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--- |
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library_name: pytorch |
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license: creativeml-openrail-m |
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tags: |
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- generative_ai |
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- quantized |
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- android |
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pipeline_tag: unconditional-image-generation |
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--- |
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# Riffusion: Optimized for Mobile Deployment |
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## State-of-the-art generative AI model used to generate spectrogram images given any text input. These spectrograms can be converted into audio clips |
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Generates high resolution spectrograms images from text prompts using a latent diffusion model. This model uses CLIP ViT-L/14 as text encoder, U-Net based latent denoising, and VAE based decoder to generate the final image. |
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This model is an implementation of Riffusion found [here](https://github.com/CompVis/stable-diffusion/tree/main). |
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This repository provides scripts to run Riffusion 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/riffusion_quantized). |
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### Model Details |
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- **Model Type:** Image generation |
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- **Model Stats:** |
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- Input: Text prompt to generate spectrogram image |
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- Text Encoder Number of parameters: 340M |
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- UNet Number of parameters: 865M |
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- VAE Decoder Number of parameters: 83M |
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- Model size: 1GB |
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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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| TextEncoder_Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 7.045 ms | 0 - 67 MB | INT8 | NPU | [Riffusion.bin](https://huggingface.co/qualcomm/Riffusion/blob/main/TextEncoder_Quantized.bin) | |
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| TextEncoder_Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 4.789 ms | 0 - 161 MB | INT8 | NPU | [Riffusion.bin](https://huggingface.co/qualcomm/Riffusion/blob/main/TextEncoder_Quantized.bin) | |
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| TextEncoder_Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 6.715 ms | 0 - 1 MB | UINT16 | NPU | Use Export Script | |
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| TextEncoder_Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 7.594 ms | 0 - 0 MB | INT8 | NPU | Use Export Script | |
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| VAEDecoder_Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 233.844 ms | 0 - 46 MB | INT8 | NPU | [Riffusion.bin](https://huggingface.co/qualcomm/Riffusion/blob/main/VAEDecoder_Quantized.bin) | |
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| VAEDecoder_Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 175.734 ms | 0 - 64 MB | INT8 | NPU | [Riffusion.bin](https://huggingface.co/qualcomm/Riffusion/blob/main/VAEDecoder_Quantized.bin) | |
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| VAEDecoder_Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 239.643 ms | 0 - 1 MB | UINT16 | NPU | Use Export Script | |
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| VAEDecoder_Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 227.581 ms | 0 - 0 MB | INT8 | NPU | Use Export Script | |
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| UNet_Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 127.531 ms | 0 - 13 MB | INT8 | NPU | [Riffusion.bin](https://huggingface.co/qualcomm/Riffusion/blob/main/UNet_Quantized.bin) | |
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| UNet_Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 90.167 ms | 0 - 1750 MB | INT8 | NPU | [Riffusion.bin](https://huggingface.co/qualcomm/Riffusion/blob/main/UNet_Quantized.bin) | |
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| UNet_Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 128.206 ms | 1 - 2 MB | UINT16 | NPU | Use Export Script | |
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| UNet_Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 129.856 ms | 0 - 0 MB | INT8 | NPU | Use Export Script | |
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## Installation |
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Install the package via pip: |
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```bash |
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pip install "qai-hub-models[riffusion-quantized]" |
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``` |
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## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device |
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Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your |
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Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`. |
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With this API token, you can configure your client to run models on the cloud |
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hosted devices. |
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```bash |
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qai-hub configure --api_token API_TOKEN |
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``` |
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Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information. |
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## Demo on-device |
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The package contains a simple end-to-end demo that downloads pre-trained |
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weights and runs this model on a sample input. |
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```bash |
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python -m qai_hub_models.models.riffusion_quantized.demo |
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``` |
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The above demo runs a reference implementation of pre-processing, model |
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inference, and post processing. |
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**NOTE**: If you want running in a Jupyter Notebook or Google Colab like |
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environment, please add the following to your cell (instead of the above). |
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``` |
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%run -m qai_hub_models.models.riffusion_quantized.demo |
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``` |
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### Run model on a cloud-hosted device |
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In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® |
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device. This script does the following: |
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* Performance check on-device on a cloud-hosted device |
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* Downloads compiled assets that can be deployed on-device for Android. |
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* Accuracy check between PyTorch and on-device outputs. |
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```bash |
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python -m qai_hub_models.models.riffusion_quantized.export |
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``` |
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``` |
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Profiling Results |
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------------------------------------------------------------ |
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TextEncoder_Quantized |
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Device : Samsung Galaxy S23 (13) |
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Runtime : QNN |
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Estimated inference time (ms) : 7.0 |
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Estimated peak memory usage (MB): [0, 67] |
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Total # Ops : 569 |
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Compute Unit(s) : NPU (569 ops) |
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------------------------------------------------------------ |
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VAEDecoder_Quantized |
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Device : Samsung Galaxy S23 (13) |
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Runtime : QNN |
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Estimated inference time (ms) : 233.8 |
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Estimated peak memory usage (MB): [0, 46] |
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Total # Ops : 170 |
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Compute Unit(s) : NPU (170 ops) |
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------------------------------------------------------------ |
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UNet_Quantized |
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Device : Samsung Galaxy S23 (13) |
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Runtime : QNN |
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Estimated inference time (ms) : 127.5 |
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Estimated peak memory usage (MB): [0, 13] |
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Total # Ops : 4933 |
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Compute Unit(s) : NPU (4933 ops) |
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``` |
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## How does this work? |
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This [export script](https://aihub.qualcomm.com/models/riffusion_quantized/qai_hub_models/models/Riffusion/export.py) |
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leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model |
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on-device. Lets go through each step below in detail: |
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Step 1: **Compile model for on-device deployment** |
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To compile a PyTorch model for on-device deployment, we first trace the model |
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in memory using the `jit.trace` and then call the `submit_compile_job` API. |
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```python |
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import torch |
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import qai_hub as hub |
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from qai_hub_models.models.riffusion_quantized import Model |
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# Load the model |
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model = Model.from_pretrained() |
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text_encoder_model = model.text_encoder |
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unet_model = model.unet |
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vae_decoder_model = model.vae_decoder |
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# Device |
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device = hub.Device("Samsung Galaxy S23") |
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# Trace model |
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text_encoder_input_shape = text_encoder_model.get_input_spec() |
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text_encoder_sample_inputs = text_encoder_model.sample_inputs() |
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traced_text_encoder_model = torch.jit.trace(text_encoder_model, [torch.tensor(data[0]) for _, data in text_encoder_sample_inputs.items()]) |
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# Compile model on a specific device |
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text_encoder_compile_job = hub.submit_compile_job( |
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model=traced_text_encoder_model , |
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device=device, |
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input_specs=text_encoder_model.get_input_spec(), |
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) |
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# Get target model to run on-device |
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text_encoder_target_model = text_encoder_compile_job.get_target_model() |
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# Trace model |
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unet_input_shape = unet_model.get_input_spec() |
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unet_sample_inputs = unet_model.sample_inputs() |
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traced_unet_model = torch.jit.trace(unet_model, [torch.tensor(data[0]) for _, data in unet_sample_inputs.items()]) |
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# Compile model on a specific device |
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unet_compile_job = hub.submit_compile_job( |
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model=traced_unet_model , |
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device=device, |
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input_specs=unet_model.get_input_spec(), |
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) |
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# Get target model to run on-device |
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unet_target_model = unet_compile_job.get_target_model() |
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# Trace model |
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vae_decoder_input_shape = vae_decoder_model.get_input_spec() |
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vae_decoder_sample_inputs = vae_decoder_model.sample_inputs() |
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traced_vae_decoder_model = torch.jit.trace(vae_decoder_model, [torch.tensor(data[0]) for _, data in vae_decoder_sample_inputs.items()]) |
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# Compile model on a specific device |
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vae_decoder_compile_job = hub.submit_compile_job( |
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model=traced_vae_decoder_model , |
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device=device, |
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input_specs=vae_decoder_model.get_input_spec(), |
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) |
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# Get target model to run on-device |
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vae_decoder_target_model = vae_decoder_compile_job.get_target_model() |
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``` |
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Step 2: **Performance profiling on cloud-hosted device** |
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After uploading compiled models from step 1. Models can be profiled model on-device using the |
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`target_model`. Note that this scripts runs the model on a device automatically |
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provisioned in the cloud. Once the job is submitted, you can navigate to a |
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provided job URL to view a variety of on-device performance metrics. |
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```python |
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# Device |
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device = hub.Device("Samsung Galaxy S23") |
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profile_job_textencoder_quantized = hub.submit_profile_job( |
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model=model_textencoder_quantized, |
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device=device, |
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) |
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profile_job_unet_quantized = hub.submit_profile_job( |
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model=model_unet_quantized, |
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device=device, |
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) |
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profile_job_vaedecoder_quantized = hub.submit_profile_job( |
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model=model_vaedecoder_quantized, |
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device=device, |
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) |
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``` |
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Step 3: **Verify on-device accuracy** |
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To verify the accuracy of the model on-device, you can run on-device inference |
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on sample input data on the same cloud hosted device. |
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```python |
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input_data_textencoder_quantized = model.text_encoder.sample_inputs() |
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inference_job_textencoder_quantized = hub.submit_inference_job( |
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model=model_textencoder_quantized, |
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device=device, |
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inputs=input_data_textencoder_quantized, |
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) |
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on_device_output_textencoder_quantized = inference_job_textencoder_quantized.download_output_data() |
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input_data_unet_quantized = model.unet.sample_inputs() |
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inference_job_unet_quantized = hub.submit_inference_job( |
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model=model_unet_quantized, |
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device=device, |
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inputs=input_data_unet_quantized, |
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) |
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on_device_output_unet_quantized = inference_job_unet_quantized.download_output_data() |
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input_data_vaedecoder_quantized = model.vae_decoder.sample_inputs() |
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inference_job_vaedecoder_quantized = hub.submit_inference_job( |
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model=model_vaedecoder_quantized, |
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device=device, |
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inputs=input_data_vaedecoder_quantized, |
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) |
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on_device_output_vaedecoder_quantized = inference_job_vaedecoder_quantized.download_output_data() |
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``` |
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With the output of the model, you can compute like PSNR, relative errors or |
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spot check the output with expected output. |
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**Note**: This on-device profiling and inference requires access to Qualcomm® |
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AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup). |
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## Deploying compiled model to Android |
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The models can be deployed using multiple runtimes: |
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- TensorFlow Lite (`.tflite` export): [This |
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tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a |
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guide to deploy the .tflite model in an Android application. |
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- QNN ( `.so` / `.bin` export ): This [sample |
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app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html) |
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provides instructions on how to use the `.so` shared library or `.bin` context binary in an Android application. |
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## View on Qualcomm® AI Hub |
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Get more details on Riffusion's performance across various devices [here](https://aihub.qualcomm.com/models/riffusion_quantized). |
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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 Riffusion can be found |
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[here](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE). |
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* The license for the compiled assets for on-device deployment can be found [here](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE) |
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## References |
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* [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) |
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* [Source Model Implementation](https://github.com/CompVis/stable-diffusion/tree/main) |
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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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## Usage and Limitations |
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This model may not be used for or in connection with any of the following applications: |
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- Accessing essential private and public services and benefits; |
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- Administration of justice and democratic processes; |
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- Assessing or recognizing the emotional state of a person; |
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- Biometric and biometrics-based systems, including categorization of persons based on sensitive characteristics; |
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- Education and vocational training; |
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- Employment and workers management; |
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- Exploitation of the vulnerabilities of persons resulting in harmful behavior; |
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- General purpose social scoring; |
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- Law enforcement; |
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- Management and operation of critical infrastructure; |
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- Migration, asylum and border control management; |
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- Predictive policing; |
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- Real-time remote biometric identification in public spaces; |
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- Recommender systems of social media platforms; |
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- Scraping of facial images (from the internet or otherwise); and/or |
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- Subliminal manipulation |
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