0.4.0

oidn-web 0.4.0 replaces the TensorFlow.js inference stack with a purpose-built, model-driven WebGPU runtime. Existing initUNetFromURL and initUNetFromBuffer integrations remain supported while gaining native FP16, adaptive scheduling, runtime diagnostics, and stronger model validation.

Highlights

• Removed TensorFlow.js and its WebGPU backend from the runtime dependencies.
• Added a custom WGSL U-Net executor with automatic FP16 selection and a deterministic FP32 fallback.
• Added built-in topology descriptors for the current OIDN small and large RT models, including clean auxiliary variants.
• Reduced the reference Vite ESM bundle from approximately 168.8 KB to 32.4 KB gzip compared with 0.3.5, a reduction of about 81%.
• Reached a 27.6 ms median for a 512 x 512 Clean Aux Large inference on the tested Apple GPU, versus 35.0 ms for the TensorFlow.js baseline (1.27x faster). Performance depends on the browser, GPU, model, and tile size.

Added

• Model-driven graph planning and lifetime-aware activation-buffer reuse.
• Versioned UNetModelSpec descriptors, topology detection, and validation of tensor names, layouts, data types, byte lengths, kernel shapes, bias shapes, and graph channel flow.
• modelSpec initialization option for future or custom OIDN model topologies.
• npm run model:inspect for model hashes, tensor signatures, and descriptor compatibility checks.
• Dynamic tile sizing with configurable minimum, initial, and maximum tile sizes and a target GPU time.
• GPU queue backpressure between tiles to keep cancellation and rendering interaction responsive.
• Runtime diagnostics through getRuntimeInfo(), including the selected engine, precision, model, kernel capabilities, tile state, and resource statistics.
• Per-layer GPU profiling through profileNextExecution() and getLastExecutionProfile() when timestamp-query is enabled.
• Cross-version browser benchmarks with queue-complete timings, sampled output validation, TFJS baseline comparison, and per-layer GPU profiles.
• Explicit, idempotent resource disposal and resource lifecycle accounting.
• Automated model, graph, scheduler, resource leak, allocation rollback, and late-async-cleanup tests.

Changed

• U-Net inference now runs directly in WGSL. The stable auto engine selects the native WGSL backend and no longer initializes TensorFlow.js.
• FP16-capable devices retain TZA half-float weights and use short FP16 FMA accumulation groups folded into FP32 accumulators. The final output remains FP32.
• Devices without shader-f16 automatically use native FP32 inference.
• Convolution tensors and activations use a blocked four-channel layout with channel-specialized shaders.
• Decoder upsample + concat + conv patterns are fused, and all network passes for a tile are encoded into one command buffer.
• Shape-independent pipelines are compiled asynchronously before initialization resolves to avoid first-denoise shader compilation stalls.
• maxTileSize is now a hard upper bound for the adaptive tile controller.
• A shared GPUDevice uses FP16 only when shader-f16 was requested during device creation; WebGPU features cannot be enabled afterward.

Migration notes

• No changes are required for basic initUNetFromURL or initUNetFromBuffer usage.
• Call dispose() when a U-Net instance is no longer needed.
• When supplying an existing GPUDevice, request shader-f16 before creating the device if FP16 inference is desired.
• Set dynamicTile: false to restore fixed-size tiling.

Full Changelog: v0.3.2...v0.4.0oidn-web 0.4.0 replaces the TensorFlow.js inference stack with a purpose-built, model-driven WebGPU runtime. Existing initUNetFromURL and
initUNetFromBuffer integrations remain supported while gaining native FP16,
adaptive scheduling, runtime diagnostics, and stronger model validation.

Highlights

• Removed TensorFlow.js and its WebGPU backend from the runtime dependencies.
• Added a custom WGSL U-Net executor with automatic FP16 selection and a
  deterministic FP32 fallback.
• Added built-in topology descriptors for the current OIDN small and large RT
  models, including clean auxiliary variants.
• Reduced the reference Vite ESM bundle from approximately 168.8 KB to 32.4 KB
  gzip compared with 0.3.5, a reduction of about 81%.
• Reached a 27.6 ms median for a 512 x 512 Clean Aux Large inference on the
  tested Apple GPU, versus 35.0 ms for the TensorFlow.js baseline (1.27x
  faster). Performance depends on the browser, GPU, model, and tile size.

Added

• Model-driven graph planning and lifetime-aware activation-buffer reuse.
• Versioned UNetModelSpec descriptors, topology detection, and validation of
  tensor names, layouts, data types, byte lengths, kernel shapes, bias shapes,
  and graph channel flow.
• modelSpec initialization option for future or custom OIDN model
  topologies.
• npm run model:inspect for model hashes, tensor signatures, and descriptor
  compatibility checks.
• Dynamic tile sizing with configurable minimum, initial, and maximum tile
  sizes and a target GPU time.
• GPU queue backpressure between tiles to keep cancellation and rendering
  interaction responsive.
• Runtime diagnostics through getRuntimeInfo(), including the selected
  engine, precision, model, kernel capabilities, tile state, and resource
  statistics.
• Per-layer GPU profiling through profileNextExecution() and
  getLastExecutionProfile() when timestamp-query is enabled.
• Cross-version browser benchmarks with queue-complete timings, sampled output
  validation, TFJS baseline comparison, and per-layer GPU profiles.
• Explicit, idempotent resource disposal and resource lifecycle accounting.
• Automated model, graph, scheduler, resource leak, allocation rollback, and
  late-async-cleanup tests.

Changed

• U-Net inference now runs directly in WGSL. The stable auto engine selects
  the native WGSL backend and no longer initializes TensorFlow.js.
• FP16-capable devices retain TZA half-float weights and use short FP16 FMA
  accumulation groups folded into FP32 accumulators. The final output remains
  FP32.
• Devices without shader-f16 automatically use native FP32 inference.
• Convolution tensors and activations use a blocked four-channel layout with
  channel-specialized shaders.
• Decoder upsample + concat + conv patterns are fused, and all network passes
  for a tile are encoded into one command buffer.
• Shape-independent pipelines are compiled asynchronously before initialization
  resolves to avoid first-denoise shader compilation stalls.
• maxTileSize is now a hard upper bound for the adaptive tile controller.
• A shared GPUDevice uses FP16 only when shader-f16 was requested during
  device creation; WebGPU features cannot be enabled afterward.

Migration notes

• No changes are required for basic initUNetFromURL or
  initUNetFromBuffer usage.
• Call dispose() when a U-Net instance is no longer needed.
• When supplying an existing GPUDevice, request shader-f16 before creating
  the device if FP16 inference is desired.
• Set dynamicTile: false to restore fixed-size tiling.