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README.md

@@ -9,7 +9,7 @@ pipeline_tag: unconditional-image-generation
 
 ---
 
-![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/controlnet_quantized/web-assets/model_demo.png)
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/controlnet/web-assets/model_demo.png)
 
 # ControlNet: Optimized for Mobile Deployment
 ## Generating visual arts from text prompt and input guiding image
@@ -22,7 +22,7 @@ This model is an implementation of ControlNet found [here](https://github.com/ll
 
 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).
+[here](https://aihub.qualcomm.com/models/controlnet).
 
 
 ### Model Details
@@ -60,7 +60,7 @@ More details on model performance across various devices, can be found
 
 Install the package via pip:
 ```bash
-pip install "qai-hub-models[controlnet-quantized]"
+pip install "qai-hub-models[controlnet]"
 ```
 
 
@@ -84,7 +84,7 @@ The package contains a simple end-to-end demo that downloads pre-trained
 weights and runs this model on a sample input.
 
 ```bash
-python -m qai_hub_models.models.controlnet_quantized.demo
+python -m qai_hub_models.models.controlnet.demo
 ```
 
 The above demo runs a reference implementation of pre-processing, model
@@ -93,7 +93,7 @@ inference, and post processing.
 **NOTE**: If you want running in a Jupyter Notebook or Google Colab like
 environment, please add the following to your cell (instead of the above).
 ```
-%run -m qai_hub_models.models.controlnet_quantized.demo
+%run -m qai_hub_models.models.controlnet.demo
 ```
 
 
@@ -106,7 +106,7 @@ device. This script does the following:
 * Accuracy check between PyTorch and on-device outputs.
 
 ```bash
-python -m qai_hub_models.models.controlnet_quantized.export
+python -m qai_hub_models.models.controlnet.export
 ```
 ```
 Profiling Results
@@ -150,91 +150,21 @@ Compute Unit(s)                 : NPU (2406 ops)
 
 ## How does this work?
 
-This [export script](https://aihub.qualcomm.com/models/controlnet_quantized/qai_hub_models/models/ControlNet/export.py)
+This [export script](https://aihub.qualcomm.com/models/controlnet/qai_hub_models/models/ControlNet/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.
+Step 1: **Upload compiled model**
 
+Upload compiled models from `qai_hub_models.models.controlnet` on hub.
 ```python
 import torch
 
 import qai_hub as hub
-from qai_hub_models.models.controlnet_quantized import Model
+from qai_hub_models.models.controlnet import Model
 
 # Load the model
-model = Model.from_pretrained()
-controlnet_model = model.controlnet
-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
-controlnet_input_shape = controlnet_model.get_input_spec()
-controlnet_sample_inputs = controlnet_model.sample_inputs()
-
-traced_controlnet_model = torch.jit.trace(controlnet_model, [torch.tensor(data[0]) for _, data in controlnet_sample_inputs.items()])
-
-# Compile model on a specific device
-controlnet_compile_job = hub.submit_compile_job(
-    model=traced_controlnet_model ,
-    device=device,
-    input_specs=controlnet_model.get_input_spec(),
-)
-
-# Get target model to run on-device
-controlnet_target_model = controlnet_compile_job.get_target_model()
-# 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()
+model = Model.from_precompiled()
 
 ```
 
@@ -249,22 +179,6 @@ provided job URL to view a variety of on-device performance metrics.
 
 # Device
 device = hub.Device("Samsung Galaxy S23")
-profile_job_controlnet_quantized = hub.submit_profile_job(
-    model=model_controlnet_quantized,
-    device=device,
-)
-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,
-)
 
 ```
 
@@ -274,38 +188,6 @@ 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_controlnet_quantized = model.controlnet.sample_inputs()
-inference_job_controlnet_quantized = hub.submit_inference_job(
-    model=model_controlnet_quantized,
-    device=device,
-    inputs=input_data_controlnet_quantized,
-)
-on_device_output_controlnet_quantized = inference_job_controlnet_quantized.download_output_data()
-
-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.
@@ -331,7 +213,7 @@ provides instructions on how to use the `.so` shared library or `.bin` context b
 
 
 ## View on Qualcomm® AI Hub
-Get more details on ControlNet's performance across various devices [here](https://aihub.qualcomm.com/models/controlnet_quantized).
+Get more details on ControlNet's performance across various devices [here](https://aihub.qualcomm.com/models/controlnet).
 Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)