Games
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Diffusion models that I trained to render a game with user input as conditioning. • 4 items • Updated • 1
Diffusion Pong Base
Unconditional Pong world model.
128x128 at 6 FPS with a 12-frame latent history. Frozen SDXL VAE (madebyollin/sdxl-vae-fp16-fix). Needs a CUDA GPU with BF16. ~59M DiT params.
Run it
pip install torch numpy pillow safetensors huggingface_hub diffusers
hf download kerzgrr/diffusionpong-base live_infer.py --local-dir .
python live_infer.py --steps 2
Or download weights locally:
hf download kerzgrr/diffusionpong-base --local-dir checkpoints/diffusionpong-base --include "ema.safetensors" --include "config.json" --include "live_infer.py"
python checkpoints/diffusionpong-base/live_infer.py --local-dir checkpoints/diffusionpong-base --steps 2 --window-scale 7 --seed 42
No paddle controls. Click the canvas to drop a yellow ball into history for a few frames if you want to poke the dynamics.
For the playable W/S version, see kerzgrr/diffusionpong.
Files
| file | what |
|---|---|
ema.safetensors |
weights |
config.json |
train-time settings |
live_infer.py |
live rollout script |
preview.png |
validation strip from step 1050 |
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