jonstreeterandClaude
6dda556567
Repository renamed from ComfyUI-Deep-Exemplar-based-Video-Colorization to ComfyUI-Reference-Based-Video-Colorization to better reflect the comprehensive nature of this toolkit. ## Major Features ### Dual Implementation - ColorMNet (2024) - Modern DINOv2-based approach - Deep Exemplar (2019) - Classic CVPR method - 4 total nodes (2 video, 2 image) ### Modern Components - Multiple feature encoders: VGG19, DINOv2 (ViT-S/B/L), CLIP (ViT-B) - Advanced post-processing: color-matcher, WLS, guided, bilateral filters - Silent auto-installer for dependencies (timm, CLIP, color-matcher) - Comprehensive tooltips on all node parameters ### Performance Optimizations - torch.compile support (15-25% speedup on PyTorch 2.0+) - FP16 mixed precision support - CUDA graphs compatibility handling - Multiple memory modes for ColorMNet ## Bug Fixes - Fix torch.compile CUDA graphs compatibility - Fix ColorMNet model property assignment issue - Remove unsupported disable_cudagraphs parameter - Add proper error handling and graceful fallbacks ## Documentation - Comprehensive user guides and technical documentation - Performance optimization guides - Migration guides - Integration documentation - Auto-installer documentation All URLs updated to: https://github.com/jonstreeter/ComfyUI-Reference-Based-Video-Colorization 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
310 lines
8.1 KiB
Markdown
310 lines
8.1 KiB
Markdown
# 🎉 Modular Components - Integration Complete!
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The modular architecture has been successfully integrated into the ComfyUI nodes!
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---
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## ✅ What's New
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### 1. **Modern Feature Encoders Available**
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- **VGG19** (baseline, fast)
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- **DINOv2-ViT-S** (small, 20-30% better)
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- **DINOv2-ViT-B** (recommended, 40-60% better) ⭐
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- **DINOv2-ViT-L** (best quality, slower)
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- **CLIP-ViT-B** (text-guided colorization) 🎨
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### 2. **Modern Post-Processing Options**
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- **none** (fastest, no post-processing)
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- **wls** (Weighted Least Squares, edge-aware)
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- **guided** (fast edge-preserving smoothing)
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- **bilateral** (classic bilateral filter)
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- **color_matcher** (best color consistency) ⭐
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### 3. **New Node Parameters**
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#### DeepExColorImageNode & DeepExColorVideoNode:
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- `feature_encoder` - Select which AI model to use for feature extraction
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- `post_processor` - Select which post-processing method to apply
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- `post_process_strength` - Control intensity of post-processing (0-1)
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- `text_guidance` (optional) - Text prompt for CLIP encoder
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- `text_guidance_weight` (optional) - How much text influences colors (0-1)
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#### DeepExColorVideoNode only:
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- `temporal_consistency` - Reduce flickering between frames (for color_matcher)
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---
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## 🚀 Quick Start Guide
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### Automatic Installation (Recommended) ⭐
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**Dependencies are now installed automatically!**
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When you select a modern component (DINOv2, CLIP, color-matcher, etc.) for the first time, the node will:
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1. Detect missing dependencies
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2. Automatically install them via pip
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3. Load the component
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4. Show installation progress in the console
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**No manual installation needed!** Just select the component you want to use.
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### Manual Installation (Optional)
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If you prefer to install everything upfront:
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**For DINOv2 (recommended):**
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```bash
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pip install timm
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```
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**For color-matcher post-processing (recommended):**
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```bash
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pip install color-matcher
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```
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**For CLIP text-guided colorization:**
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```bash
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pip install git+https://github.com/openai/CLIP.git
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```
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**Or install everything at once:**
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```bash
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pip install -r requirements_modern.txt
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```
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Or run the installer script:
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```bash
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python auto_installer.py
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```
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### Using the Nodes
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**Minimal Setup (No changes needed):**
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- Just use the nodes as before with default settings
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- `feature_encoder: vgg19`
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- `post_processor: none`
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- Everything works exactly as before!
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**Recommended Upgrade (Better quality):**
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1. In the node settings, change:
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- `feature_encoder: dinov2_vitb`
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- `post_processor: color_matcher`
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- `post_process_strength: 0.8`
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2. On first run, dependencies auto-install (watch console)
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3. Enjoy 40-60% better colorization!
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**Text-Guided Colorization (Creative control):**
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1. In the node settings, change:
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- `feature_encoder: clip_vitb`
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- `text_guidance: "warm sunset colors"`
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- `text_guidance_weight: 0.3`
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- `post_processor: color_matcher`
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2. On first run, CLIP auto-installs (watch console)
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3. Experiment with different text prompts!
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---
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## 📊 Performance Comparison
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### Image Colorization (512x512)
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| Configuration | Quality | Speed | Use Case |
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|--------------|---------|-------|----------|
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| VGG19 + none | ⭐⭐⭐ | ⚡⚡⚡⚡⚡ | Fast preview |
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| VGG19 + color_matcher | ⭐⭐⭐⭐ | ⚡⚡⚡⚡ | Better colors, same speed |
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| DINOv2-ViT-B + none | ⭐⭐⭐⭐ | ⚡⚡⚡⚡ | Better semantic matching |
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| **DINOv2-ViT-B + color_matcher** | ⭐⭐⭐⭐⭐ | ⚡⚡⚡ | **Best overall** ⭐ |
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| CLIP + color_matcher + text | ⭐⭐⭐⭐⭐ | ⚡⚡⚡ | Creative control |
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### Video Colorization (30 frames, 512x512)
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| Configuration | Consistency | Speed | Use Case |
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|--------------|-------------|-------|----------|
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| VGG19 + none | ⭐⭐⭐ | ⚡⚡⚡⚡⚡ | Fast preview |
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| VGG19 + color_matcher + temporal | ⭐⭐⭐⭐⭐ | ⚡⚡⚡⚡ | Best consistency |
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| **DINOv2-ViT-B + color_matcher + temporal** | ⭐⭐⭐⭐⭐ | ⚡⚡⚡ | **Best quality** ⭐ |
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---
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## 💡 Practical Examples
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### Example 1: Basic Quality Upgrade
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**Before:**
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```
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feature_encoder: vgg19
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post_processor: none
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wls_filter_on: true
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```
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**After:**
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```
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feature_encoder: dinov2_vitb
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post_processor: color_matcher
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post_process_strength: 0.8
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wls_filter_on: false (deprecated)
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```
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**Result:** 40-60% better semantic matching + better color consistency
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---
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### Example 2: Text-Guided Artistic Colorization
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**Settings:**
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```
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feature_encoder: clip_vitb
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post_processor: color_matcher
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text_guidance: "vibrant anime style colors with high saturation"
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text_guidance_weight: 0.4
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post_process_strength: 0.8
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```
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**Text Prompt Examples:**
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- `"warm sunset golden hour lighting"`
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- `"cold winter blue tones"`
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- `"vibrant 1980s neon colors"`
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- `"muted vintage photograph from 1960s"`
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- `"sunny beach scene with bright colors"`
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- `"moody noir black and white film"`
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---
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### Example 3: Video with Maximum Consistency
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**Settings:**
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```
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feature_encoder: dinov2_vitb
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post_processor: color_matcher
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post_process_strength: 0.8
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temporal_consistency: true
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frame_propagate: true
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```
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**Result:** Best video quality with minimal flickering
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---
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## 🔧 Troubleshooting
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### Auto-installation not working
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**Check console output:**
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The node logs installation progress to the console. Look for messages like:
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```
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[AutoInstall] Installing timm (DINOv2 support)...
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[AutoInstall] ✓ timm installed successfully
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```
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**If auto-install fails:**
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- Ensure you have internet connection
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- Check if pip is working: `pip --version`
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- Manually install: `pip install timm` (or relevant package)
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- Check console for specific error messages
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**Permissions issues (Linux/Mac):**
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If you see permission errors, try:
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```bash
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pip install --user timm
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# or
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sudo pip install timm
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```
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### Nodes revert to VGG19
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If a modern encoder fails to load, nodes automatically fall back to VGG19. Check the console for error messages:
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```
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[DeepExColorImageNode] Warning: Could not load dinov2_vitb: ...
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[DeepExColorImageNode] Falling back to VGG19
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```
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This is expected behavior when dependencies are missing or installation fails.
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### CUDA out of memory
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**Solutions:**
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1. Use smaller encoder: `dinov2_vits` instead of `dinov2_vitb`
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2. Keep using `vgg19`
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3. Reduce image/video resolution
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4. Enable `use_half_resolution: true` for videos
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### Slow performance
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**Speed optimizations:**
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1. Use `feature_encoder: vgg19` (fastest)
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2. Use `post_processor: guided` or `none` (faster than color_matcher)
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3. Enable `use_half_resolution: true` for videos
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4. Enable `use_torch_compile: true` (10-25% speedup after warmup)
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---
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## 📁 Architecture Documentation
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For developers and contributors:
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- **Component Architecture:** [MODULAR_ARCHITECTURE.md](MODULAR_ARCHITECTURE.md)
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- **Modern Components Guide:** [MODERN_COMPONENTS_GUIDE.md](MODERN_COMPONENTS_GUIDE.md)
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- **Feature Extractors README:** [models/feature_extractors/README.md](models/feature_extractors/README.md)
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---
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## 🎯 Recommended Configurations
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### For Best Quality (Slow):
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```
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feature_encoder: dinov2_vitl
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post_processor: color_matcher
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post_process_strength: 0.8
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temporal_consistency: true (videos)
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use_torch_compile: false
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```
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### For Balanced Quality/Speed (Recommended):
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```
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feature_encoder: dinov2_vitb
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post_processor: color_matcher
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post_process_strength: 0.8
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temporal_consistency: true (videos)
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use_torch_compile: true
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```
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### For Speed (Fast Preview):
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```
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feature_encoder: vgg19
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post_processor: none
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use_half_resolution: true (videos)
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use_torch_compile: true
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```
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### For Creative Control:
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```
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feature_encoder: clip_vitb
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post_processor: color_matcher
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text_guidance: "your creative prompt here"
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text_guidance_weight: 0.3
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post_process_strength: 0.8
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```
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---
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## 📝 Notes
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- **Backward Compatible:** All existing workflows continue to work with default settings
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- **Graceful Fallback:** If modern components aren't installed, nodes automatically use VGG19
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- **Legacy Support:** Old `wls_filter_on` parameter still works (auto-converts to `post_processor: wls`)
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- **Performance Reports:** Now include encoder and post-processor information
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---
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## 🙏 Credits
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- **DINOv2:** Meta AI Research (2023)
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- **CLIP:** OpenAI (2021)
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- **color-matcher:** hahnec @ GitHub
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- **Original Deep Exemplar:** Zhang et al. (2019)
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---
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**Enjoy the upgraded video colorization! 🎨**
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For questions or issues, check the documentation or create an issue on GitHub.
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