v0.34.0

bc78410 verified 5 days ago

11 kB

	---
	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/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).



	### 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](https://github.com/quic/ai-hub-apps/tree/main/apps/windows/python/StableDiffusion) tutorial to quantize model with custom weights.

	## Quantize and Deploy Your Own Fine-Tuned Stable Diffusion

	Please follow the [Quantize Stable Diffusion]({REPOSITORY_URL}/tutorials/stable_diffusion/quantize_stable_diffusion.md) tutorial to quantize model with custom weights.



	## Installation


	Install the package via pip:
	```bash
	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](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.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.

	```bash
	python -m qai_hub_models.models.stable_diffusion_v2_1.export
	```






	## 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).
	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).