# demo & simple test
def main():
    from diffusers.utils import load_image

    pipe = StableDiffusionMultiControlNetPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5", safety_checker=None, torch_dtype=torch.float16
    ).to("cuda")
    pipe.enable_xformers_memory_efficient_attention()

    controlnet_canny = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16).to(
        "cuda"
    )
    controlnet_pose = ControlNetModel.from_pretrained(
        "lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16
    ).to("cuda")

    canny_left = load_image("https://huggingface.co/takuma104/controlnet_dev/resolve/main/vermeer_left.png")
    canny_right = load_image("https://huggingface.co/takuma104/controlnet_dev/resolve/main/vermeer_right.png")
    pose_right = load_image("https://huggingface.co/takuma104/controlnet_dev/resolve/main/pose_right.png")

    image = pipe(
        prompt="best quality, extremely detailed",
        negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
        processors=[
            ControlNetProcessor(controlnet_canny, canny_left),
            ControlNetProcessor(controlnet_canny, canny_right),
        ],
        generator=torch.Generator(device="cpu").manual_seed(0),
        num_inference_steps=30,
        width=512,
        height=512,
    ).images[0]
    image.save("/tmp/canny_left_right.png")

    image = pipe(
        prompt="best quality, extremely detailed",
        negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
        processors=[
            ControlNetProcessor(controlnet_canny, canny_left),
            ControlNetProcessor(controlnet_pose, pose_right),
        ],
        generator=torch.Generator(device="cpu").manual_seed(0),
        num_inference_steps=30,
        width=512,
        height=512,
    ).images[0]
    image.save("/tmp/canny_left_pose_right.png")


if __name__ == "__main__":
    main()