https://github.com/parisneo/chronoflux

A Gan to see a face at a specific age
https://github.com/parisneo/chronoflux

Last synced: 5 months ago
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A Gan to see a face at a specific age

• Host: GitHub
• URL: https://github.com/parisneo/chronoflux
• Owner: ParisNeo
• License: apache-2.0
• Created: 2025-02-10T19:21:43.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-12T14:21:12.000Z (over 1 year ago)
• Last Synced: 2025-06-28T07:06:58.751Z (about 1 year ago)
• Language: Python
• Size: 23.4 KB
• Stars: 6
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# ChronoFlux: Neural Age Transformation Model (WIP) ⏳🧬

[![Apache License](https://img.shields.io/badge/License-Apache_2.0-green.svg)](https://opensource.org/licenses/Apache-2.0)

[![PyTorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?logo=PyTorch&logoColor=white)](https://pytorch.org/)



  



## Overview 🔍

ChronoFlux is a conditional GAN model that transforms facial images to target ages while preserving identity. Built with PyTorch and trained on the UTKFace dataset, this AI can:

- Age progression/regression with continuous age control

- Maintain facial identity features

- Generate high-quality 128x128 resolution images

## Features ✨

- 🎮 Conditional age control (0-100 years)

- 🤖 Hybrid U-Net architecture with age embeddings

- 📦 Easy Hugging Face Hub integration

- 🚀 Production-ready inference pipeline

- 🔧 Modular design for customization

## Installation ⚙️

```bash

pip install torch torchvision datasets huggingface_hub matplotlib numpy

```

## Usage 🚀

```python

from chronoflux import age_transformation

from PIL import Image

# Load from Hugging Face Hub

model = age_transformation.load_model("ParisNeo/ChronoFlux")

# Transform image

input_image = Image.open("person.jpg")

result = model.transform(

    image=input_image,

    target_age=65,  # Between 0-100

    output_size=256  # Upscale if needed

)

result.save("aged_person.jpg")

```

## Training 🏋️♂️

1. Clone repo:

```bash

git clone https://github.com/ParisNeo/ChronoFlux.git

cd scripts

```

2. Train model:

```python

python train.py \

  --dataset utk-face \

  --batch_size 32 \

  --epochs 100 \

  --resolution 128 \

  --output_dir results

```

## Model Architecture 🧠

```mermaid

graph TD

    A[Input Image] --> B(Encoder)

    C[Target Age] --> D(Age Embedding)

    B --> E[Latent Features]

    D --> E

    E --> F(Decoder)

    F --> G[Transformed Image]

```

## Contributing 🤝

We welcome contributions! Please see:

- [Contributing Guidelines](CONTRIBUTING.md)

- [Code of Conduct](CODE_OF_CONDUCT.md)

## License 📜

```

Copyright 2023 ParisNeo

Licensed under the Apache License, Version 2.0 (the "License");

you may not use this file except in compliance with the License.

You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software

distributed under the License is distributed on an "AS IS" BASIS,

WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

See the License for the specific language governing permissions and

limitations under the License.

```

## Acknowledgments 🌟

- UTKFace Dataset contributors

- PyTorch team

- Hugging Face community

---

**Ethical Note:** Use responsibly. Avoid generating content that could enable age discrimination or identity manipulation.