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  ESRGAN is a machine learning model that upscales an image with minimal loss in quality.
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- This model is an implementation of ESRGAN found [here](https://github.com/xinntao/ESRGAN/).
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  This repository provides scripts to run ESRGAN 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/esrgan).
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  - Number of parameters: 16.7M
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  - Model size: 64.0 MB
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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 | 70.5 ms | 3 - 6 MB | FP16 | NPU | [ESRGAN.tflite](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.tflite)
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 70.092 ms | 0 - 104 MB | FP16 | NPU | [ESRGAN.so](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.so)
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  ## Installation
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  ```bash
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  python -m qai_hub_models.models.esrgan.export
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  ```
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-
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  ```
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- Profile Job summary of ESRGAN
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- --------------------------------------------------
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- Device: Snapdragon X Elite CRD (11)
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- Estimated Inference Time: 65.27 ms
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- Estimated Peak Memory Range: 0.20-0.20 MB
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- Compute Units: NPU (1026) | Total (1026)
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-
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  ```
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  Get more details on ESRGAN's performance across various devices [here](https://aihub.qualcomm.com/models/esrgan).
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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 ESRGAN can be found
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- [here](https://github.com/xinntao/ESRGAN/blob/master/LICENSE).
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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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  * [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://arxiv.org/abs/1809.00219)
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  * [Source Model Implementation](https://github.com/xinntao/ESRGAN/)
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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:ai-hub-support@qti.qualcomm.com).
 
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  ESRGAN is a machine learning model that upscales an image with minimal loss in quality.
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+ This model is an implementation of ESRGAN found [here]({source_repo}).
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  This repository provides scripts to run ESRGAN 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/esrgan).
 
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  - Number of parameters: 16.7M
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  - Model size: 64.0 MB
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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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+ | ESRGAN | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 67.448 ms | 3 - 6 MB | FP16 | NPU | [ESRGAN.tflite](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.tflite) |
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+ | ESRGAN | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 70.723 ms | 0 - 110 MB | FP16 | NPU | [ESRGAN.so](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.so) |
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+ | ESRGAN | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 70.475 ms | 0 - 42 MB | FP16 | NPU | [ESRGAN.onnx](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.onnx) |
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+ | ESRGAN | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 55.287 ms | 3 - 659 MB | FP16 | NPU | [ESRGAN.tflite](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.tflite) |
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+ | ESRGAN | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 55.722 ms | 0 - 110 MB | FP16 | NPU | [ESRGAN.so](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.so) |
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+ | ESRGAN | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 59.118 ms | 6 - 695 MB | FP16 | NPU | [ESRGAN.onnx](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.onnx) |
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+ | ESRGAN | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 60.535 ms | 3 - 807 MB | FP16 | NPU | [ESRGAN.tflite](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.tflite) |
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+ | ESRGAN | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 62.046 ms | 0 - 2 MB | FP16 | NPU | Use Export Script |
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+ | ESRGAN | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 64.451 ms | 3 - 8 MB | FP16 | NPU | [ESRGAN.tflite](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.tflite) |
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+ | ESRGAN | SA8255 (Proxy) | SA8255P Proxy | QNN | 62.024 ms | 0 - 2 MB | FP16 | NPU | Use Export Script |
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+ | ESRGAN | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 68.788 ms | 3 - 6 MB | FP16 | NPU | [ESRGAN.tflite](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.tflite) |
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+ | ESRGAN | SA8775 (Proxy) | SA8775P Proxy | QNN | 63.011 ms | 0 - 2 MB | FP16 | NPU | Use Export Script |
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+ | ESRGAN | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 64.819 ms | 3 - 6 MB | FP16 | NPU | [ESRGAN.tflite](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.tflite) |
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+ | ESRGAN | SA8650 (Proxy) | SA8650P Proxy | QNN | 63.19 ms | 0 - 2 MB | FP16 | NPU | Use Export Script |
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+ | ESRGAN | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 136.101 ms | 3 - 619 MB | FP16 | NPU | [ESRGAN.tflite](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.tflite) |
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+ | ESRGAN | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 134.526 ms | 0 - 88 MB | FP16 | NPU | Use Export Script |
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+ | ESRGAN | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 42.308 ms | 0 - 180 MB | FP16 | NPU | [ESRGAN.tflite](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.tflite) |
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+ | ESRGAN | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 37.764 ms | 1 - 131 MB | FP16 | NPU | Use Export Script |
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+ | ESRGAN | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 38.328 ms | 8 - 186 MB | FP16 | NPU | [ESRGAN.onnx](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.onnx) |
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+ | ESRGAN | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 64.824 ms | 0 - 0 MB | FP16 | NPU | Use Export Script |
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+ | ESRGAN | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 65.67 ms | 38 - 38 MB | FP16 | NPU | [ESRGAN.onnx](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.onnx) |
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  ## Installation
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  ```bash
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  python -m qai_hub_models.models.esrgan.export
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  ```
 
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  ```
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+ Profiling Results
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+ ------------------------------------------------------------
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+ ESRGAN
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+ Device : Samsung Galaxy S23 (13)
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+ Runtime : TFLITE
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+ Estimated inference time (ms) : 67.4
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+ Estimated peak memory usage (MB): [3, 6]
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+ Total # Ops : 1024
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+ Compute Unit(s) : NPU (1024 ops)
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  ```
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  Get more details on ESRGAN's performance across various devices [here](https://aihub.qualcomm.com/models/esrgan).
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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 ESRGAN can be found [here](https://github.com/xinntao/ESRGAN/blob/master/LICENSE).
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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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  * [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://arxiv.org/abs/1809.00219)
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  * [Source Model Implementation](https://github.com/xinntao/ESRGAN/)
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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:ai-hub-support@qti.qualcomm.com).