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README.md

@@ -16,7 +16,7 @@ tags:
 
 On-device, high-resolution image synthesis from text and image prompts. ControlNet guides Stable-diffusion with provided input image to generate accurate images from given input prompt.
 
-This model is an implementation of ControlNet found [here](https://github.com/lllyasviel/ControlNet).
+This model is an implementation of ControlNet found [here]({source_repo}).
 This repository provides scripts to run ControlNet on Qualcomm® devices.
 More details on model performance across various devices, can be found
 [here](https://aihub.qualcomm.com/models/controlnet_quantized).
@@ -34,17 +34,23 @@ More details on model performance across various devices, can be found
   - ControlNet Number of parameters: 361M
   - Model size: 1.4GB
 
+| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
+|---|---|---|---|---|---|---|---|---|
+| TextEncoder_Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 11.394 ms | 0 - 74 MB | UINT16 | NPU | [ControlNet.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/TextEncoder_Quantized.bin) |
+| TextEncoder_Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 8.08 ms | 0 - 137 MB | UINT16 | NPU | [ControlNet.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/TextEncoder_Quantized.bin) |
+| TextEncoder_Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 10.982 ms | 0 - 1 MB | UINT16 | NPU | Use Export Script |
+| UNet_Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 262.52 ms | 11 - 17 MB | UINT16 | NPU | [ControlNet.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/UNet_Quantized.bin) |
+| UNet_Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 192.789 ms | 3 - 1247 MB | UINT16 | NPU | [ControlNet.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/UNet_Quantized.bin) |
+| UNet_Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 260.158 ms | 14 - 15 MB | UINT16 | NPU | Use Export Script |
+| VAEDecoder_Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 390.243 ms | 0 - 36 MB | UINT16 | NPU | [ControlNet.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/VAEDecoder_Quantized.bin) |
+| VAEDecoder_Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 294.404 ms | 0 - 88 MB | UINT16 | NPU | [ControlNet.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/VAEDecoder_Quantized.bin) |
+| VAEDecoder_Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 379.548 ms | 0 - 1 MB | UINT16 | NPU | Use Export Script |
+| ControlNet_Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 100.33 ms | 2 - 68 MB | UINT16 | NPU | [ControlNet.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_Quantized.bin) |
+| ControlNet_Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 76.94 ms | 0 - 533 MB | UINT16 | NPU | [ControlNet.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_Quantized.bin) |
+| ControlNet_Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 103.52 ms | 2 - 3 MB | UINT16 | NPU | Use Export Script |
 
 
 
-| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
-| ---|---|---|---|---|---|---|---|
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Binary | 11.394 ms | 0 - 74 MB | UINT16 | NPU |  [TextEncoder_Quantized.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/TextEncoder_Quantized.bin) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Binary | 262.52 ms | 11 - 17 MB | UINT16 | NPU |  [UNet_Quantized.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/UNet_Quantized.bin) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Binary | 390.243 ms | 0 - 36 MB | UINT16 | NPU |  [VAEDecoder_Quantized.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/VAEDecoder_Quantized.bin) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Binary | 100.33 ms | 2 - 68 MB | UINT16 | NPU |  [ControlNet_Quantized.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_Quantized.bin) 
-
-
 
 ## Installation
 
@@ -100,37 +106,43 @@ device. This script does the following:
 ```bash
 python -m qai_hub_models.models.controlnet_quantized.export
 ```
-
 ```
-Profile Job summary of TextEncoder_Quantized
---------------------------------------------------
-Device: QCS8550 (Proxy) (12)
-Estimated Inference Time: 10.98 ms
-Estimated Peak Memory Range: 0.07-1.20 MB
-Compute Units: NPU (569) | Total (569)
-
-Profile Job summary of UNet_Quantized
---------------------------------------------------
-Device: QCS8550 (Proxy) (12)
-Estimated Inference Time: 260.16 ms
-Estimated Peak Memory Range: 13.75-15.10 MB
-Compute Units: NPU (5433) | Total (5433)
-
-Profile Job summary of VAEDecoder_Quantized
---------------------------------------------------
-Device: QCS8550 (Proxy) (12)
-Estimated Inference Time: 379.55 ms
-Estimated Peak Memory Range: 0.28-1.47 MB
-Compute Units: NPU (408) | Total (408)
-
-Profile Job summary of ControlNet_Quantized
---------------------------------------------------
-Device: QCS8550 (Proxy) (12)
-Estimated Inference Time: 103.52 ms
-Estimated Peak Memory Range: 1.85-3.07 MB
-Compute Units: NPU (2405) | Total (2405)
-
-
+Profiling Results
+------------------------------------------------------------
+TextEncoder_Quantized
+Device                          : Samsung Galaxy S23 (13)
+Runtime                         : QNN                    
+Estimated inference time (ms)   : 11.4                   
+Estimated peak memory usage (MB): [0, 74]                
+Total # Ops                     : 570                    
+Compute Unit(s)                 : NPU (570 ops)          
+
+------------------------------------------------------------
+UNet_Quantized
+Device                          : Samsung Galaxy S23 (13)
+Runtime                         : QNN                    
+Estimated inference time (ms)   : 262.5                  
+Estimated peak memory usage (MB): [11, 17]               
+Total # Ops                     : 5434                   
+Compute Unit(s)                 : NPU (5434 ops)         
+
+------------------------------------------------------------
+VAEDecoder_Quantized
+Device                          : Samsung Galaxy S23 (13)
+Runtime                         : QNN                    
+Estimated inference time (ms)   : 390.2                  
+Estimated peak memory usage (MB): [0, 36]                
+Total # Ops                     : 409                    
+Compute Unit(s)                 : NPU (409 ops)          
+
+------------------------------------------------------------
+ControlNet_Quantized
+Device                          : Samsung Galaxy S23 (13)
+Runtime                         : QNN                    
+Estimated inference time (ms)   : 100.3                  
+Estimated peak memory usage (MB): [2, 68]                
+Total # Ops                     : 2406                   
+Compute Unit(s)                 : NPU (2406 ops)         
 ```
 
 
@@ -254,15 +266,19 @@ provides instructions on how to use the `.so` shared library or `.bin` context b
 Get more details on ControlNet's performance across various devices [here](https://aihub.qualcomm.com/models/controlnet_quantized).
 Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 
+
 ## License
-- The license for the original implementation of ControlNet can be found
-  [here](https://github.com/lllyasviel/ControlNet/blob/main/LICENSE).
-- The license for the compiled assets for on-device deployment can be found [here](https://github.com/lllyasviel/ControlNet/blob/main/LICENSE)
+* The license for the original implementation of ControlNet can be found [here](https://github.com/lllyasviel/ControlNet/blob/main/LICENSE).
+* The license for the compiled assets for on-device deployment can be found [here](https://github.com/lllyasviel/ControlNet/blob/main/LICENSE)
+
+
 
 ## References
 * [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543)
 * [Source Model Implementation](https://github.com/lllyasviel/ControlNet)
 
+
+
 ## Community
 * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
 * For questions or feedback please [reach out to us](mailto:[email protected]).