https://github.com/kefirchk/animatica-ml-engine
Animatica is my diploma project that generates videos from text and images using neural networks. It automates animation creation with image generation, image-to-video conversion, and post-processing
https://github.com/kefirchk/animatica-ml-engine
animation first-order-model gan machine-learning neural-networks python pytorch real-time video-generation
Last synced: about 1 month ago
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Animatica is my diploma project that generates videos from text and images using neural networks. It automates animation creation with image generation, image-to-video conversion, and post-processing
- Host: GitHub
- URL: https://github.com/kefirchk/animatica-ml-engine
- Owner: kefirchk
- License: mit
- Created: 2025-04-07T19:11:39.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-04-10T19:45:45.000Z (about 1 month ago)
- Last Synced: 2025-04-13T11:14:30.875Z (about 1 month ago)
- Topics: animation, first-order-model, gan, machine-learning, neural-networks, python, pytorch, real-time, video-generation
- Language: Jupyter Notebook
- Homepage:
- Size: 11.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Animatica-ml-engine
Animatica is my diploma project that generates videos from images using neural networks. It automates animation creation with image generation, image-to-video conversion, and post-processing.
## Deploying on Local
### Step 1
Create a virtual environment.
###### *Linux/macOS:*
```bash
python3 -m venv venv
source venv/bin/activate
```
###### *Windows:*
```bash
python -m venv venv
source venv/Scripts/activate
```
### Step 2
```bash
cd app
pip install -r requirements.txt
```
### Step 3
Download pretrained models and save them in folder named extract
Checkpoints can be found under following link:
[google-drive](https://drive.google.com/drive/folders/1PyQJmkdCsAkOYwUyaj_l-l0as-iLDgeH)
or [yandex-disk](https://disk.yandex.ru/d/lEw8uRm140L_eQ).
###### *Linux/macOS:*
```bash
unzip checkpoints.zip
rm checkpoints.zip
```
###### *Windows:*
Unzip ***checkpoints.zip*** using unzipping software like *7zip*.
### Step 4
Run the project:
1. Run application from live camera:
```python .\animate_in_real_time.py -i path_to_input_file -c path_to_checkpoint```
Example:
```python .\animate_in_real_time.py -i .\data\input\monalisa.png -c .\data\checkpoints\vox-cpk.pth.tar```
2. Run application from video file:
```python .\animate_in_real_time.py -i path_to_input_file -c path_to_checkpoint -v path_to_video_file```
Example:
```python .\animate_in_real_time.py -i .\data\input\monalisa.png -c .\checkpoints\vox-cpk.pth.tar -v .\data\input\test.mp4 ```
## ~~Deploying via Docker~~
DOES NOT WORK!
### ~~Step 1~~
```bash
docker build -t animatica-ml-engine .
```
### ~~Step 2~~
~~Run container.~~
###### ~~With CUDA:~~
```bash
docker run --rm -it --device=/dev/video0:/dev/video0 \
--env DISPLAY=$DISPLAY \
--env="QT_X11_NO_MITSHM=1" \
-v /dev/video0:/dev/video0 \
-v /tmp/.X11-unix:/tmp/.X11-unix:ro \
--gpus all -v "${PWD}:/app" \
-p 8888:8888 -p 6006:6006 \
--name first-order-model \
first-order-model jupyter notebook --no-browser --port 8888 --ip=* --allow-root
```
###### ~~Without CUDA:~~
```bash
docker run --rm -it --device=/dev/video0:/dev/video0 \
--env DISPLAY=$DISPLAY \
--env="QT_X11_NO_MITSHM=1" \
-v /dev/video0:/dev/video0 \
-v /tmp/.X11-unix:/tmp/.X11-unix:ro \
-v "${PWD}:/app" \
-p 8888:8888 -p 6006:6006 \
--name first-order-model \
first-order-model jupyter notebook --no-browser --port 8888 --ip=* --allow-root
```
## Demo
## TODO
- [x] Add pre-commit.
- [ ] Add GitHub Actions.
- [ ] Add Docker.
- [ ] Optimize and refactor the code.