--- library_name: pytorch license: other tags: - generative_ai - android pipeline_tag: unconditional-image-generation --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stable_diffusion_v2_1_quantized/web-assets/model_demo.png) # Stable-Diffusion-v2.1: 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-v2.1 found [here](https://github.com/CompVis/stable-diffusion/tree/main). This repository provides scripts to run Stable-Diffusion-v2.1 on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/stable_diffusion_v2_1_quantized). ### Model Details - **Model Type:** Model_use_case.image_generation - **Model Stats:** - Input: Text prompt to generate image - Text Encoder Number of parameters: 340M - UNet Number of parameters: 865M - VAE Decoder Number of parameters: 83M - Model size: 1GB | Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model |---|---|---|---|---|---|---|---|---| | TextEncoderQuantizable | w8a16 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN | 15.92 ms | 0 - 9 MB | NPU | Use Export Script | | TextEncoderQuantizable | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN | 6.594 ms | 0 - 2 MB | NPU | Use Export Script | | TextEncoderQuantizable | w8a16 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN | 6.814 ms | 0 - 9 MB | NPU | Use Export Script | | TextEncoderQuantizable | w8a16 | SA7255P ADP | Qualcomm® SA7255P | QNN | 15.92 ms | 0 - 9 MB | NPU | Use Export Script | | TextEncoderQuantizable | w8a16 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN | 6.634 ms | 0 - 3 MB | NPU | Use Export Script | | TextEncoderQuantizable | w8a16 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN | 6.813 ms | 0 - 2 MB | NPU | Use Export Script | | TextEncoderQuantizable | w8a16 | SA8775P ADP | Qualcomm® SA8775P | QNN | 6.814 ms | 0 - 9 MB | NPU | Use Export Script | | TextEncoderQuantizable | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN | 6.632 ms | 0 - 3 MB | NPU | Use Export Script | | TextEncoderQuantizable | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 6.859 ms | 0 - 386 MB | NPU | [Stable-Diffusion-v2.1.onnx](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/Stable-Diffusion-v2.1_w8a16.onnx) | | TextEncoderQuantizable | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN | 4.62 ms | 0 - 19 MB | NPU | Use Export Script | | TextEncoderQuantizable | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 5.075 ms | 0 - 14 MB | NPU | [Stable-Diffusion-v2.1.onnx](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/Stable-Diffusion-v2.1_w8a16.onnx) | | TextEncoderQuantizable | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN | 4.173 ms | 0 - 14 MB | NPU | Use Export Script | | TextEncoderQuantizable | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 4.499 ms | 0 - 13 MB | NPU | [Stable-Diffusion-v2.1.onnx](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/Stable-Diffusion-v2.1_w8a16.onnx) | | TextEncoderQuantizable | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 6.834 ms | 0 - 0 MB | NPU | Use Export Script | | TextEncoderQuantizable | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 6.823 ms | 378 - 378 MB | NPU | [Stable-Diffusion-v2.1.onnx](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/Stable-Diffusion-v2.1_w8a16.onnx) | | UnetQuantizable | w8a16 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN | 241.358 ms | 0 - 8 MB | NPU | Use Export Script | | UnetQuantizable | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN | 97.192 ms | 0 - 3 MB | NPU | Use Export Script | | UnetQuantizable | w8a16 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN | 92.111 ms | 0 - 8 MB | NPU | Use Export Script | | UnetQuantizable | w8a16 | SA7255P ADP | Qualcomm® SA7255P | QNN | 241.358 ms | 0 - 8 MB | NPU | Use Export Script | | UnetQuantizable | w8a16 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN | 97.903 ms | 0 - 3 MB | NPU | Use Export Script | | UnetQuantizable | w8a16 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN | 96.775 ms | 0 - 2 MB | NPU | Use Export Script | | UnetQuantizable | w8a16 | SA8775P ADP | Qualcomm® SA8775P | QNN | 92.111 ms | 0 - 8 MB | NPU | Use Export Script | | UnetQuantizable | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN | 97.437 ms | 0 - 5 MB | NPU | Use Export Script | | UnetQuantizable | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 98.551 ms | 0 - 3 MB | NPU | [Stable-Diffusion-v2.1.onnx](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/Stable-Diffusion-v2.1_w8a16.onnx) | | UnetQuantizable | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN | 69.198 ms | 0 - 19 MB | NPU | Use Export Script | | UnetQuantizable | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 69.745 ms | 0 - 17 MB | NPU | [Stable-Diffusion-v2.1.onnx](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/Stable-Diffusion-v2.1_w8a16.onnx) | | UnetQuantizable | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN | 61.688 ms | 0 - 14 MB | NPU | Use Export Script | | UnetQuantizable | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 62.663 ms | 0 - 14 MB | NPU | [Stable-Diffusion-v2.1.onnx](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/Stable-Diffusion-v2.1_w8a16.onnx) | | UnetQuantizable | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 98.945 ms | 0 - 0 MB | NPU | Use Export Script | | UnetQuantizable | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 99.538 ms | 842 - 842 MB | NPU | [Stable-Diffusion-v2.1.onnx](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/Stable-Diffusion-v2.1_w8a16.onnx) | | VaeDecoderQuantizable | w8a16 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN | 720.835 ms | 1 - 10 MB | NPU | Use Export Script | | VaeDecoderQuantizable | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN | 270.663 ms | 0 - 3 MB | NPU | Use Export Script | | VaeDecoderQuantizable | w8a16 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN | 250.403 ms | 0 - 12 MB | NPU | Use Export Script | | VaeDecoderQuantizable | w8a16 | SA7255P ADP | Qualcomm® SA7255P | QNN | 720.835 ms | 1 - 10 MB | NPU | Use Export Script | | VaeDecoderQuantizable | w8a16 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN | 271.924 ms | 0 - 3 MB | NPU | Use Export Script | | VaeDecoderQuantizable | w8a16 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN | 274.917 ms | 0 - 2 MB | NPU | Use Export Script | | VaeDecoderQuantizable | w8a16 | SA8775P ADP | Qualcomm® SA8775P | QNN | 250.403 ms | 0 - 12 MB | NPU | Use Export Script | | VaeDecoderQuantizable | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN | 269.888 ms | 0 - 3 MB | NPU | Use Export Script | | VaeDecoderQuantizable | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 269.758 ms | 0 - 66 MB | NPU | [Stable-Diffusion-v2.1.onnx](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/Stable-Diffusion-v2.1_w8a16.onnx) | | VaeDecoderQuantizable | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN | 203.298 ms | 0 - 20 MB | NPU | Use Export Script | | VaeDecoderQuantizable | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 204.412 ms | 3 - 22 MB | NPU | [Stable-Diffusion-v2.1.onnx](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/Stable-Diffusion-v2.1_w8a16.onnx) | | VaeDecoderQuantizable | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN | 193.019 ms | 0 - 15 MB | NPU | Use Export Script | | VaeDecoderQuantizable | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 188.783 ms | 3 - 17 MB | NPU | [Stable-Diffusion-v2.1.onnx](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/Stable-Diffusion-v2.1_w8a16.onnx) | | VaeDecoderQuantizable | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 266.283 ms | 0 - 0 MB | NPU | Use Export Script | | VaeDecoderQuantizable | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 266.925 ms | 63 - 63 MB | NPU | [Stable-Diffusion-v2.1.onnx](https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/blob/main/Stable-Diffusion-v2.1_w8a16.onnx) | ## Installation Install the package via pip: ```bash pip install "qai-hub-models[stable-diffusion-v2-1-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 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_v2_1_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_v2_1_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_v2_1_quantized.export ``` ``` Profiling Results ------------------------------------------------------------ TextEncoderQuantizable Device : cs_8275 (ANDROID 14) Runtime : QNN Estimated inference time (ms) : 15.9 Estimated peak memory usage (MB): [0, 9] Total # Ops : 971 Compute Unit(s) : npu (971 ops) gpu (0 ops) cpu (0 ops) ------------------------------------------------------------ UnetQuantizable Device : cs_8275 (ANDROID 14) Runtime : QNN Estimated inference time (ms) : 241.4 Estimated peak memory usage (MB): [0, 8] Total # Ops : 5783 Compute Unit(s) : npu (5783 ops) gpu (0 ops) cpu (0 ops) ------------------------------------------------------------ VaeDecoderQuantizable Device : cs_8275 (ANDROID 14) Runtime : QNN Estimated inference time (ms) : 720.8 Estimated peak memory usage (MB): [1, 10] Total # Ops : 189 Compute Unit(s) : npu (189 ops) gpu (0 ops) cpu (0 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-v2.1's performance across various devices [here](https://aihub.qualcomm.com/models/stable_diffusion_v2_1_quantized). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License * The license for the original implementation of Stable-Diffusion-v2.1 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).