from diffusers import FluxPipeline def print_hi(name): print(f'Hi, {name}') torch.set_default_device("mps") pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(device="mps") # pipe.enable_model_cpu_offload() # save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power prompt = "A cat holding a sign that says hello world" image = pipe( prompt, guidance_scale=0.0, num_inference_steps=16, max_sequence_length=256, generator=torch.Generator("mps").manual_seed(0) ).images[0] image.save("flux-schnell.png") if __name__ == '__main__': print_hi('PyCharm')