--- library_name: pytorch license: creativeml-openrail-m pipeline_tag: unconditional-image-generation tags: - generative_ai - quantized - android --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/riffusion_quantized/web-assets/model_demo.png) # Riffusion: Optimized for Mobile Deployment ## State-of-the-art generative AI model used to generate spectrogram images given any text input. These spectrograms can be converted into audio clips 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. This model is an implementation of Riffusion found [here](https://github.com/CompVis/stable-diffusion/tree/main). This repository provides scripts to run Riffusion on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/riffusion_quantized). ### Model Details - **Model Type:** Image generation - **Model Stats:** - Input: Text prompt to generate spectrogram image - Text Encoder Number of parameters: 340M - UNet Number of parameters: 865M - VAE Decoder Number of parameters: 83M - Model size: 1GB | 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 | 7.045 ms | 0 - 67 MB | INT8 | NPU | [Riffusion.bin](https://huggingface.co/qualcomm/Riffusion/blob/main/TextEncoder_Quantized.bin) | | 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) | | TextEncoder_Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 6.715 ms | 0 - 1 MB | UINT16 | NPU | Use Export Script | | TextEncoder_Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 7.594 ms | 0 - 0 MB | INT8 | NPU | Use Export Script | | 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) | | 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) | | VAEDecoder_Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 239.643 ms | 0 - 1 MB | UINT16 | NPU | Use Export Script | | VAEDecoder_Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 227.581 ms | 0 - 0 MB | INT8 | NPU | Use Export Script | | 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) | | 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) | | UNet_Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 128.206 ms | 1 - 2 MB | UINT16 | NPU | Use Export Script | | UNet_Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 129.856 ms | 0 - 0 MB | INT8 | NPU | Use Export Script | ## Installation This model can be installed as a Python package via pip. ```bash pip install "qai-hub-models[riffusion_quantized]" ``` ## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`. With this API token, you can configure your client to run models on the cloud hosted devices. ```bash qai-hub configure --api_token API_TOKEN ``` Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information. ## Demo on-device 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.riffusion_quantized.demo ``` The above demo runs a reference implementation of pre-processing, model 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.riffusion_quantized.demo ``` ### Run model on a cloud-hosted device In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following: * Performance check on-device on a cloud-hosted device * Downloads compiled assets that can be deployed on-device for Android. * Accuracy check between PyTorch and on-device outputs. ```bash python -m qai_hub_models.models.riffusion_quantized.export ``` ``` Profiling Results ------------------------------------------------------------ TextEncoder_Quantized Device : Samsung Galaxy S23 (13) Runtime : QNN Estimated inference time (ms) : 7.0 Estimated peak memory usage (MB): [0, 67] Total # Ops : 569 Compute Unit(s) : NPU (569 ops) ------------------------------------------------------------ VAEDecoder_Quantized Device : Samsung Galaxy S23 (13) Runtime : QNN Estimated inference time (ms) : 233.8 Estimated peak memory usage (MB): [0, 46] Total # Ops : 170 Compute Unit(s) : NPU (170 ops) ------------------------------------------------------------ UNet_Quantized Device : Samsung Galaxy S23 (13) Runtime : QNN Estimated inference time (ms) : 127.5 Estimated peak memory usage (MB): [0, 13] Total # Ops : 4933 Compute Unit(s) : NPU (4933 ops) ``` ## How does this work? This [export script](https://aihub.qualcomm.com/models/riffusion_quantized/qai_hub_models/models/Riffusion/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.riffusion_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). ## Deploying compiled model to Android The models can be deployed using multiple runtimes: - TensorFlow Lite (`.tflite` export): [This tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a guide to deploy the .tflite model in an Android application. - QNN ( `.so` / `.bin` 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. ## View on Qualcomm® AI Hub Get more details on Riffusion's performance across various devices [here](https://aihub.qualcomm.com/models/riffusion_quantized). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License * The license for the original implementation of Riffusion can be found [here](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE). * The license for the compiled assets for on-device deployment can be found [here](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE) ## References * [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) * [Source Model Implementation](https://github.com/CompVis/stable-diffusion/tree/main) ## 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:ai-hub-support@qti.qualcomm.com). ## Usage and Limitations 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