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Stable-Diffusion-v1.5

Unconditional Image Generation
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metadata
library_name: pytorch
license: other
tags:
  - generative_ai
  - android
pipeline_tag: unconditional-image-generation

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.

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.

Model Details

  • Model Type: Model_use_case.image_generation
  • Model Stats:
    • Input: Text prompt to generate image
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
text_encoder w8a16 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) PRECOMPILED_QNN_ONNX 5.484 ms 0 - 162 MB NPU Use Export Script
text_encoder w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile PRECOMPILED_QNN_ONNX 3.945 ms 0 - 22 MB NPU Use Export Script
text_encoder w8a16 Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile PRECOMPILED_QNN_ONNX 3.106 ms 0 - 11 MB NPU Use Export Script
text_encoder w8a16 Snapdragon 7 Gen 4 QRD Snapdragon® 7 Gen 4 Mobile PRECOMPILED_QNN_ONNX 5.757 ms 0 - 14 MB NPU Use Export Script
text_encoder w8a16 Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile PRECOMPILED_QNN_ONNX 2.619 ms 0 - 10 MB NPU Use Export Script
text_encoder w8a16 Snapdragon X Elite CRD Snapdragon® X Elite PRECOMPILED_QNN_ONNX 5.646 ms 157 - 157 MB NPU Use Export Script
unet w8a16 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) PRECOMPILED_QNN_ONNX 112.731 ms 0 - 899 MB NPU Use Export Script
unet w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile PRECOMPILED_QNN_ONNX 79.969 ms 0 - 16 MB NPU Use Export Script
unet w8a16 Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile PRECOMPILED_QNN_ONNX 63.819 ms 0 - 21 MB NPU Use Export Script
unet w8a16 Snapdragon 7 Gen 4 QRD Snapdragon® 7 Gen 4 Mobile PRECOMPILED_QNN_ONNX 172.669 ms 0 - 10 MB NPU Use Export Script
unet w8a16 Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile PRECOMPILED_QNN_ONNX 46.846 ms 0 - 7 MB NPU Use Export Script
unet w8a16 Snapdragon X Elite CRD Snapdragon® X Elite PRECOMPILED_QNN_ONNX 113.219 ms 842 - 842 MB NPU Use Export Script
vae w8a16 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) PRECOMPILED_QNN_ONNX 219.968 ms 3 - 6 MB NPU Use Export Script
vae w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile PRECOMPILED_QNN_ONNX 162.551 ms 3 - 22 MB NPU Use Export Script
vae w8a16 Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile PRECOMPILED_QNN_ONNX 147.035 ms 3 - 14 MB NPU Use Export Script
vae w8a16 Snapdragon 7 Gen 4 QRD Snapdragon® 7 Gen 4 Mobile PRECOMPILED_QNN_ONNX 445.273 ms 3 - 17 MB NPU Use Export Script
vae w8a16 Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile PRECOMPILED_QNN_ONNX 89.9 ms 3 - 13 MB NPU Use Export Script
vae w8a16 Snapdragon X Elite CRD Snapdragon® X Elite PRECOMPILED_QNN_ONNX 218.025 ms 59 - 59 MB NPU Use Export Script

Deploy to Snapdragon X Elite NPU

Please follow the Stable Diffusion Windows App tutorial to quantize model with custom weights.

Quantize and Deploy Your Own Fine-Tuned Stable Diffusion

Please follow the Quantize Stable Diffusion tutorial to quantize model with custom weights.

Installation

Install the package via pip:

# NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
pip install "qai-hub-models[stable-diffusion-v1-5]"

Configure Qualcomm® AI Hub Workbench to run this model on a cloud-hosted device

Sign-in to Qualcomm® AI Hub Workbench 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.

qai-hub configure --api_token API_TOKEN

Navigate to 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.

python -m qai_hub_models.models.stable_diffusion_v1_5.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.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.
python -m qai_hub_models.models.stable_diffusion_v1_5.export

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

  • TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.

  • QNN (.so export ): This sample app 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. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of Stable-Diffusion-v1.5 can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

Community