EricRollei-Eric_Image_Processing_Nodes/Docs/DNCNN_NAFNET_GUIDE.md

DnCNN and NAFNet Pre-trained Denoising Nodes - Quick Start Guide

DnCNN and NAFNet Pre-trained Denoising Nodes

✅ Installation Complete!

You now have two state-of-the-art pre-trained denoising nodes that work immediately without any training!

---

📦 What Was Installed

Files Created:

• models/dncnn_architecture.py - DnCNN model and processor
• models/nafnet_architecture.py - NAFNet model and processor
• nodes/dncnn_node.py - DnCNN ComfyUI node
• nodes/nafnet_node.py - NAFNet ComfyUI node
• Both __init__.py files updated for registration

---

🚀 Quick Start

Step 1: Restart ComfyUI

Completely close and restart ComfyUI to load the new nodes.

Step 2: Find the Nodes

Look under: Eric's Image Processing → Pre-trained Denoisers

• DnCNN Denoise (Pre-trained)
• NAFNet Denoise (Pre-trained)

Step 3: Use Them!

Just connect an image and run - they work immediately!

First use: Weights will download automatically (~2-10MB per model). This happens once, then they're cached.

---

🎯 DnCNN Denoise Node

Overview

Classic deep learning denoiser from 2017 that still performs excellently.

Available Models:

1. dncnn_25 (Default, Recommended)

   • Noise level: σ=25
   • Best for moderate noise
   • Grayscale processing

2. dncnn_15

   • Noise level: σ=15
   • Best for light noise
   • Grayscale processing

3. dncnn_50

   • Noise level: σ=50
   • Best for heavy noise
   • Grayscale processing

4. dncnn3

   • Blind denoising (any noise level)
   • Grayscale processing
   • More flexible but slightly less accurate

5. dncnn_color_blind

   • Blind denoising for RGB images
   • Color processing
   • Most versatile

When to Use DnCNN:

• ✅ Fast processing needed
• ✅ Film grain or moderate noise
• ✅ You know the approximate noise level
• ✅ Smaller file sizes preferred

Model Size: ~2-3 MB per model

Speed: ⚡⚡⚡ Very Fast

---

🚀 NAFNet Denoise Node

Overview

State-of-the-art 2022 architecture with no activation functions - more efficient!

Available Models:

1. nafnet-width32 (Default, Recommended)

   • Lighter, faster model
   • ~2M parameters
   • Best for speed

2. nafnet-width64

   • Balanced quality/speed
   • ~8M parameters
   • Best all-around

3. nafnet-sidd

   • Trained on real-world noise (SIDD dataset)
   • Best for camera noise, ISO noise
   • Handles complex real-world scenarios

When to Use NAFNet:

• ✅ Best quality needed
• ✅ Real-world camera noise
• ✅ Complex noise patterns
• ✅ Latest technology preferred

Model Size: ~8-30 MB per model

Speed: ⚡⚡ Fast

---

📊 Comparison: DnCNN vs NAFNet

Feature	DnCNN	NAFNet
Year	2017	2022
Speed	Faster ⚡⚡⚡	Fast ⚡⚡
Quality	Excellent ⭐⭐⭐⭐	State-of-Art ⭐⭐⭐⭐⭐
Size	Smaller (2-3MB)	Larger (8-30MB)
Best For	Known noise levels	Real-world noise
Film Grain	Very Good	Excellent
Color	Limited	Full RGB

---

💡 Usage Tips

For Film Grain:

1. Try DnCNN-25 first (fast, good results)
2. If not enough → Try NAFNet-width32
3. For best quality → Use NAFNet-sidd

For Camera Noise:

1. Start with NAFNet-sidd (trained for real-world)
2. Alternative: NAFNet-width64 (more general)
3. Fast option: DnCNN-color-blind

For Synthetic Noise:

1. Know noise level? → Use matching DnCNN model
2. Unknown noise? → Use NAFNet-width32
3. Very noisy? → Try DnCNN-50

Batch Processing:

Both nodes support batch processing automatically:

• Connect multiple images
• All processed with same settings
• Faster than processing one-by-one

---

🔧 Technical Details

DnCNN Architecture:

• 17-layer CNN with residual learning
• Predicts noise, not clean image
• Batch normalization for stability
• ~670K parameters

NAFNet Architecture:

• No activation functions (unique!)
• Simple gating mechanisms instead
• U-Net style encoder-decoder
• Layer normalization
• 2M-8M parameters

Automatic Weight Management:

• Weights download automatically on first use
• Cached in: models/pretrained_weights/
• Shared across all uses
• No manual downloading needed

---

🎨 Example Workflows

Simple Denoising:

Load Image → DnCNN/NAFNet → Save Image

Compare Models:

Load Image → DnCNN-25 → Preview
          ↘ NAFNet-32 → Preview

Denoise + Enhance:

Load Image → NAFNet → BM3D → Sharpen → Save

---

🐛 Troubleshooting

"Weights not downloading"

• Check internet connection
• Check firewall settings
• Weights come from GitHub - ensure access
• Manual download possible from GitHub releases

"Out of memory"

• Use DnCNN (smaller memory footprint)
• Use NAFNet-width32 instead of width64
• Process images at lower resolution
• Enable CPU mode if GPU insufficient

"Slow processing"

• Normal on first use (downloading weights)
• Subsequent uses are much faster
• Consider DnCNN for speed priority
• GPU highly recommended

---

📚 References

DnCNN:

Paper: "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising"
Authors: Zhang et al.
Published: IEEE TIP 2017
Weights Source: https://github.com/cszn/KAIR

NAFNet:

Paper: "Simple Baselines for Image Restoration"
Authors: Chen et al.
Published: ECCV 2022
Weights Source: https://github.com/megvii-research/NAFNet

---

✨ Summary

You now have professional-grade denoising that:

• ✅ Works immediately - no training required
• ✅ Automatically downloads weights
• ✅ Handles multiple noise types
• ✅ Processes batches efficiently
• ✅ Offers speed/quality options

Quick Recommendation: Start with DnCNN-25 for most images. If you need better quality or have real camera noise, upgrade to NAFNet-width32 or NAFNet-sidd.

---

Enjoy your new denoising superpowers! 🎉