cestralDiscreteScheduler

pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config)

generator = torch.Generator(device="cuda").manual_seed(8)
image = pipeline(prompt, generator=generator, num_inference_steps=30).images[0]
image    DPMSolverMultistepScheduler gives a reasonable speed/quality trade-off and can be run with as little as 20 steps.   Copied from diffusers import DPMSolverMultistepScheduler

pipeline