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README.md

@@ -37,18 +37,39 @@ More details on model performance across various devices, can be found
 
 | 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 | 11.633 ms | 0 - 1 MB | INT8 | NPU | [Stable-Diffusion-v2.1.bin](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/TextEncoder_Quantized.bin) |
-| TextEncoder_Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 7.759 ms | 0 - 8 MB | INT8 | NPU | [Stable-Diffusion-v2.1.bin](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/TextEncoder_Quantized.bin) |
-| TextEncoder_Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 11.773 ms | 0 - 0 MB | INT8 | NPU | Use Export Script |
-| TextEncoder_Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 10.7 ms | 0 - 1 MB | UINT16 | NPU | Use Export Script |
-| VAEDecoder_Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 217.134 ms | 0 - 2 MB | INT8 | NPU | [Stable-Diffusion-v2.1.bin](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/VAEDecoder_Quantized.bin) |
-| VAEDecoder_Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 161.705 ms | 0 - 8 MB | INT8 | NPU | [Stable-Diffusion-v2.1.bin](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/VAEDecoder_Quantized.bin) |
-| VAEDecoder_Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 220.179 ms | 0 - 0 MB | INT8 | NPU | Use Export Script |
-| VAEDecoder_Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 225.416 ms | 0 - 2 MB | UINT16 | NPU | Use Export Script |
-| UNet_Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 101.094 ms | 0 - 2 MB | INT8 | NPU | [Stable-Diffusion-v2.1.bin](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/UNet_Quantized.bin) |
-| UNet_Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 72.62 ms | 0 - 8 MB | INT8 | NPU | [Stable-Diffusion-v2.1.bin](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/UNet_Quantized.bin) |
-| UNet_Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 102.486 ms | 0 - 0 MB | INT8 | NPU | Use Export Script |
-| UNet_Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 96.631 ms | 1 - 2 MB | UINT16 | NPU | Use Export Script |
+| TextEncoderQuantizable | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 6.622 ms | 0 - 2 MB | W8A16 | NPU | [Stable-Diffusion-v2.1.so](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/TextEncoderQuantizable.so) |
+| TextEncoderQuantizable | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 4.851 ms | 0 - 19 MB | W8A16 | NPU | [Stable-Diffusion-v2.1.so](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/TextEncoderQuantizable.so) |
+| TextEncoderQuantizable | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 4.198 ms | 0 - 15 MB | W8A16 | NPU | Use Export Script |
+| TextEncoderQuantizable | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 6.896 ms | 0 - 0 MB | W8A16 | NPU | Use Export Script |
+| TextEncoderQuantizable | SA7255P ADP | SA7255P | QNN | 88.097 ms | 0 - 8 MB | W8A16 | NPU | Use Export Script |
+| TextEncoderQuantizable | SA8255 (Proxy) | SA8255P Proxy | QNN | 6.68 ms | 0 - 2 MB | W8A16 | NPU | Use Export Script |
+| TextEncoderQuantizable | SA8650 (Proxy) | SA8650P Proxy | QNN | 6.651 ms | 0 - 5 MB | W8A16 | NPU | Use Export Script |
+| TextEncoderQuantizable | SA8775P ADP | SA8775P | QNN | 7.894 ms | 0 - 10 MB | W8A16 | NPU | Use Export Script |
+| TextEncoderQuantizable | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 88.097 ms | 0 - 8 MB | W8A16 | NPU | Use Export Script |
+| TextEncoderQuantizable | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 6.643 ms | 0 - 3 MB | W8A16 | NPU | Use Export Script |
+| TextEncoderQuantizable | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 7.894 ms | 0 - 10 MB | W8A16 | NPU | Use Export Script |
+| UnetQuantizable | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 97.767 ms | 0 - 3 MB | W8A16 | NPU | [Stable-Diffusion-v2.1.so](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/UnetQuantizable.so) |
+| UnetQuantizable | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 69.335 ms | 0 - 19 MB | W8A16 | NPU | [Stable-Diffusion-v2.1.so](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/UnetQuantizable.so) |
+| UnetQuantizable | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 61.27 ms | 0 - 14 MB | W8A16 | NPU | Use Export Script |
+| UnetQuantizable | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 99.423 ms | 0 - 0 MB | W8A16 | NPU | Use Export Script |
+| UnetQuantizable | SA7255P ADP | SA7255P | QNN | 1468.169 ms | 0 - 8 MB | W8A16 | NPU | Use Export Script |
+| UnetQuantizable | SA8255 (Proxy) | SA8255P Proxy | QNN | 96.812 ms | 0 - 2 MB | W8A16 | NPU | Use Export Script |
+| UnetQuantizable | SA8650 (Proxy) | SA8650P Proxy | QNN | 97.233 ms | 0 - 3 MB | W8A16 | NPU | Use Export Script |
+| UnetQuantizable | SA8775P ADP | SA8775P | QNN | 110.658 ms | 0 - 9 MB | W8A16 | NPU | Use Export Script |
+| UnetQuantizable | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 1468.169 ms | 0 - 8 MB | W8A16 | NPU | Use Export Script |
+| UnetQuantizable | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 98.147 ms | 0 - 3 MB | W8A16 | NPU | Use Export Script |
+| UnetQuantizable | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 110.658 ms | 0 - 9 MB | W8A16 | NPU | Use Export Script |
+| VaeDecoderQuantizable | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 274.636 ms | 0 - 4 MB | W8A16 | NPU | [Stable-Diffusion-v2.1.so](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/VaeDecoderQuantizable.so) |
+| VaeDecoderQuantizable | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 206.701 ms | 0 - 18 MB | W8A16 | NPU | [Stable-Diffusion-v2.1.so](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/VaeDecoderQuantizable.so) |
+| VaeDecoderQuantizable | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 189.387 ms | 0 - 355 MB | W8A16 | NPU | Use Export Script |
+| VaeDecoderQuantizable | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 266.827 ms | 0 - 0 MB | W8A16 | NPU | Use Export Script |
+| VaeDecoderQuantizable | SA7255P ADP | SA7255P | QNN | 4462.005 ms | 1 - 10 MB | W8A16 | NPU | Use Export Script |
+| VaeDecoderQuantizable | SA8255 (Proxy) | SA8255P Proxy | QNN | 274.28 ms | 0 - 3 MB | W8A16 | NPU | Use Export Script |
+| VaeDecoderQuantizable | SA8650 (Proxy) | SA8650P Proxy | QNN | 272.687 ms | 0 - 2 MB | W8A16 | NPU | Use Export Script |
+| VaeDecoderQuantizable | SA8775P ADP | SA8775P | QNN | 301.027 ms | 0 - 10 MB | W8A16 | NPU | Use Export Script |
+| VaeDecoderQuantizable | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 4462.005 ms | 1 - 10 MB | W8A16 | NPU | Use Export Script |
+| VaeDecoderQuantizable | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 259.311 ms | 0 - 3 MB | W8A16 | NPU | Use Export Script |
+| VaeDecoderQuantizable | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 301.027 ms | 0 - 10 MB | W8A16 | NPU | Use Export Script |
 
 
 
@@ -58,7 +79,7 @@ More details on model performance across various devices, can be found
 
 Install the package via pip:
 ```bash
-pip install "qai-hub-models[stable-diffusion-v2-1-quantized]"
+pip install "qai-hub-models[stable-diffusion-v2-1-quantized]" -f https://qaihub-public-python-wheels.s3.us-west-2.amazonaws.com/index.html
 ```
 
 
@@ -76,7 +97,7 @@ Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information.
 
 
 
-## Demo on-device
+## Demo off target
 
 The package contains a simple end-to-end demo that downloads pre-trained
 weights and runs this model on a sample input.
@@ -109,171 +130,34 @@ python -m qai_hub_models.models.stable_diffusion_v2_1_quantized.export
 ```
 Profiling Results
 ------------------------------------------------------------
-TextEncoder_Quantized
+TextEncoderQuantizable
 Device                          : Samsung Galaxy S23 (13)
 Runtime                         : QNN                    
-Estimated inference time (ms)   : 11.6                   
-Estimated peak memory usage (MB): [0, 1]                 
-Total # Ops                     : 1040                   
-Compute Unit(s)                 : NPU (1040 ops)         
+Estimated inference time (ms)   : 6.6                    
+Estimated peak memory usage (MB): [0, 2]                 
+Total # Ops                     : 787                    
+Compute Unit(s)                 : NPU (787 ops)          
 
 ------------------------------------------------------------
-VAEDecoder_Quantized
+UnetQuantizable
 Device                          : Samsung Galaxy S23 (13)
 Runtime                         : QNN                    
-Estimated inference time (ms)   : 217.1                  
-Estimated peak memory usage (MB): [0, 2]                 
-Total # Ops                     : 170                    
-Compute Unit(s)                 : NPU (170 ops)          
+Estimated inference time (ms)   : 97.8                   
+Estimated peak memory usage (MB): [0, 3]                 
+Total # Ops                     : 5891                   
+Compute Unit(s)                 : NPU (5891 ops)         
 
 ------------------------------------------------------------
-UNet_Quantized
+VaeDecoderQuantizable
 Device                          : Samsung Galaxy S23 (13)
 Runtime                         : QNN                    
-Estimated inference time (ms)   : 101.1                  
-Estimated peak memory usage (MB): [0, 2]                 
-Total # Ops                     : 6361                   
-Compute Unit(s)                 : NPU (6361 ops)         
+Estimated inference time (ms)   : 274.6                  
+Estimated peak memory usage (MB): [0, 4]                 
+Total # Ops                     : 189                    
+Compute Unit(s)                 : NPU (189 ops)          
 ```
 
 
-## How does this work?
-
-This [export script](https://aihub.qualcomm.com/models/stable_diffusion_v2_1_quantized/qai_hub_models/models/Stable-Diffusion-v2.1/export.py)
-leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
-on-device. Lets go through each step below in detail:
-
-Step 1: **Compile model for on-device deployment**
-
-To compile a PyTorch model for on-device deployment, we first trace the model
-in memory using the `jit.trace` and then call the `submit_compile_job` API.
-
-```python
-import torch
-
-import qai_hub as hub
-from qai_hub_models.models.stable_diffusion_v2_1_quantized import Model
-
-# Load the model
-model = Model.from_pretrained()
-text_encoder_model = model.text_encoder
-unet_model = model.unet
-vae_decoder_model = model.vae_decoder
-
-# Device
-device = hub.Device("Samsung Galaxy S23")
-
-# Trace model
-text_encoder_input_shape = text_encoder_model.get_input_spec()
-text_encoder_sample_inputs = text_encoder_model.sample_inputs()
-
-traced_text_encoder_model = torch.jit.trace(text_encoder_model, [torch.tensor(data[0]) for _, data in text_encoder_sample_inputs.items()])
-
-# Compile model on a specific device
-text_encoder_compile_job = hub.submit_compile_job(
-    model=traced_text_encoder_model ,
-    device=device,
-    input_specs=text_encoder_model.get_input_spec(),
-)
-
-# Get target model to run on-device
-text_encoder_target_model = text_encoder_compile_job.get_target_model()
-# Trace model
-unet_input_shape = unet_model.get_input_spec()
-unet_sample_inputs = unet_model.sample_inputs()
-
-traced_unet_model = torch.jit.trace(unet_model, [torch.tensor(data[0]) for _, data in unet_sample_inputs.items()])
-
-# Compile model on a specific device
-unet_compile_job = hub.submit_compile_job(
-    model=traced_unet_model ,
-    device=device,
-    input_specs=unet_model.get_input_spec(),
-)
-
-# Get target model to run on-device
-unet_target_model = unet_compile_job.get_target_model()
-# Trace model
-vae_decoder_input_shape = vae_decoder_model.get_input_spec()
-vae_decoder_sample_inputs = vae_decoder_model.sample_inputs()
-
-traced_vae_decoder_model = torch.jit.trace(vae_decoder_model, [torch.tensor(data[0]) for _, data in vae_decoder_sample_inputs.items()])
-
-# Compile model on a specific device
-vae_decoder_compile_job = hub.submit_compile_job(
-    model=traced_vae_decoder_model ,
-    device=device,
-    input_specs=vae_decoder_model.get_input_spec(),
-)
-
-# Get target model to run on-device
-vae_decoder_target_model = vae_decoder_compile_job.get_target_model()
-
-```
-
-
-Step 2: **Performance profiling on cloud-hosted device**
-
-After uploading compiled models from step 1. Models can be profiled model on-device using the
-`target_model`. Note that this scripts runs the model on a device automatically
-provisioned in the cloud.  Once the job is submitted, you can navigate to a
-provided job URL to view a variety of on-device performance metrics.
-```python
-
-# Device
-device = hub.Device("Samsung Galaxy S23")
-profile_job_textencoder_quantized = hub.submit_profile_job(
-    model=model_textencoder_quantized,
-    device=device,
-)
-profile_job_unet_quantized = hub.submit_profile_job(
-    model=model_unet_quantized,
-    device=device,
-)
-profile_job_vaedecoder_quantized = hub.submit_profile_job(
-    model=model_vaedecoder_quantized,
-    device=device,
-)
-
-```
-
-Step 3: **Verify on-device accuracy**
-
-To verify the accuracy of the model on-device, you can run on-device inference
-on sample input data on the same cloud hosted device.
-```python
-
-input_data_textencoder_quantized = model.text_encoder.sample_inputs()
-inference_job_textencoder_quantized = hub.submit_inference_job(
-    model=model_textencoder_quantized,
-    device=device,
-    inputs=input_data_textencoder_quantized,
-)
-on_device_output_textencoder_quantized = inference_job_textencoder_quantized.download_output_data()
-
-input_data_unet_quantized = model.unet.sample_inputs()
-inference_job_unet_quantized = hub.submit_inference_job(
-    model=model_unet_quantized,
-    device=device,
-    inputs=input_data_unet_quantized,
-)
-on_device_output_unet_quantized = inference_job_unet_quantized.download_output_data()
-
-input_data_vaedecoder_quantized = model.vae_decoder.sample_inputs()
-inference_job_vaedecoder_quantized = hub.submit_inference_job(
-    model=model_vaedecoder_quantized,
-    device=device,
-    inputs=input_data_vaedecoder_quantized,
-)
-on_device_output_vaedecoder_quantized = inference_job_vaedecoder_quantized.download_output_data()
-
-```
-With the output of the model, you can compute like PSNR, relative errors or
-spot check the output with expected output.
-
-**Note**: This on-device profiling and inference requires access to Qualcomm®
-AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
-
 
 
 
@@ -286,9 +170,9 @@ The models can be deployed using multiple runtimes:
   guide to deploy the .tflite model in an Android application.
 
 
-- QNN ( `.so` / `.bin` export ): This [sample
+- QNN (`.so` export ): This [sample
   app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
-provides instructions on how to use the `.so` shared library or `.bin` context binary in an Android application.
+provides instructions on how to use the `.so` shared library  in an Android application.
 
 
 ## View on Qualcomm® AI Hub
@@ -314,25 +198,3 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 * For questions or feedback please [reach out to us](mailto:[email protected]).
 
 
-## Usage and Limitations
-
-This model may not be used for or in connection with any of the following applications:
-
-- Accessing essential private and public services and benefits;
-- Administration of justice and democratic processes;
-- Assessing or recognizing the emotional state of a person;
-- Biometric and biometrics-based systems, including categorization of persons based on sensitive characteristics;
-- Education and vocational training;
-- Employment and workers management;
-- Exploitation of the vulnerabilities of persons resulting in harmful behavior;
-- General purpose social scoring;
-- Law enforcement;
-- Management and operation of critical infrastructure;
-- Migration, asylum and border control management;
-- Predictive policing;
-- Real-time remote biometric identification in public spaces;
-- Recommender systems of social media platforms;
-- Scraping of facial images (from the internet or otherwise); and/or
-- Subliminal manipulation
-
-