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Auxiliary textures were created during init() using default dimensions
(1280x720) before resize() was called with actual window size. This
caused compute shaders to receive uniforms with correct resolution but
render to wrong-sized textures.
Changes:
- Add MainSequence::resize_auxiliary_texture() to recreate textures
- Override resize() in CircleMaskEffect to resize circle_mask texture
- Override resize() in CNNEffect to resize captured_frame texture
- Bind groups are recreated with new texture views after resize
Tests: All 36 tests passing
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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Root cause: Uniform buffers created but not initialized before bind group
creation, causing undefined UV coordinates in circle_mask_compute.wgsl.
Changes:
- Add get_common_uniforms() helper to Effect base class
- Refactor render()/compute() signatures: 5 params → CommonPostProcessUniforms&
- Fix uninitialized uniforms in CircleMaskEffect and CNNEffect
- Update all 19 effect implementations and headers
- Fix WGSL syntax error in FlashEffect (u.audio_intensity → audio_intensity)
- Update test files (test_sequence.cc)
Benefits:
- Cleaner API: construct uniforms once per frame, reuse across effects
- More maintainable: CommonPostProcessUniforms changes need no call site updates
- Fixes UV coordinate bug in circle_mask_compute.wgsl
All 36 tests passing (100%)
handoff(Claude): Effect API refactor complete
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PyTorch Conv2d uses zero-padding; shader was using Repeat mode which
wraps edges. ClampToEdge better approximates zero-padding behavior.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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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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