https://github.com/akbartus/deoldify-on-browser
This repository demonstrates browser based implementation of DeOldify that colorizes black & white images. It is powered by Onnx and does not require any web servers.
https://github.com/akbartus/deoldify-on-browser
ai deoldify onnx onnxruntime-web
Last synced: 3 months ago
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This repository demonstrates browser based implementation of DeOldify that colorizes black & white images. It is powered by Onnx and does not require any web servers.
- Host: GitHub
- URL: https://github.com/akbartus/deoldify-on-browser
- Owner: akbartus
- License: mit
- Created: 2024-09-08T18:16:03.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-08T23:37:45.000Z (over 1 year ago)
- Last Synced: 2025-02-28T19:26:56.565Z (over 1 year ago)
- Topics: ai, deoldify, onnx, onnxruntime-web
- Language: HTML
- Homepage: https://deoldify.glitch.me/
- Size: 135 KB
- Stars: 6
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DeOldify on Browser
### **Description / Rationale**
This repository demonstrates web-based implementation of DeOldify, a Deep Learning based project for colorizing and restoring old images. This project demonstrates implementation of 2 models:
1. Original artistic DeOldify model ("original" folder)
2. Quantized DeOldify model ("quantized" folder).
### **Instructions**
To use a DeOldify example, copy the corresponding html file contents. To dowload and locally serve models download them from the link provided:
1. Original artistic model: https://cdn.glitch.me/2046b88b-673a-457f-b1b8-7169ce9bf13a/deoldify-art.onnx (~243mb)
2. Quantized model: https://cdn.glitch.me/2046b88b-673a-457f-b1b8-7169ce9bf13a/deoldify-quant.onnx (~61mb)
### **Onnx files and Quantization**
Original onnx files were taken from releases page of Deoldify Onnx repository by Thomas De.
To quantize an onnx file do the following:
1. Open Google Colab and create a new Notebook.
2. Upload onnx file downloaded from releases page above. Also upload "remove_initializer_from_input.py". The file content is given below:
```python
# /content/remove_initializer_from_input.py
import argparse
import onnx
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True, help="input model")
parser.add_argument("--output", required=True, help="output model")
args = parser.parse_args()
return args
def remove_initializer_from_input():
args = get_args()
model = onnx.load(args.input)
if model.ir_version < 4:
print("Model with ir_version below 4 requires to include initilizer in graph input")
return
inputs = model.graph.input
name_to_input = {}
for input in inputs:
name_to_input[input.name] = input
for initializer in model.graph.initializer:
if initializer.name in name_to_input:
inputs.remove(name_to_input[initializer.name])
onnx.save(model, args.output)
if __name__ == "__main__":
remove_initializer_from_input()
```
3. Run the following code:
```python
# Install dependencies and
!pip install onnxruntime
!pip install onnx
```
Run onnx preprocess:
```python
!python -m onnxruntime.quantization.preprocess --input '/content/deoldify.onnx' --output '/content/deoldify-final.onnx'
```
Generate quantized file:
```python
import onnx
from onnxruntime.quantization import quantize_dynamic, QuantType
model_fp32 = '/content/deoldify-final.onnx'
model_quant = '/content/deoldify-quant.onnx'
quantized_model = quantize_dynamic(model_fp32, model_quant, weight_type=QuantType.QUInt8)
```
Remove initializer from input of deoldify-quant.onnx file (otherwise the model will be throwing an error related to initializer):
```python
!python /content/remove_initializer_from_input.py --input /content/deoldify-quant.onnx --output /content/deoldify-quant-clear.onnx
```
### **Demo**
To see quantized DeOldify model at work, visit the following page: Demo