| Age | Commit message (Collapse) | Author | |
|---|---|---|---|
| 20 hours | chore: Update CNN architecture to 3×3×3 with new trained weights | skal | |
| Changed from 3×5×3 to 3×3×3 architecture for testing. Changes: - cnn_layer.wgsl: Use 3×3 conv for all layers - cnn_weights_generated.wgsl: Regenerated weights - image_style_processor.py: Made executable handoff(Claude): CNN mismatch analysis complete, patch extraction added, docs updated Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com> | |||
| 33 hours | feat: Add coordinate-aware CNN layer 0 for position-dependent stylization | skal | |
| - Implement CoordConv2d custom layer accepting (x,y) patch center - Split layer 0 weights: rgba_weights (9x mat4x4) + coord_weights (mat2x4) - Add *_with_coord() functions to 3x3/5x5/7x7 convolution shaders - Update training script to generate coordinate grid and export split weights - Regenerate placeholder weights with new format Size impact: +32B coord weights + ~100B shader code = +132B total All 36 tests passing (100%) handoff(Claude): CNN coordinate awareness implemented, ready for training Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com> | |||
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index : demo.git | |
| Vide-coded 64k demo system |
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