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.

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.

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	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	QNN_CONTEXT_BINARY	18.499 ms	0 - 8 MB	NPU	Use Export Script
text_encoder	w8a16	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	QNN_CONTEXT_BINARY	8.205 ms	0 - 3 MB	NPU	Use Export Script
text_encoder	w8a16	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	QNN_CONTEXT_BINARY	8.377 ms	0 - 9 MB	NPU	Use Export Script
text_encoder	w8a16	SA7255P ADP	Qualcomm® SA7255P	QNN_CONTEXT_BINARY	18.499 ms	0 - 8 MB	NPU	Use Export Script
text_encoder	w8a16	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	QNN_CONTEXT_BINARY	8.366 ms	0 - 2 MB	NPU	Use Export Script
text_encoder	w8a16	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	QNN_CONTEXT_BINARY	8.216 ms	0 - 2 MB	NPU	Use Export Script
text_encoder	w8a16	SA8775P ADP	Qualcomm® SA8775P	QNN_CONTEXT_BINARY	8.377 ms	0 - 9 MB	NPU	Use Export Script
text_encoder	w8a16	Samsung Galaxy S23	Snapdragon® 8 Gen 2 Mobile	QNN_CONTEXT_BINARY	8.285 ms	0 - 7 MB	NPU	Use Export Script
text_encoder	w8a16	Samsung Galaxy S23	Snapdragon® 8 Gen 2 Mobile	PRECOMPILED_QNN_ONNX	8.141 ms	0 - 387 MB	NPU	Use Export Script
text_encoder	w8a16	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	QNN_CONTEXT_BINARY	5.601 ms	0 - 18 MB	NPU	Use Export Script
text_encoder	w8a16	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	PRECOMPILED_QNN_ONNX	5.404 ms	0 - 19 MB	NPU	Use Export Script
text_encoder	w8a16	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite Mobile	QNN_CONTEXT_BINARY	4.56 ms	0 - 15 MB	NPU	Use Export Script
text_encoder	w8a16	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite Mobile	PRECOMPILED_QNN_ONNX	5.312 ms	0 - 14 MB	NPU	Use Export Script
text_encoder	w8a16	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN_CONTEXT_BINARY	8.652 ms	0 - 0 MB	NPU	Use Export Script
text_encoder	w8a16	Snapdragon X Elite CRD	Snapdragon® X Elite	PRECOMPILED_QNN_ONNX	8.311 ms	378 - 378 MB	NPU	Use Export Script
unet	w8a16	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	QNN_CONTEXT_BINARY	232.583 ms	0 - 8 MB	NPU	Use Export Script
unet	w8a16	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	QNN_CONTEXT_BINARY	93.286 ms	0 - 2 MB	NPU	Use Export Script
unet	w8a16	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	QNN_CONTEXT_BINARY	87.78 ms	0 - 9 MB	NPU	Use Export Script
unet	w8a16	SA7255P ADP	Qualcomm® SA7255P	QNN_CONTEXT_BINARY	232.583 ms	0 - 8 MB	NPU	Use Export Script
unet	w8a16	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	QNN_CONTEXT_BINARY	93.785 ms	0 - 3 MB	NPU	Use Export Script
unet	w8a16	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	QNN_CONTEXT_BINARY	93.873 ms	0 - 3 MB	NPU	Use Export Script
unet	w8a16	SA8775P ADP	Qualcomm® SA8775P	QNN_CONTEXT_BINARY	87.78 ms	0 - 9 MB	NPU	Use Export Script
unet	w8a16	Samsung Galaxy S23	Snapdragon® 8 Gen 2 Mobile	QNN_CONTEXT_BINARY	93.717 ms	0 - 7 MB	NPU	Use Export Script
unet	w8a16	Samsung Galaxy S23	Snapdragon® 8 Gen 2 Mobile	PRECOMPILED_QNN_ONNX	95.033 ms	0 - 898 MB	NPU	Use Export Script
unet	w8a16	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	QNN_CONTEXT_BINARY	66.248 ms	0 - 18 MB	NPU	Use Export Script
unet	w8a16	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	PRECOMPILED_QNN_ONNX	67.516 ms	0 - 14 MB	NPU	Use Export Script
unet	w8a16	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite Mobile	QNN_CONTEXT_BINARY	58.258 ms	0 - 14 MB	NPU	Use Export Script
unet	w8a16	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite Mobile	PRECOMPILED_QNN_ONNX	58.617 ms	0 - 19 MB	NPU	Use Export Script
unet	w8a16	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN_CONTEXT_BINARY	95.326 ms	0 - 0 MB	NPU	Use Export Script
unet	w8a16	Snapdragon X Elite CRD	Snapdragon® X Elite	PRECOMPILED_QNN_ONNX	96.279 ms	843 - 843 MB	NPU	Use Export Script
vae	w8a16	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	QNN_CONTEXT_BINARY	719.113 ms	0 - 9 MB	NPU	Use Export Script
vae	w8a16	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	QNN_CONTEXT_BINARY	270.059 ms	1 - 3 MB	NPU	Use Export Script
vae	w8a16	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	QNN_CONTEXT_BINARY	249.046 ms	0 - 10 MB	NPU	Use Export Script
vae	w8a16	SA7255P ADP	Qualcomm® SA7255P	QNN_CONTEXT_BINARY	719.113 ms	0 - 9 MB	NPU	Use Export Script
vae	w8a16	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	QNN_CONTEXT_BINARY	271.52 ms	0 - 3 MB	NPU	Use Export Script
vae	w8a16	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	QNN_CONTEXT_BINARY	271.625 ms	0 - 4 MB	NPU	Use Export Script
vae	w8a16	SA8775P ADP	Qualcomm® SA8775P	QNN_CONTEXT_BINARY	249.046 ms	0 - 10 MB	NPU	Use Export Script
vae	w8a16	Samsung Galaxy S23	Snapdragon® 8 Gen 2 Mobile	QNN_CONTEXT_BINARY	269.852 ms	0 - 2 MB	NPU	Use Export Script
vae	w8a16	Samsung Galaxy S23	Snapdragon® 8 Gen 2 Mobile	PRECOMPILED_QNN_ONNX	274.742 ms	0 - 68 MB	NPU	Use Export Script
vae	w8a16	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	QNN_CONTEXT_BINARY	202.714 ms	0 - 21 MB	NPU	Use Export Script
vae	w8a16	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	PRECOMPILED_QNN_ONNX	203.279 ms	3 - 22 MB	NPU	Use Export Script
vae	w8a16	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite Mobile	QNN_CONTEXT_BINARY	175.114 ms	0 - 14 MB	NPU	Use Export Script
vae	w8a16	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite Mobile	PRECOMPILED_QNN_ONNX	174.448 ms	3 - 17 MB	NPU	Use Export Script
vae	w8a16	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN_CONTEXT_BINARY	265.137 ms	0 - 0 MB	NPU	Use Export Script
vae	w8a16	Snapdragon X Elite CRD	Snapdragon® X Elite	PRECOMPILED_QNN_ONNX	264.748 ms	62 - 62 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:

pip install "qai-hub-models[stable-diffusion-v2-1]"

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_v2_1.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.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_v2_1.export

Profiling Results
------------------------------------------------------------
text_encoder
Device                          : cs_8275 (ANDROID 14)                 
Runtime                         : QNN_CONTEXT_BINARY                   
Estimated inference time (ms)   : 18.5                                 
Estimated peak memory usage (MB): [0, 8]                               
Total # Ops                     : 788                                  
Compute Unit(s)                 : npu (788 ops) gpu (0 ops) cpu (0 ops)

------------------------------------------------------------
unet
Device                          : cs_8275 (ANDROID 14)                  
Runtime                         : QNN_CONTEXT_BINARY                    
Estimated inference time (ms)   : 232.6                                 
Estimated peak memory usage (MB): [0, 8]                                
Total # Ops                     : 5784                                  
Compute Unit(s)                 : npu (5784 ops) gpu (0 ops) cpu (0 ops)

------------------------------------------------------------
vae
Device                          : cs_8275 (ANDROID 14)                 
Runtime                         : QNN_CONTEXT_BINARY                   
Estimated inference time (ms)   : 719.1                                
Estimated peak memory usage (MB): [0, 9]                               
Total # Ops                     : 175                                  
Compute Unit(s)                 : npu (175 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-v2.1's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

The license for the original implementation of Stable-Diffusion-v2.1 can be found here.
The license for the compiled assets for on-device deployment can be found here

References

Community

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