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DDColor

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DDColor
File size: 6,361 Bytes
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---
library_name: pytorch
license: other
tags:
- android
pipeline_tag: image-to-image

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ddcolor/web-assets/model_demo.png)

# DDColor: Optimized for Qualcomm Devices

DDColor is a coloring algorithm that produces natural, vivid color results from incoming black and white images.

This is based on the implementation of DDColor found [here](https://github.com/piddnad/DDColor/).
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/ddcolor) 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/ddcolor/releases/v0.49.1/ddcolor-onnx-float.zip)
| ONNX | w8a16 | Universal | QAIRT 2.42, ONNX Runtime 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ddcolor/releases/v0.49.1/ddcolor-onnx-w8a16.zip)
| ONNX | w8a8 | Universal | QAIRT 2.42, ONNX Runtime 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ddcolor/releases/v0.49.1/ddcolor-onnx-w8a8.zip)
| QNN_DLC | float | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ddcolor/releases/v0.49.1/ddcolor-qnn_dlc-float.zip)
| QNN_DLC | w8a8 | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ddcolor/releases/v0.49.1/ddcolor-qnn_dlc-w8a8.zip)
| TFLITE | float | Universal | QAIRT 2.43, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ddcolor/releases/v0.49.1/ddcolor-tflite-float.zip)
| TFLITE | w8a8 | Universal | QAIRT 2.43, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ddcolor/releases/v0.49.1/ddcolor-tflite-w8a8.zip)

For more device-specific assets and performance metrics, visit **[DDColor on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/ddcolor)**.


### 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/ddcolor) 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 [DDColor on GitHub](https://github.com/qualcomm/ai-hub-models/tree/v0.49.1/qai_hub_models/models/ddcolor) for usage instructions.

## Model Details

**Model Type:** Model_use_case.image_editing

**Model Stats:**
- Model checkpoint: ddcolor_paper_tiny.pth
- Input resolution: 224x224
- Number of parameters: 56.3M
- Model size (float): 215 MB
- Model size (w8a8): 54.8 MB

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| DDColor | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 676.747 ms | 1 - 728 MB | NPU
| DDColor | QNN_DLC | float | Snapdragon® X2 Elite | 713.048 ms | 1 - 1 MB | NPU
| DDColor | QNN_DLC | float | Snapdragon® X Elite | 1146.686 ms | 1 - 1 MB | NPU
| DDColor | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 831.044 ms | 1 - 1368 MB | NPU
| DDColor | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 1999.451 ms | 1 - 783 MB | NPU
| DDColor | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 1096.751 ms | 1 - 3 MB | NPU
| DDColor | QNN_DLC | float | Qualcomm® SA8775P | 1108.481 ms | 1 - 754 MB | NPU
| DDColor | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 1208.928 ms | 0 - 485 MB | NPU
| DDColor | QNN_DLC | float | Qualcomm® SA7255P | 1999.451 ms | 1 - 783 MB | NPU
| DDColor | QNN_DLC | float | Qualcomm® SA8295P | 1257.294 ms | 1 - 409 MB | NPU
| DDColor | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 838.052 ms | 0 - 781 MB | NPU
| DDColor | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 936.415 ms | 0 - 499 MB | NPU
| DDColor | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 1205.208 ms | 0 - 567 MB | NPU
| DDColor | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 3031.947 ms | 0 - 332 MB | NPU
| DDColor | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 1733.136 ms | 0 - 4 MB | NPU
| DDColor | TFLITE | w8a8 | Qualcomm® SA8775P | 1730.83 ms | 0 - 331 MB | NPU
| DDColor | TFLITE | w8a8 | Qualcomm® QCM6690 | 1610.56 ms | 104 - 505 MB | CPU
| DDColor | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 2116.106 ms | 0 - 561 MB | NPU
| DDColor | TFLITE | w8a8 | Qualcomm® SA7255P | 3031.947 ms | 0 - 332 MB | NPU
| DDColor | TFLITE | w8a8 | Qualcomm® SA8295P | 2043.602 ms | 0 - 333 MB | NPU
| DDColor | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 967.678 ms | 1 - 416 MB | NPU
| DDColor | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 492.383 ms | 25 - 395 MB | CPU

## License
* The license for the original implementation of DDColor can be found
  [here](https://github.com/piddnad/DDColor/blob/master/LICENSE).

## References
* [DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders](https://arxiv.org/abs/2201.03545)
* [Source Model Implementation](https://github.com/piddnad/DDColor/)

## 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).