File size: 1,435 Bytes
19ab982 e92b993 19ab982 e92b993 19ab982 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 |
---
title: Neural Pong
emoji: 🎮
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
license: mit
---
# Neural Pong
A real-time Pong game where frames are generated by a diffusion model trained with rectified flow matching. Control the blue paddle using arrow keys or WASD to play!
## Features
- **Real-time frame generation**: Uses a frame-autoregressive transformer with diffusion sampling
- **Interactive gameplay**: Control the paddle with keyboard inputs
- **Configurable parameters**: Adjust FPS and diffusion steps
- **Low-latency streaming**: Achieves ~16 FPS with 4 diffusion steps
## How to Play
1. Wait for the model to load (you'll see a loading spinner)
2. Click "Start Stream" to begin generating frames
3. Use **Arrow Keys** or **WASD** to control the blue paddle:
- **Up/W**: Move paddle up
- **Down/S**: Move paddle down
4. Adjust the FPS and diffusion steps using the controls
5. Click "Stop Stream" when done
## Technical Details
This demo uses a small transformer model trained with rectified flow matching to simulate Pong game frames conditioned on user inputs. The model generates 24×24 pixel frames in real-time using diffusion sampling with configurable steps. Performance targets ~16 FPS with 4 diffusion steps on GPU hardware.
## Model Architecture
- Frame-autoregressive transformer
- Rectified flow matching training
- Caching for efficient inference
- GPU-accelerated generation
|