4, use at your own risk with critical workflows.