Image-to-Image
PyTorch
ONNX
TensorRT
English
super-resolution
image-restoration
sisr
onnxruntime
spandrel
chainner
edge
deployment
mit
Instructions to use Phips/PULSE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TensorRT
How to use Phips/PULSE with TensorRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Clarify PULSE as tiny NERVE sibling
Browse files
README.md
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**A tiny, deployment-first super-resolution network.**
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PULSE is the
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- `pulse`: 48 channels, **353,856 parameters**
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- `pulse_fast`: 32 channels, **162,192 parameters**
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**A tiny, deployment-first super-resolution network.**
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PULSE is the **tiny/fast sibling of NERVE**: a much smaller convolutional SISR
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model with the same deployment-first philosophy. It trades capacity for size and
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speed, so it is less capable than NERVE and is intended for light cleanup and
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clean upscaling rather than heavy restoration. It trains on a consumer GPU,
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converts without surprises, and embeds almost anywhere.
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- `pulse`: 48 channels, **353,856 parameters**
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- `pulse_fast`: 32 channels, **162,192 parameters**
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