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---
library_name: pytorch
license: other
tags:
- android
pipeline_tag: image-to-image
---
# 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/quic/ai-hub-models/blob/main/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 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ddcolor/releases/v0.46.0/ddcolor-onnx-float.zip)
| ONNX | w8a8 | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ddcolor/releases/v0.46.0/ddcolor-onnx-w8a8.zip)
| ONNX | w8a8_mixed_int16 | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ddcolor/releases/v0.46.0/ddcolor-onnx-w8a8_mixed_int16.zip)
| QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ddcolor/releases/v0.46.0/ddcolor-qnn_dlc-float.zip)
| QNN_DLC | w8a8 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ddcolor/releases/v0.46.0/ddcolor-qnn_dlc-w8a8.zip)
| QNN_DLC | w8a8_mixed_int16 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ddcolor/releases/v0.46.0/ddcolor-qnn_dlc-w8a8_mixed_int16.zip)
| TFLITE | float | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ddcolor/releases/v0.46.0/ddcolor-tflite-float.zip)
| TFLITE | w8a8 | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ddcolor/releases/v0.46.0/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/quic/ai-hub-models/blob/main/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/quic/ai-hub-models/blob/main/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® X Elite | 1135.963 ms | 1 - 1 MB | NPU
| DDColor | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 826.47 ms | 0 - 1160 MB | NPU
| DDColor | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 1983.976 ms | 1 - 795 MB | NPU
| DDColor | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 1100.967 ms | 1 - 4 MB | NPU
| DDColor | QNN_DLC | float | Qualcomm® SA8775P | 1101.267 ms | 1 - 644 MB | NPU
| DDColor | QNN_DLC | float | Qualcomm® QCS9075 | 1106.511 ms | 1 - 4 MB | NPU
| DDColor | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 1191.042 ms | 0 - 482 MB | NPU
| DDColor | QNN_DLC | float | Qualcomm® SA7255P | 1983.976 ms | 1 - 795 MB | NPU
| DDColor | QNN_DLC | float | Qualcomm® SA8295P | 1222.255 ms | 1 - 410 MB | NPU
| DDColor | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 832.809 ms | 1 - 754 MB | NPU
| DDColor | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 682.665 ms | 1 - 655 MB | NPU
| DDColor | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 1206.113 ms | 0 - 569 MB | NPU
| DDColor | TFLITE | w8a8 | Qualcomm® QCS6490 | 678.572 ms | 106 - 235 MB | CPU
| DDColor | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 3031.978 ms | 1 - 483 MB | NPU
| DDColor | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 1697.236 ms | 0 - 4 MB | NPU
| DDColor | TFLITE | w8a8 | Qualcomm® SA8775P | 1730.938 ms | 0 - 496 MB | NPU
| DDColor | TFLITE | w8a8 | Qualcomm® QCS9075 | 1670.237 ms | 0 - 62 MB | NPU
| DDColor | TFLITE | w8a8 | Qualcomm® QCM6690 | 1694.244 ms | 9 - 409 MB | CPU
| DDColor | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 2119.822 ms | 0 - 551 MB | NPU
| DDColor | TFLITE | w8a8 | Qualcomm® SA7255P | 3031.978 ms | 1 - 483 MB | NPU
| DDColor | TFLITE | w8a8 | Qualcomm® SA8295P | 2039.833 ms | 0 - 348 MB | NPU
| DDColor | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 936.183 ms | 0 - 457 MB | NPU
| DDColor | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 487.698 ms | 4 - 374 MB | CPU
| DDColor | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 936.444 ms | 0 - 492 MB | NPU
## 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).
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