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Stable-Diffusion-v1.5

Unconditional Image Generation
PyTorch
generative_ai
android
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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
    • 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
text_encoder w8a16 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 11.111 ms 0 - 9 MB NPU Use Export Script
text_encoder w8a16 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 5.363 ms 0 - 2 MB NPU Use Export Script
text_encoder w8a16 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 5.933 ms 0 - 10 MB NPU Use Export Script
text_encoder w8a16 SA7255P ADP Qualcomm® SA7255P QNN 11.111 ms 0 - 9 MB NPU Use Export Script
text_encoder w8a16 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 5.396 ms 0 - 3 MB NPU Use Export Script
text_encoder w8a16 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 5.398 ms 0 - 3 MB NPU Use Export Script
text_encoder w8a16 SA8775P ADP Qualcomm® SA8775P QNN 5.933 ms 0 - 10 MB NPU Use Export Script
text_encoder w8a16 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 5.413 ms 0 - 2 MB NPU Use Export Script
text_encoder w8a16 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 5.773 ms 0 - 162 MB NPU Stable-Diffusion-v1.5.onnx
text_encoder w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 3.909 ms 0 - 18 MB NPU Use Export Script
text_encoder w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 4.097 ms 0 - 19 MB NPU Stable-Diffusion-v1.5.onnx
text_encoder w8a16 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 3.501 ms 0 - 14 MB NPU Use Export Script
text_encoder w8a16 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 3.754 ms 0 - 14 MB NPU Stable-Diffusion-v1.5.onnx
text_encoder w8a16 Snapdragon X Elite CRD Snapdragon® X Elite QNN 5.836 ms 0 - 0 MB NPU Use Export Script
text_encoder w8a16 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 5.977 ms 157 - 157 MB NPU Stable-Diffusion-v1.5.onnx
unet w8a16 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 263.602 ms 0 - 7 MB NPU Use Export Script
unet w8a16 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 110.81 ms 0 - 2 MB NPU Use Export Script
unet w8a16 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 105.273 ms 0 - 8 MB NPU Use Export Script
unet w8a16 SA7255P ADP Qualcomm® SA7255P QNN 263.602 ms 0 - 7 MB NPU Use Export Script
unet w8a16 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 111.393 ms 0 - 7 MB NPU Use Export Script
unet w8a16 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 111.635 ms 0 - 3 MB NPU Use Export Script
unet w8a16 SA8775P ADP Qualcomm® SA8775P QNN 105.273 ms 0 - 8 MB NPU Use Export Script
unet w8a16 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 111.871 ms 0 - 3 MB NPU Use Export Script
unet w8a16 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 111.763 ms 0 - 898 MB NPU Stable-Diffusion-v1.5.onnx
unet w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 78.019 ms 0 - 18 MB NPU Use Export Script
unet w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 80.045 ms 0 - 15 MB NPU Stable-Diffusion-v1.5.onnx
unet w8a16 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 67.577 ms 0 - 14 MB NPU Use Export Script
unet w8a16 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 67.421 ms 0 - 15 MB NPU Stable-Diffusion-v1.5.onnx
unet w8a16 Snapdragon X Elite CRD Snapdragon® X Elite QNN 113.221 ms 0 - 0 MB NPU Use Export Script
unet w8a16 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 114.344 ms 842 - 842 MB NPU Stable-Diffusion-v1.5.onnx
vae w8a16 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 718.731 ms 0 - 9 MB NPU Use Export Script
vae w8a16 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 270.917 ms 0 - 4 MB NPU Use Export Script
vae w8a16 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 248.917 ms 0 - 10 MB NPU Use Export Script
vae w8a16 SA7255P ADP Qualcomm® SA7255P QNN 718.731 ms 0 - 9 MB NPU Use Export Script
vae w8a16 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 270.775 ms 0 - 3 MB NPU Use Export Script
vae w8a16 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 270.064 ms 0 - 3 MB NPU Use Export Script
vae w8a16 SA8775P ADP Qualcomm® SA8775P QNN 248.917 ms 0 - 10 MB NPU Use Export Script
vae w8a16 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 271.182 ms 0 - 2 MB NPU Use Export Script
vae w8a16 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 269.668 ms 0 - 66 MB NPU Stable-Diffusion-v1.5.onnx
vae w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 207.636 ms 0 - 18 MB NPU Use Export Script
vae w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 207.55 ms 3 - 22 MB NPU Stable-Diffusion-v1.5.onnx
vae w8a16 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 175.815 ms 0 - 14 MB NPU Use Export Script
vae w8a16 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 194.632 ms 3 - 17 MB NPU Stable-Diffusion-v1.5.onnx
vae w8a16 Snapdragon X Elite CRD Snapdragon® X Elite QNN 265.394 ms 0 - 0 MB NPU Use Export Script
vae w8a16 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 265.427 ms 63 - 63 MB NPU Stable-Diffusion-v1.5.onnx

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:

pip install "qai-hub-models[stable-diffusion-v1-5]"

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

Sign-in to Qualcomm® AI Hub 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

Profiling Results
------------------------------------------------------------
text_encoder
Device                          : cs_8275 (ANDROID 14)                 
Runtime                         : QNN                                  
Estimated inference time (ms)   : 11.1                                 
Estimated peak memory usage (MB): [0, 9]                               
Total # Ops                     : 437                                  
Compute Unit(s)                 : npu (437 ops) gpu (0 ops) cpu (0 ops)

------------------------------------------------------------
unet
Device                          : cs_8275 (ANDROID 14)                  
Runtime                         : QNN                                   
Estimated inference time (ms)   : 263.6                                 
Estimated peak memory usage (MB): [0, 7]                                
Total # Ops                     : 4041                                  
Compute Unit(s)                 : npu (4041 ops) gpu (0 ops) cpu (0 ops)

------------------------------------------------------------
vae
Device                          : cs_8275 (ANDROID 14)                 
Runtime                         : QNN                                  
Estimated inference time (ms)   : 718.7                                
Estimated peak memory usage (MB): [0, 9]                               
Total # Ops                     : 173                                  
Compute Unit(s)                 : npu (173 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 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