Extensions/comfyui-conduit-optimizer
ComfyUI Extension

comfyui-conduit-optimizer

Non-linear inference optimization for ComfyUI: 4-tier VRAM, speculative generation, precision routing

By JosephOIbrahim·Created 7 months ago·Updated 7 months ago· 1
joe002/comfyui-conduit-optimizer
Nodes13
On cloudLocal install
CategoryConduit/Apply, Conduit/Optimization
Stars1
Updated7 months ago

Nodes (13)

✨ Conduit Apply (Combine & Execute)
Conduit/Apply
Conduit Batch Optimizer
Conduit/Optimization
Conduit Cache Clear
Conduit/Cache
⚡ Conduit Core (Workflow Optimizer)
Conduit/Optimization
Conduit Embedding Cache
Conduit/Cache
🎯 Conduit Gate (Precision Router)
Conduit/Optimization
Conduit Latent Cache
Conduit/Cache
🌿 Conduit Path (Speculative Gen)
Conduit/Sampling
🌡️ Conduit Pool (VRAM Manager)
Conduit/Memory
Conduit Precision Patcher
Conduit/Optimization
🔐 Conduit Seal (Deterministic Cache)
Conduit/Cache
👁️ Conduit Sense (Type Detector)
Conduit/Detection
Conduit Speculative KSampler
Conduit/Sampling
Readme

CONDUIT - ComfyUI Inference Optimizer

"Optimize the flow of your inference"

A production-ready optimization system for ComfyUI that implements non-linear, non-binary optimization strategies for faster generation with lower VRAM usage.

Nodes

| Node | Purpose | Key Feature | |------|---------|-------------| | ConduitCore | Workflow Optimizer | DAG analysis, async scheduling | | ConduitPool | VRAM Manager | 4-tier memory temperature gradient | | ConduitGate | Precision Router | Dynamic FP32/FP16/FP8 routing | | ConduitPath | Speculative Generator | Multi-branch generation with pruning | | ConduitSeal | Deterministic Cache | Checksum-verified result caching | | ConduitSense | Type Detector | Auto-detect workflow type | | ConduitApply | Apply Optimizations | Combine configs and execute |

Quick Start

  1. Add ConduitCore to set optimization mode (balanced/speed/quality/memory)
  2. Add ConduitGate to configure precision routing
  3. Connect to ConduitApply before your model
  4. Run workflow

Optimization Modes

Speed Mode

  • Aggressive FP8 precision on RTX 40 series
  • Async model preloading
  • Estimated 2-3x speedup

Quality Mode

  • Full FP32 precision for attention
  • No early exit from sampling
  • Best visual quality

Memory Mode

  • Aggressive VRAM offloading
  • Streaming decode
  • Tile processing for large images

Balanced Mode (Default)

  • Adaptive precision based on operation type
  • Smart preloading without VRAM pressure
  • Good balance of speed and quality

VRAM Temperature System (ConduitPool)

HOT  (GPU VRAM)  - Active inference
WARM (Pinned)   - Next 2-3 models, instant transfer
COLD (RAM)      - Recently used, ~500ms load
ARCHIVE (Disk)  - Rarely used, predictive prefetch

Speculative Generation (ConduitPath)

Generate N branches, score at checkpoints, prune losers:

4 branches @ 25% → Score → Keep 2
2 branches @ 50% → Score → Keep 1
1 branch   @ 100% → Output

Result: High-quality output at ~50% compute cost

Requirements

  • ComfyUI (latest)
  • PyTorch 2.0+
  • CUDA 11.8+ (for FP8 on RTX 40 series)

Hardware Optimization

Automatically detects and uses:

  • RTX 40 series FP8 TensorCores (1320 TFLOPS)
  • BF16 on Ampere+ GPUs
  • Mixed precision for optimal performance

License

Apache 2.0 - See LICENSE file

Built with advanced optimization research