Simplify DeepExemplar to VGG19-only, fix ColorVidNet skip connections

This commit simplifies the DeepExemplar nodes and fixes several critical issues
discovered during encoder compatibility testing.

Key Changes:

**DeepExemplar Simplification:**
- Removed experimental encoder support from DeepExemplar nodes (VGG19 only)
- Removed feature_encoder, text_guidance parameters from both Image/Video nodes
- Hard-coded DeepExemplar to use VGG19 (its trained baseline)
- Removed 100+ lines of encoder loading/fallback logic
- Simplified dimension adjustment to always use min_multiple=32

Rationale: DeepExemplar's NonlocalNet and ColorNet were trained end-to-end
with VGG19 features. Random projection layers cannot meaningfully translate
between feature spaces (DINOv2/CLIP → VGG19) without training, resulting in
color artifacts. Alternative encoders remain available for ColorMNet.

**ColorVidNet Skip Connection Fixes:**
- Added dynamic interpolation for skip connection alignment
- Fixed three skip connections (conv8_1+conv3_3, conv9_1+conv2_2, conv10_1+conv1_2)
- Prevents shape mismatches with non-standard input dimensions

**NonlocalNet Dynamic Upsampling:**
- Replaced fixed scale_factor=4 upsampling with dynamic interpolation
- Added feature alignment before concatenation
- Ensures compatibility with variable input dimensions

**Feature Extractors Documentation:**
- Added comprehensive warnings about experimental encoder limitations
- Added dinov2_vits support to encoder factory
- Fixed DINOv2 non-square patch grid calculations (patch_h = H//14, patch_w = W//14)
- Fixed CLIP device placement for normalization buffers and projection layers
- Changed CLIP normalization from CLIP-native to ImageNet for compatibility

**Code Cleanup:**
- Removed development markdown files (GITHUB_READY.md, etc.)
- Removed post_processing package (bilateral, guided, WLS filters)
- Removed training/testing scripts (train.py, test.py)
- Removed .vscode configuration files

This leaves a cleaner, production-ready codebase focused on inference.

Tested and verified:
- DeepExemplar Image/Video nodes work correctly with VGG19
- ColorMNet nodes support all encoders (vgg19, resnet50, dinov2_*, clip_vitb)
- Skip connections handle arbitrary input dimensions
- All crashes and color artifacts resolved

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

lib/.vscode/settings.json

@@ -1,3 +0,0 @@
-{
-    "python.formatting.provider": "yapf"
-}