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  XLSR is designed for lightweight real-time upscaling of images.
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- This model is an implementation of XLSR found [here](https://github.com/quic/aimet-model-zoo/tree/develop/aimet_zoo_torch/xlsr).
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  This repository provides scripts to run XLSR on Qualcomm® devices.
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  More details on model performance across various devices, can be found
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  [here](https://aihub.qualcomm.com/models/xlsr).
@@ -29,15 +29,32 @@ More details on model performance across various devices, can be found
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  - Number of parameters: 22.0K
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  - Model size: 92.7 KB
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- | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
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- | ---|---|---|---|---|---|---|---|
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 2.483 ms | 0 - 69 MB | FP16 | NPU | [XLSR.tflite](https://huggingface.co/qualcomm/XLSR/blob/main/XLSR.tflite)
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.448 ms | 0 - 3 MB | FP16 | NPU | [XLSR.so](https://huggingface.co/qualcomm/XLSR/blob/main/XLSR.so)
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-
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  ## Installation
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  ```bash
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  python -m qai_hub_models.models.xlsr.export
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  ```
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-
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  ```
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- Profile Job summary of XLSR
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- --------------------------------------------------
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- Device: Snapdragon X Elite CRD (11)
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- Estimated Inference Time: 1.46 ms
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- Estimated Peak Memory Range: 0.20-0.20 MB
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- Compute Units: NPU (21) | Total (21)
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-
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  ```
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  Get more details on XLSR's performance across various devices [here](https://aihub.qualcomm.com/models/xlsr).
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  Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
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  ## License
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- - The license for the original implementation of XLSR can be found
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- [here](https://github.com/quic/aimet-model-zoo/blob/develop/LICENSE.pdf).
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- - The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
 
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  ## References
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  * [Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices](https://arxiv.org/abs/2105.10288)
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  * [Source Model Implementation](https://github.com/quic/aimet-model-zoo/tree/develop/aimet_zoo_torch/xlsr)
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  ## Community
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  * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
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  * For questions or feedback please [reach out to us](mailto:[email protected]).
 
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  XLSR is designed for lightweight real-time upscaling of images.
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+ This model is an implementation of XLSR found [here]({source_repo}).
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  This repository provides scripts to run XLSR on Qualcomm® devices.
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  More details on model performance across various devices, can be found
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  [here](https://aihub.qualcomm.com/models/xlsr).
 
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  - Number of parameters: 22.0K
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  - Model size: 92.7 KB
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+ | Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
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+ |---|---|---|---|---|---|---|---|---|
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+ | XLSR | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 2.58 ms | 0 - 9 MB | FP16 | NPU | [XLSR.tflite](https://huggingface.co/qualcomm/XLSR/blob/main/XLSR.tflite) |
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+ | XLSR | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 1.375 ms | 0 - 3 MB | FP16 | NPU | [XLSR.so](https://huggingface.co/qualcomm/XLSR/blob/main/XLSR.so) |
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+ | XLSR | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 1.509 ms | 0 - 2 MB | FP16 | NPU | [XLSR.onnx](https://huggingface.co/qualcomm/XLSR/blob/main/XLSR.onnx) |
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+ | XLSR | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 1.793 ms | 0 - 24 MB | FP16 | NPU | [XLSR.tflite](https://huggingface.co/qualcomm/XLSR/blob/main/XLSR.tflite) |
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+ | XLSR | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 1.08 ms | 0 - 14 MB | FP16 | NPU | [XLSR.so](https://huggingface.co/qualcomm/XLSR/blob/main/XLSR.so) |
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+ | XLSR | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 1.084 ms | 0 - 23 MB | FP16 | NPU | [XLSR.onnx](https://huggingface.co/qualcomm/XLSR/blob/main/XLSR.onnx) |
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+ | XLSR | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 2.467 ms | 0 - 1 MB | FP16 | NPU | [XLSR.tflite](https://huggingface.co/qualcomm/XLSR/blob/main/XLSR.tflite) |
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+ | XLSR | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 1.359 ms | 0 - 1 MB | FP16 | NPU | Use Export Script |
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+ | XLSR | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 2.551 ms | 0 - 88 MB | FP16 | NPU | [XLSR.tflite](https://huggingface.co/qualcomm/XLSR/blob/main/XLSR.tflite) |
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+ | XLSR | SA8255 (Proxy) | SA8255P Proxy | QNN | 1.344 ms | 0 - 1 MB | FP16 | NPU | Use Export Script |
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+ | XLSR | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 2.6 ms | 2 - 3 MB | FP16 | NPU | [XLSR.tflite](https://huggingface.co/qualcomm/XLSR/blob/main/XLSR.tflite) |
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+ | XLSR | SA8775 (Proxy) | SA8775P Proxy | QNN | 1.364 ms | 0 - 1 MB | FP16 | NPU | Use Export Script |
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+ | XLSR | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 2.451 ms | 0 - 31 MB | FP16 | NPU | [XLSR.tflite](https://huggingface.co/qualcomm/XLSR/blob/main/XLSR.tflite) |
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+ | XLSR | SA8650 (Proxy) | SA8650P Proxy | QNN | 1.341 ms | 0 - 2 MB | FP16 | NPU | Use Export Script |
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+ | XLSR | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 3.255 ms | 6 - 30 MB | FP16 | NPU | [XLSR.tflite](https://huggingface.co/qualcomm/XLSR/blob/main/XLSR.tflite) |
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+ | XLSR | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 1.541 ms | 0 - 14 MB | FP16 | NPU | Use Export Script |
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+ | XLSR | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 1.913 ms | 0 - 16 MB | FP16 | NPU | [XLSR.tflite](https://huggingface.co/qualcomm/XLSR/blob/main/XLSR.tflite) |
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+ | XLSR | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 0.686 ms | 0 - 9 MB | FP16 | NPU | Use Export Script |
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+ | XLSR | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 1.057 ms | 0 - 15 MB | FP16 | NPU | [XLSR.onnx](https://huggingface.co/qualcomm/XLSR/blob/main/XLSR.onnx) |
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+ | XLSR | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 1.5 ms | 0 - 0 MB | FP16 | NPU | Use Export Script |
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+ | XLSR | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.516 ms | 9 - 9 MB | FP16 | NPU | [XLSR.onnx](https://huggingface.co/qualcomm/XLSR/blob/main/XLSR.onnx) |
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  ## Installation
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  ```bash
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  python -m qai_hub_models.models.xlsr.export
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  ```
 
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  ```
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+ Profiling Results
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+ ------------------------------------------------------------
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+ XLSR
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+ Device : Samsung Galaxy S23 (13)
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+ Runtime : TFLITE
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+ Estimated inference time (ms) : 2.6
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+ Estimated peak memory usage (MB): [0, 9]
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+ Total # Ops : 16
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+ Compute Unit(s) : NPU (13 ops) CPU (3 ops)
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  ```
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  Get more details on XLSR's performance across various devices [here](https://aihub.qualcomm.com/models/xlsr).
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  Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
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+
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  ## License
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+ * The license for the original implementation of XLSR can be found [here](https://github.com/quic/aimet-model-zoo/blob/develop/LICENSE.pdf).
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+ * The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
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+
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  ## References
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  * [Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices](https://arxiv.org/abs/2105.10288)
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  * [Source Model Implementation](https://github.com/quic/aimet-model-zoo/tree/develop/aimet_zoo_torch/xlsr)
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  ## Community
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  * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
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  * For questions or feedback please [reach out to us](mailto:[email protected]).