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library_name: pytorch
license: other
tags:
- android
pipeline_tag: image-to-video
---
# First-Order-Motion-Model: Optimized for Qualcomm Devices
FOMM is a machine learning model that animates a still image to mirror the movements from a target video.
This is based on the implementation of First-Order-Motion-Model found [here](https://github.com/AliaksandrSiarohin/first-order-model/tree/master).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/tree/v0.49.1/qai_hub_models/models/fomm) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
## Getting Started
There are two ways to deploy this model on your device:
### Option 1: Download Pre-Exported Models
Below are pre-exported model assets ready for deployment.
| Runtime | Precision | Chipset | SDK Versions | Download |
|---|---|---|---|---|
| ONNX | float | Universal | QAIRT 2.42, ONNX Runtime 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fomm/releases/v0.49.1/fomm-onnx-float.zip)
For more device-specific assets and performance metrics, visit **[First-Order-Motion-Model on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/fomm)**.
### Option 2: Export with Custom Configurations
Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/tree/v0.49.1/qai_hub_models/models/fomm) Python library to compile and export the model with your own:
- Custom weights (e.g., fine-tuned checkpoints)
- Custom input shapes
- Target device and runtime configurations
This option is ideal if you need to customize the model beyond the default configuration provided here.
See our repository for [First-Order-Motion-Model on GitHub](https://github.com/qualcomm/ai-hub-models/tree/v0.49.1/qai_hub_models/models/fomm) for usage instructions.
## Model Details
**Model Type:** Model_use_case.video_generation
**Model Stats:**
- Model checkpoint: vox-256
- Input resolution: 256x256
- Model size (FOMMDetector) (float): 54.2 MB
- Model size (FOMMGenerator) (float): 174 MB
## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| FOMMDetector | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 2.75 ms | 0 - 24 MB | NPU
| FOMMDetector | ONNX | float | Snapdragon® X2 Elite | 2.777 ms | 28 - 28 MB | NPU
| FOMMDetector | ONNX | float | Snapdragon® X Elite | 4.579 ms | 27 - 27 MB | NPU
| FOMMDetector | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 3.275 ms | 0 - 32 MB | NPU
| FOMMDetector | ONNX | float | Qualcomm® QCS9075 | 5.8 ms | 1 - 4 MB | NPU
| FOMMDetector | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 2.925 ms | 0 - 21 MB | NPU
| FOMMGenerator | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 10.76 ms | 0 - 196 MB | NPU
| FOMMGenerator | ONNX | float | Snapdragon® X2 Elite | 12.262 ms | 91 - 91 MB | NPU
| FOMMGenerator | ONNX | float | Snapdragon® X Elite | 29.148 ms | 89 - 89 MB | NPU
| FOMMGenerator | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 17.31 ms | 3 - 222 MB | NPU
| FOMMGenerator | ONNX | float | Qualcomm® QCS9075 | 34.708 ms | 18 - 22 MB | NPU
| FOMMGenerator | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 13.265 ms | 8 - 196 MB | NPU
## License
* The license for the original implementation of First-Order-Motion-Model can be found
[here](https://github.com/AliaksandrSiarohin/first-order-model/blob/master/LICENSE.md).
## References
* [First Order Motion Model for Image Animation](https://arxiv.org/abs/2003.00196)
* [Source Model Implementation](https://github.com/AliaksandrSiarohin/first-order-model/tree/master)
## 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).
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