8GB VRAM? No Problem! Nunchaku’s Flux Breakthrough Makes ComfyUI Blazing Fast

Nunchaku isn’t just another Flux model accelerator—it’s a paradigm shift. While testing on a modest Tesla T4 (16GB VRAM), I watched it generate 1024×1024 images in 26 seconds while using under 8GB VRAM—peaking at just 33% utilization. This isn’t brute-force speed; it’s surgical efficiency.

The implications are staggering:

• Mid-range GPUs (like a 12GB RTX 3060) will outperform these results
• High-end cards (RTX 4090) could achieve single-digit render times
• Quality remains uncompromised—fine details and textures stay razor-sharp

This is just the baseline. As we’ll explore, Nunchaku’s real magic lies in its optimized workflow and ControlNet compatibility—tools that transform rapid generation into intelligent rapid generation.

Video Tutorial:

The Nunchaku Workflow Demystified

Nunchaku supercharges ComfyUI with two specialized nodes:

1. Flux DiT Loader – The engine driving the speed revolution
2. Text Encoder Loader – Optimized for precision (though Dual Clip Loader works in a pinch)

Key Components

• Specialty Models Required:
  • Base model (mandatory)
  • Optional add-ons:
    • Canny for edge control
    • Fill for inpainting/outpainting

Critical Settings

Setting	Function	Recommendation
Cache Threshold (0.12 default)	Speed ↔ Quality trade-off	0 = max quality; higher = faster but coarser
Attention Method	Processing mode	Only “nunchaku-fp16” available currently
CPU Offload (Auto)	VRAM saver	<14GB GPUs: Keep auto; manually enable if crashing

Pro Configuration Tips

• Text Encoder Options:
  • High VRAM (16GB+): Use fp16 T5 encoder
  • Low VRAM (8GB): Switch to GGUF T5 version
• Memory Management:
  • Attach an “Unload Model” node to automatically clear VRAM after text encoding

“Think of the Unload Model node as your memory janitor—it quietly cleans up behind the scenes so you don’t hit VRAM walls.”

This streamlined architecture explains how Nunchaku achieves its blistering speeds without turning your GPU into a space heater.

Benchmarking Nunchaku: A Comprehensive Performance Analysis

1. Nunchaku vs. PixelWave: The Quality Showdown

Test Methodology:

• Same hardware: Tesla T4 GPU (16GB VRAM)
• Resolution: 1024×1024
• Sampling steps: 28 (both workflows)

Performance Metrics:

Metric	Nunchaku	PixelWave	Advantage
Render Time	26 seconds	112 seconds	4.3x faster

Quality Comparison:

• Facial Features:
  • Nunchaku rendered natural-looking freckles with proper distribution
  • PixelWave created clustered freckle patterns with uneven density
• Eye Details:
  • Nunchaku produced clear iris patterns with depth
  • PixelWave showed smudging in the pupil region
• Clothing Texture:
  • Nunchaku’s fur trim showed individual hair strands
  • PixelWave’s version appeared more painterly
• Anatomical Accuracy:
  • Nunchaku correctly rendered all body parts
  • PixelWave failed to generate one foot and distorted toes

Nunchaku:

PixelWave:

Nunchaku Full Body Image:

PixelWave Full Body Image:

2. LoRA Integration: Turbo vs. Standard

Compared three configurations:

Configuration	Render Time	Quality Assessment
Nunchaku (Standard)	26s	Excellent detail preservation
Nunchaku + Turbo LoRA	17s	Noticeable quality degradation
Flux fp8 + Turbo LoRA	40s	Poor detail, artifacts

Quality Trade-offs:

1. Standard LoRA:
   • Maintained all texture details
   • Preserved prompt adherence
   • Recommended for final outputs
2. Turbo LoRA:
   • speed boost
   • Lost fabric weave patterns
   • Simplified facial features
   • Only suitable for quick previews

Nunchaku + Turbo LoRA:

Flux fp8 + Turbo LoRA:

3. Nunchaku vs. WaveSpeed: Architecture Comparison

Workflow Differences:

Component	Nunchaku	WaveSpeed
Base Model	Specialized SVD Quant	PixelWave
Control Method	Dedicated nodes	Plugin-based

Performance Testing:

• Both set to equivalent quality settings

Results:

Metric	Nunchaku	WaveSpeed	Difference
Render Time	28s	84s	3x faster

Nunchaku’s ControlNet Mastery: Precision at Speed

1. Canny Edge Implementation: Dedicated vs. Union Workflows

Benchmark Setup

• Test Image: Female figure with complex drapery
• Hardware: Tesla T4 GPU (16GB VRAM)
• Baseline: Traditional Flux Canny (GGUF Q5 model)

Performance Comparison

Method	Render Time	Fold Detail
Flux Canny	196s	Perfect drape continuity
Nunchaku Canny (0.12 threshold)	25s	Minor fold blending
Nunchaku Canny (0.3 threshold)	9s	Lost mid-frequency details

Nunchaku Canny (0.3 threshold):

Nunchaku Canny (0.12 threshold):

Flux Canny:

2. Union ControlNet: The Flexible Alternative

Architecture Deep Dive

• Single Model handles multiple ControlNet types (Canny/Depth/Normal)

Performance Data

Task	Dedicated Model	Union ControlNet	Delta
Canny Generation	25s	38s	+52%
Multi-ControlNet Switching	N/A	Instant	–

When to Use Union:

1. Rapid prototyping with multiple control types
2. VRAM-rich environments (≥12GB)
3. Non-critical anatomical precision

3. Real-World Workflow Recommendations

For Concept Artists:

1. Use Union ControlNet for:
   • Rapid layout iterations
   • Multi-control experiments
2. Switch to dedicated models for:
   • Final presentation shots
   • Commercial work

Hardware Guidelines:

GPU Class	Recommended Setup
≤8GB VRAM	Dedicated models + CPU offload
>8GB	Union ControlNet

Conclusion

Nunchaku isn’t just another tool—it’s a quantum leap for ComfyUI workflows. By combining blazing speed (5x faster than alternatives), uncompromising detail, and efficient hardware usage, it redefines what’s possible in AI-assisted content creation.

Support me On Patreon to get all the workflows and detailed installation guide:

https://www.patreon.com/posts/exclusive-126851101