Extensions/iSeeBetter Node for ComfyUI
ComfyUI Extension

iSeeBetter Node for ComfyUI

Custom Node to implement iSeeBetter upscaling method.

By llikethat·Created 8 months ago·Updated 8 months ago· 1
llikethat/ComfyUI-iseebetter
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ComfyUI-iSeeBetter

ComfyUI custom nodes for iSeeBetter video super-resolution with BasicVSR/BasicVSR++ learned flow, SRGAN enhancement, and water-specific optimization.

Based on iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks.

Features

Core Video Super-Resolution

  • RBPN Generator - Recurrent Back-Projection Network for temporal video SR
  • Optical Flow Integration - Temporal alignment using DIS, Farneback, or simple flow
  • Multi-frame Processing - Leverages neighboring frames for better quality
  • Tiled Processing - Handle large images without running out of memory

BasicVSR / BasicVSR++ (NEW!)

State-of-the-art video super-resolution with learned optical flow:

  • SpyNet - Lightweight learned optical flow network (no external flow needed)
  • Bidirectional Propagation - Uses both past and future frames
  • Second-order Grid Propagation (BasicVSR++) - Enhanced temporal modeling
  • Artifact-free - Avoids "golf ball" artifacts from external flow estimation

SRGAN Enhancement

  • Discriminator - SRGAN discriminator for GAN-based quality assessment
  • Perceptual Loss - VGG-based perceptual similarity scoring
  • Generator Loss - Combined MSE + Perceptual + Adversarial + TV loss

Water-Specific Enhancement

Optimized for water bodies: sea, ocean, waterfall, splash, swell, wake

  • Water Detail Enhancer - Neural network trained for water textures
  • Frequency Attention - Enhances wave patterns, ripples, reflections
  • Water Post-Processing - Highlight enhancement, local contrast, blue boost

Nodes

Video Upscaling Nodes

| Node | Description | |------|-------------| | iSeeBetter Model Loader | Load RBPN/iSeeBetter model weights | | iSeeBetter Video Upscale | Full temporal upscaling with optical flow (supports SpyNet) | | iSeeBetter Clean Upscale | Artifact-free upscaling (recommended if seeing artifacts) | | iSeeBetter Simple Upscale | Simplified upscaling (no explicit optical flow) | | iSeeBetter Frame Buffer | Buffer frames for temporal processing |

BasicVSR Nodes (NEW!)

| Node | Description | |------|-------------| | BasicVSR Model Loader | Load/create BasicVSR or BasicVSR++ model | | BasicVSR Video Upscale | Upscale using learned optical flow (best quality) |

Water Enhancement Nodes

| Node | Description | |------|-------------| | Water Detail Enhance | Neural network enhancement for water textures | | Water Post Process | CPU-based post-processing (highlights, contrast, color) | | Water Enhancer Model Loader | Load custom water enhancer weights |

Quality & Utility Nodes

| Node | Description | |------|-------------| | SRGAN Discriminator Loader | Load discriminator for quality assessment | | Perceptual Quality Score | Calculate VGG-based perceptual similarity | | Image Sharpener | Unsharp masking for detail enhancement | | iSeeBetter Debug Test | Test single frame to isolate issues |

Troubleshooting

"Golf ball" / Pixelated Artifacts

If you see golf-balling or bad pixel artifacts:

Quick Fix Options

  1. Use the Clean Upscale Node: The new "iSeeBetter Clean Upscale (No Artifacts)" node is designed to avoid artifacts:

    • Uses optimized flow estimation that won't cause artifacts
    • Doesn't use tiling (processes full frames)
    • Has use_temporal_info toggle - try setting to False if artifacts persist
    • Has blend_factor to mix with bicubic for smoother results

  2. Adjust Flow Settings in standard iSeeBetter Video Upscale:

    • Set optical_flow_method to zero - this disables motion compensation entirely
    • Set smooth_flow to True (default)
    • Set flow_scale to 0.5 or lower - reduces motion compensation strength

  3. Disable Tiling: Set tile_size=0 to process full frames (requires more VRAM)

Diagnostic Steps

  1. Test with Debug Node: Use the "iSeeBetter Debug Test" node to isolate:

    • Set use_zero_flow=True and force_no_tiling=True
    • If artifacts persist, the issue is with the model weights
    • If artifacts disappear, the issue is with flow/tiling

  2. Check console output: Look for these stats:

    [iSeeBetter] Output stats (before clamp on future frames):
  - min=0.0000, max=1.0000  <- Good
  - mean=0.4500, std=0.2000 <- Normal range
    • Very low std (<0.01) = model may be producing flat output
    • Very high std (>0.5) = may indicate artifacts

  3. Check flow statistics:

    [iSeeBetter] Flow stats (after scale): min=-X, max=X, mean=~0
    • If values are very large (>50), try reducing flow_scale
    • If you see NaN or Inf, there's a computation error

  4. Verify model compatibility:

    • Model should output values in [0, 1] range after processing
    • Check that nFrames matches (usually 7 for iSeeBetter)
    • Scale factor should match (2x, 4x, or 8x)

Installation

  1. Clone or download this repository to your ComfyUI/custom_nodes/ directory:

    cd ComfyUI/custom_nodes
git clone https://github.com/yourusername/ComfyUI-iSeeBetter.git

  2. Install dependencies:

    pip install -r requirements.txt

  3. Download model weights and place them in ComfyUI/models/iseebetter/:

  4. Restart ComfyUI

Usage

Basic Video Upscaling (iSeeBetter)

Load Video → iSeeBetter Model Loader → iSeeBetter Video Upscale → Save Video

BasicVSR Pipeline (Best Quality, No Artifacts)

Load Video → BasicVSR Model Loader → BasicVSR Video Upscale → Save Video

Hybrid: iSeeBetter with SpyNet Flow

Use iSeeBetter model but with learned optical flow:

Load Video → iSeeBetter Model Loader → iSeeBetter Video Upscale (optical_flow_method=spynet) → Save Video

Water Enhancement Pipeline

For optimal water body upscaling:

Load Video 
    ↓
BasicVSR Model Loader → BasicVSR Video Upscale
    ↓
Water Detail Enhance (strength: 0.5)
    ↓
Water Post Process (highlight: 0.2, contrast: 0.3, blue_boost: 1.1)
    ↓
Image Sharpener (amount: 0.3)
    ↓
Save Video

Quality Assessment

Compare your upscaled results:

Original HR → Perceptual Quality Score ← Upscaled SR
                      ↓
              Quality Report (loss value + assessment)

Model Files

Place models in ComfyUI/models/iseebetter/:

| File Pattern | Type | |--------------|------| | *.pth, *rbpn* | RBPN generator weights | | *discriminator*, *netD* | SRGAN discriminator | | *water* | Water enhancer weights |

Parameters Guide

BasicVSR Model Loader (Recommended)

  • variant: basicvsr (faster) or basicvsr_plusplus (better quality)
  • scale_factor: 2x or 4x upscaling
  • num_features: Feature channels (64 default, higher = better quality but more VRAM)
  • num_blocks: Residual blocks (15 default)

BasicVSR Video Upscale

  • chunk_size: Process frames in chunks (10 default, reduce if OOM)

iSeeBetter Video Upscale

  • optical_flow_method:
    • spynet (recommended) - Learned optical flow, best quality
    • dis - Dense Inverse Search (OpenCV)
    • farneback - Classical Farneback (OpenCV)
    • simple - Gradient-based, fastest
    • zero - No motion compensation (use if seeing artifacts)
  • tile_size: 0 = auto (no tiling for high VRAM), or 256/384 for large images
  • tile_overlap: 32 recommended for seamless tiles
  • smooth_flow: Enable Gaussian smoothing on flow (reduces artifacts)
  • flow_scale: Scale factor for optical flow magnitude (lower = less motion compensation)

iSeeBetter Clean Upscale

  • use_temporal_info: Use neighbor frames for temporal consistency. Disable if seeing artifacts
  • blend_factor: Blend between model output (1.0) and bicubic fallback (0.0)

Water Detail Enhance

  • strength: 0.0-1.0, blend with original
  • use_frequency_attention: Enable for wave/ripple enhancement

Water Post Process

  • highlight_strength: 0.1-0.3 for subtle reflection enhancement
  • contrast_strength: 0.2-0.5 for texture detail
  • saturation: 1.0-1.3 for color vibrancy
  • blue_boost: 1.0-1.2 for ocean/water color

Technical Details

BasicVSR/BasicVSR++ Architecture

  • SpyNet: 6-level spatial pyramid for learned optical flow
  • Bidirectional Propagation: Forward and backward temporal features
  • Second-order Propagation (++): Two-stage propagation in each direction
  • Pixel Shuffle Upsampler: Sub-pixel convolution for upscaling

iSeeBetter Architecture

  • Generator: RBPN with DBPN-S modules
  • Discriminator: SRGAN-style with LeakyReLU
  • Loss Function: MSE + Perceptual (VGG) + Adversarial + TV

Water Enhancement

  • Frequency Attention: Separates low/mid/high frequency for targeted enhancement
  • Multi-scale Processing: 3x3, 5x5, 7x7 kernels for different pattern sizes
  • Residual Learning: Learns enhancement residuals, not full reconstruction

Requirements

torch>=1.9.0
torchvision>=0.10.0
numpy>=1.19.0
opencv-python>=4.5.0

Citation

If you use this in your research:

@article{iseebetter2020,
  title={iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networks},
  author={Chadha, Aman and Britto, John and Roja, M Mani},
  journal={Computational Visual Media},
  year={2020},
  publisher={Springer}
}

License

This project is for research purposes. See the original iSeeBetter repository for licensing details.

Acknowledgments