* Include the MLX gemm kernels.
* Clippy lints.
* Export the gemm_f32 kernel.
* Add the f16/bf16 variants.
* Add the initial dispatch code.
* More plugging of the mlx kernels.
* Add a currently broken test.
* Tweaks.
* Bugfix + get the tests to pass.
* Enable the gemm bf16 tests.
* Add some randomized tests.
* Update candle-metal-kernels/src/lib.rs
Co-authored-by: ivarflakstad <69173633+ivarflakstad@users.noreply.github.com>
* More fixes.
* More clippy fixes.
---------
Co-authored-by: ivarflakstad <69173633+ivarflakstad@users.noreply.github.com>
* Allow loading images with given std and mean
* OpenCLIP text encoder component
* Two MobileCLIP models
* Clippy fixes.
---------
Co-authored-by: Laurent <laurent.mazare@gmail.com>
* silero-vad v5 example
This change adds an example of how to run silero-vad v5
* PR: rename 'vad' to 'silero-vad'
* Update README.md
---------
Co-authored-by: Laurent Mazare <laurent.mazare@gmail.com>
index_select does not support negative indexing, but
this change adds just enough workarounds in onnx to
allow evaluating silero-vad models (which make use of
negative indices).
* onnx: workaround pow with negative base
rather than fully defining pow in the cpu backend (as in #2318),
this implements a much smaller change which is sufficient to evaluate silero-vad
onnx models. Specifically, checking if pow is run with 2.0 exponent, and if so
evaluate as simply `x*x` instead of the cpu backend of `e^(2.0 * ln(x))`.
* PR: use Tensor::powf insead
powf correctly handles a negative base.
* Start sketching parler-tts support.
* Implement the attention.
* Add the example code.
* Fix the example.
* Add the description + t5 encode it.
* More of the parler forward pass.
* Fix the positional embeddings.
* Support random sampling in generation.
* Handle EOS.
* Add the python decoder.
* Proper causality mask.
* Soft NMS with thresholds
* NMS Test
* Soft nms w/ boxes removed below threshold
* Soft nms test
* No longer removing bounding boxes to fit Soft-NMS focus
* Initialize confidence
* Added comments
* Refactored out updating based on IOU/sigma
* Score_threshold -> confidence_threshold for clarity
* Remove bboxes below confidence threshold
* Softnms basic functionality test
* Softnms confidence decay test
* Softnms confidence threshold test
* Softnms no overlapping bbox test
* Testing confidence after no overlap test
* Single bbox and no bbox tests
* Signify test completion
* Handling result of test functions
* Checking all pairs of bboxes instead of a forward pass
* Equal confidence overlap test
* Clarified tests for implementation
* No longer dropping boxes, just setting to 0.0
* Formatted w/ cargo
* Add the flux autoencoder.
* Add the encoder down-blocks.
* Upsampling in the decoder.
* Sketch the flow matching model.
* More flux model.
* Add some of the positional embeddings.
* Add the rope embeddings.
* Add the sampling functions.
* Add the flux example.
* Fix the T5 bits.
* Proper T5 tokenizer.
* Clip encoder path fix.
* Get the clip embeddings.
* No configurable weights in layer norm.
* More weights related fixes.
* Yet another shape fix.
* DType fix.
* Fix a couple more shape issues.
* DType fixes.
* Fix the latent dims.
* Fix more shape issues.
* Autoencoder fixes.
* Get some generations out.
* Bugfix.
* T5 padding.
* Clippy fix.
* Add the decode only mode.
* Fix.
* More fixes.
* Finally get some generations to work.
* Add readme.
