Games
Collection
Diffusion models that I trained to render a game with user input as conditioning. • 4 items • Updated • 1
An action-conditioned Breakout world model. Hold A / D (or the arrow keys) and the model generates the next frame.
128x128 at 6 FPS with 12 frames of latent history. The model has about 70M parameters and uses the frozen SDXL VAE from madebyollin/sdxl-vae-fp16-fix.
Requires a CUDA GPU with BF16 support.
pip install torch numpy pillow safetensors huggingface_hub diffusers
hf download kerzgrr/diffusionbreakout live_infer.py --local-dir .
python live_infer.py --steps 4
To keep the weights in a local folder:
hf download kerzgrr/diffusionbreakout \
--local-dir checkpoints/diffusionbreakout \
--include "ema.safetensors" \
--include "config.json" \
--include "live_infer.py"
python checkpoints/diffusionbreakout/live_infer.py \
--local-dir checkpoints/diffusionbreakout \
--steps 4 \
--window-scale 6
Click the window for focus. A / Left moves left; D / Right moves right.
| file | contents |
|---|---|
ema.safetensors |
action-conditioned EMA weights |
config.json |
model, video, codec, and training settings |
live_infer.py |
single-file live inference |
demo.gif |
live rollout |
Actions are encoded as [left, right].