llgraph=True)

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"

# First call to `pipe` is slow, subsequent ones are faster.
image = pipe(prompt, num_inference_steps=30).images[0] torch.compile offers different backends and modes. For maximum inference speed, use “max-autotune” for the inductor backend. “max-autotune” uses CUDA graphs and optimizes the compilation graph specifically for latency. CUDA graphs great