---
license: mit
language:
- en
pipeline_tag: image-to-video
tags:
- GAN
- U-Net
---

# Model Card: Ayo_Generator for GIF Frame Generation

## Model Overview
The **Ayo_Generator** model is a GAN-based architecture designed to generate animated sequences, such as GIFs, from a single input image. The model uses a combination of CNN layers, upsampling, and attention mechanisms to produce smooth, continuous motion frames from a static image input. The architecture is particularly suited for generating simple animations (e.g., jumping, running) in pixel-art styles or other low-resolution images.

## Intended Use
The **Ayo_Generator** can be used in creative projects, animation generation, or for educational purposes to demonstrate GAN-based sequential generation. Users can input a static character image and generate a sequence of frames that simulate motion.

### Applications
- **Sprite Animation for Games:** Generate small animated characters from a single pose.
- **Educational Demos:** Teach GAN-based frame generation and image-to-motion transformations.
- **GIF Creation:** Turn still images into animated GIFs with basic motion patterns.

## How It Works
1. **Input Image Encoding:** The input image is encoded through a series of convolutional layers, capturing spatial features.
2. **Frame-Specific Embedding:** Each frame is assigned an embedding that indicates its position in the sequence.
3. **Sequential Frame Generation:** Each frame is generated sequentially, with the generator network using the previous frame as context for generating the next.
4. **Attention and Skip Connections:** These features help retain spatial details and produce coherent motion across frames.

## Model Architecture
- **Encoder:** Uses multiple convolutional layers to encode the input image into a lower-dimensional feature space.
- **Dense Layers:** Compress and embed the encoded information to capture relevant features while reducing dimensionality.
- **Decoder:** Upsamples the compressed feature representation, generating frame-by-frame outputs.
- **Attention and Skip Connections:** Improve coherence and preserve details, helping to ensure continuity across frames.

## Training Data
The **Ayo_Generator** was trained on a custom dataset containing animated characters and their associated motion frames. The dataset includes:
- **Character Images:** Base images from which motion frames were generated.
- **Motion Frames:** Frames for each character to simulate movement, such as walking or jumping.

### Data Preprocessing
Input images are preprocessed to 128x128 resolution and normalized to a [-1, 1] scale. Frame embeddings are incorporated to help the model understand sequential order, with each frame index converted into a unique embedding vector.

## Sample GIF Generation
Given an input image, this example code generates a series of frames and stitches them into a GIF.

```python
import imageio

input_image = ...  # Load or preprocess an input image as needed
generated_frames = [generator(input_image, tf.constant([i])) for i in range(10)]

# Save as GIF
with imageio.get_writer('generated_animation.gif', mode='I') as writer:
    for frame in generated_frames:
        writer.append_data((frame.numpy() * 255).astype(np.uint8))
```

## Evaluation Metrics

The model was evaluated based on:

- **MSE Loss (Pixel Similarity):** Measures pixel-level similarity between real and generated frames.
- **Perceptual Loss:** Captures higher-level similarity using VGG19 features for realism in generated frames.
- **Temporal Consistency:** Ensures frames flow smoothly by minimizing the difference between adjacent frames.

## Future Improvements

Potential improvements for the Ayo Generator include:

- **Enhanced Temporal Consistency:** Using RNNs or temporal loss to improve coherence.
- **Higher Resolution Output:** Modifying the model to support 256x256 or higher.
- **Additional Character Variation:** Adding data variety to improve generalization.

## Ethical Considerations

The **Ayo Generator** is intended for creative and educational purposes. Users should avoid:

- **Unlawful or Offensive Content:** Misusing the model to create or distribute harmful animations.
- **Unauthorized Replication of Identities:** Ensure that generated characters respect IP and individual likeness rights.

## Model Card Author

This Model Card was created by [Minseok Kim]. For any questions, please contact me at kevkim1018@gmail.com or https://github.com/minnnnnnnn-dev


## Acknowledgments

I would like to extend my gratitude to [Junyoung Choi] https://github.com/tomato-data for valuable insights and assistance throughout the development of the **Ayo Generator** model. Their feedback greatly contributed to the improvement of this project.

Additionally, special thanks to the [Team **Six Guys**] for providing helpful resources and support during the research process.