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Add BindGroupLayoutBuilder, BindGroupBuilder, RenderPipelineBuilder,
and SamplerCache to reduce repetitive WGPU code. Refactor
post_process_helper, cnn_effect, and rotating_cube_effect.
Changes:
- Bind group creation: 19 instances, 14→4 lines each
- Pipeline creation: 30-50→8 lines
- Sampler deduplication: 6 instances → cached
- Total boilerplate reduction: -122 lines across 3 files
Builder pattern prevents binding index errors and consolidates
platform-specific #ifdef in fewer locations. Binary size unchanged
(6.3M debug). Tests pass.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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Two bugs causing black screen when CNN post-processing activated:
1. Framebuffer capture timing: Capture ran inside post-effect loop after
ping-pong swaps, causing layers 1+ to capture wrong buffer. Moved
capture before loop to copy framebuffer_a once before post-chain starts.
2. Missing uniforms update: CNNEffect never updated uniforms_ buffer,
leaving uniforms.resolution uninitialized (0,0). UV calculation
p.xy/uniforms.resolution produced NaN, causing all texture samples
to return black. Added uniforms update in update_bind_group().
Files modified:
- src/gpu/effect.cc: Capture before post-chain (lines 308-346)
- src/gpu/effects/cnn_effect.cc: Add uniforms update (lines 132-142)
- workspaces/main/shaders/cnn/cnn_layer.wgsl: Remove obsolete comment
- doc/CNN_DEBUG.md: Historical debugging doc
- CLAUDE.md: Reference CNN_DEBUG.md in historical section
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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CNNEffect's "original" input was black because FadeEffect (priority 1) ran
before CNNEffect (priority 1), fading the scene. Changed framebuffer capture
to use framebuffer_a (scene output) instead of current_input (post-chain).
Also add seq_compiler validation to detect post-process priority collisions
within and across concurrent sequences, preventing similar render order issues.
Updated stub_types.h WGPULoadOp enum values to match webgpu.h spec.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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Implements automatic layer chaining and generic framebuffer capture API for
multi-layer neural network effects with proper original input preservation.
Key changes:
- Effect::needs_framebuffer_capture() - generic API for pre-render capture
- MainSequence: auto-capture to "captured_frame" auxiliary texture
- CNNEffect: multi-layer support via layer_index/total_layers params
- seq_compiler: expands "layers=N" to N chained effect instances
- Shader: @binding(4) original_input available to all layers
- Training: generates layer switches and original input binding
- Blend: mix(original, result, blend_amount) uses layer 0 input
Timeline syntax: CNNEffect layers=3 blend=0.7
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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Implements multi-layer convolutional neural network shader for stylized
post-processing of 3D rendered scenes:
**Core Components:**
- CNNEffect: C++ effect class with single-layer rendering (expandable to multi-pass)
- Modular WGSL snippets: cnn_activation, cnn_conv3x3/5x5/7x7, cnn_weights_generated
- Placeholder identity-like weights for initial testing (to be replaced by trained weights)
**Architecture:**
- Flexible kernel sizes (3×3, 5×5, 7×7) via separate snippet files
- ShaderComposer integration (#include resolution)
- Residual connections (input + processed output)
- Supports parallel convolutions (design ready, single conv implemented)
**Size Impact:**
- ~3-4 KB shader code (snippets + main shader)
- ~2-4 KB weights (depends on network architecture when trained)
- Total: ~5-8 KB (acceptable for 64k demo)
**Testing:**
- CNNEffect added to test_demo_effects.cc
- 36/36 tests passing (100%)
**Next Steps:**
- Training script (scripts/train_cnn.py) to generate real weights
- Multi-layer rendering with ping-pong textures
- Weight quantization for size optimization
handoff(Claude): CNN effect foundation complete, ready for training integration
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