--- library_name: pytorch license: creativeml-openrail-m tags: - generative_ai - quantized - android pipeline_tag: unconditional-image-generation --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stable_diffusion_v1_5_w8a16_quantized/web-assets/model_demo.png) # Stable-Diffusion-v1.5: Optimized for Mobile Deployment ## State-of-the-art generative AI model used to generate detailed images conditioned on text descriptions Generates high resolution 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 Stable-Diffusion-v1.5 found [here](https://github.com/CompVis/stable-diffusion/tree/main). This repository provides scripts to run Stable-Diffusion-v1.5 on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/stable_diffusion_v1_5_w8a16_quantized). ### Model Details - **Model Type:** Image generation - **Model Stats:** - Input: Text prompt to generate image - QNN-SDK: 2.28 - 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 |---|---|---|---|---|---|---|---|---| | TextEncoderQuantizable | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 4.548 ms | 0 - 3 MB | W8A16 | NPU | [Stable-Diffusion-v1.5.so](https://huggingface.co/qualcomm/Stable-Diffusion-v1.5/blob/main/TextEncoderQuantizable.so) | | TextEncoderQuantizable | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 3.347 ms | 0 - 21 MB | W8A16 | NPU | [Stable-Diffusion-v1.5.so](https://huggingface.co/qualcomm/Stable-Diffusion-v1.5/blob/main/TextEncoderQuantizable.so) | | TextEncoderQuantizable | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 3.053 ms | 0 - 14 MB | W8A16 | NPU | Use Export Script | | TextEncoderQuantizable | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 4.901 ms | 0 - 0 MB | W8A16 | NPU | Use Export Script | | TextEncoderQuantizable | SA7255P ADP | SA7255P | QNN | 30.749 ms | 0 - 9 MB | W8A16 | NPU | Use Export Script | | TextEncoderQuantizable | SA8255 (Proxy) | SA8255P Proxy | QNN | 4.585 ms | 0 - 3 MB | W8A16 | NPU | Use Export Script | | TextEncoderQuantizable | SA8650 (Proxy) | SA8650P Proxy | QNN | 4.548 ms | 0 - 2 MB | W8A16 | NPU | Use Export Script | | TextEncoderQuantizable | SA8775P ADP | SA8775P | QNN | 5.874 ms | 0 - 10 MB | W8A16 | NPU | Use Export Script | | TextEncoderQuantizable | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 30.749 ms | 0 - 9 MB | W8A16 | NPU | Use Export Script | | TextEncoderQuantizable | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 4.581 ms | 0 - 2 MB | W8A16 | NPU | Use Export Script | | TextEncoderQuantizable | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 5.874 ms | 0 - 10 MB | W8A16 | NPU | Use Export Script | | UnetQuantizable | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 114.402 ms | 0 - 2 MB | W8A16 | NPU | [Stable-Diffusion-v1.5.so](https://huggingface.co/qualcomm/Stable-Diffusion-v1.5/blob/main/UnetQuantizable.so) | | UnetQuantizable | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 81.02 ms | 0 - 18 MB | W8A16 | NPU | [Stable-Diffusion-v1.5.so](https://huggingface.co/qualcomm/Stable-Diffusion-v1.5/blob/main/UnetQuantizable.so) | | UnetQuantizable | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 61.556 ms | 0 - 15 MB | W8A16 | NPU | Use Export Script | | UnetQuantizable | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 116.85 ms | 0 - 0 MB | W8A16 | NPU | Use Export Script | | UnetQuantizable | SA7255P ADP | SA7255P | QNN | 1587.305 ms | 0 - 8 MB | W8A16 | NPU | Use Export Script | | UnetQuantizable | SA8255 (Proxy) | SA8255P Proxy | QNN | 114.391 ms | 0 - 2 MB | W8A16 | NPU | Use Export Script | | UnetQuantizable | SA8650 (Proxy) | SA8650P Proxy | QNN | 114.482 ms | 0 - 3 MB | W8A16 | NPU | Use Export Script | | UnetQuantizable | SA8775P ADP | SA8775P | QNN | 131.759 ms | 0 - 8 MB | W8A16 | NPU | Use Export Script | | UnetQuantizable | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 1587.305 ms | 0 - 8 MB | W8A16 | NPU | Use Export Script | | UnetQuantizable | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 114.914 ms | 0 - 3 MB | W8A16 | NPU | Use Export Script | | UnetQuantizable | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 131.759 ms | 0 - 8 MB | W8A16 | NPU | Use Export Script | | VaeDecoderQuantizable | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 296.059 ms | 0 - 70 MB | W8A16 | NPU | [Stable-Diffusion-v1.5.so](https://huggingface.co/qualcomm/Stable-Diffusion-v1.5/blob/main/VaeDecoderQuantizable.so) | | VaeDecoderQuantizable | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 230.689 ms | 0 - 315 MB | W8A16 | NPU | [Stable-Diffusion-v1.5.so](https://huggingface.co/qualcomm/Stable-Diffusion-v1.5/blob/main/VaeDecoderQuantizable.so) | | VaeDecoderQuantizable | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 270.158 ms | 0 - 0 MB | W8A16 | NPU | Use Export Script | | VaeDecoderQuantizable | SA7255P ADP | SA7255P | QNN | 4461.686 ms | 0 - 9 MB | W8A16 | NPU | Use Export Script | | VaeDecoderQuantizable | SA8255 (Proxy) | SA8255P Proxy | QNN | 286.526 ms | 0 - 4 MB | W8A16 | NPU | Use Export Script | | VaeDecoderQuantizable | SA8650 (Proxy) | SA8650P Proxy | QNN | 286.182 ms | 0 - 3 MB | W8A16 | NPU | Use Export Script | | VaeDecoderQuantizable | SA8775P ADP | SA8775P | QNN | 301.173 ms | 0 - 10 MB | W8A16 | NPU | Use Export Script | | VaeDecoderQuantizable | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 4461.686 ms | 0 - 9 MB | W8A16 | NPU | Use Export Script | | VaeDecoderQuantizable | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 281.374 ms | 0 - 3 MB | W8A16 | NPU | Use Export Script | | VaeDecoderQuantizable | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 301.173 ms | 0 - 10 MB | W8A16 | NPU | Use Export Script | ## Installation Install the package via pip: ```bash pip install "qai-hub-models[stable-diffusion-v1-5-w8a16-quantized]" -f https://qaihub-public-python-wheels.s3.us-west-2.amazonaws.com/index.html ``` ## 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 off target 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.stable_diffusion_v1_5_w8a16_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.stable_diffusion_v1_5_w8a16_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.stable_diffusion_v1_5_w8a16_quantized.export ``` ``` Profiling Results ------------------------------------------------------------ TextEncoderQuantizable Device : Samsung Galaxy S23 (13) Runtime : QNN Estimated inference time (ms) : 4.5 Estimated peak memory usage (MB): [0, 3] Total # Ops : 437 Compute Unit(s) : NPU (437 ops) ------------------------------------------------------------ UnetQuantizable Device : Samsung Galaxy S23 (13) Runtime : QNN Estimated inference time (ms) : 114.4 Estimated peak memory usage (MB): [0, 2] Total # Ops : 4149 Compute Unit(s) : NPU (4149 ops) ------------------------------------------------------------ VaeDecoderQuantizable Device : Samsung Galaxy S23 (13) Runtime : QNN Estimated inference time (ms) : 296.1 Estimated peak memory usage (MB): [0, 70] Total # Ops : 189 Compute Unit(s) : NPU (189 ops) ``` ## 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` 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 in an Android application. ## View on Qualcomm® AI Hub Get more details on Stable-Diffusion-v1.5's performance across various devices [here](https://aihub.qualcomm.com/models/stable_diffusion_v1_5_w8a16_quantized). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License * The license for the original implementation of Stable-Diffusion-v1.5 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).