def main(
    name: str = "flux-schnell",
    width: int = 1360,
    height: int = 768,
    seed: int | None = None,
    prompt: str = (
        "a photo of a forest with mist swirling around the tree trunks. The word "
        '"FLUX" is painted over it in big, red brush strokes with visible texture'
    ),
    device: str = "cuda" if torch.cuda.is_available() else "cpu",
    num_steps: int | None = None,
    loop: bool = False,
    guidance: float = 3.5,
    offload: bool = False,
    output_dir: str = "output",
    add_sampling_metadata: bool = True,
    trt: bool = False,
    trt_transformer_precision: str = "bf16",
    **kwargs: dict | None,
):
    """
    Sample the flux model. Either interactively (set `--loop`) or run for a
    single image.

    Args:
        name: Name of the model to load
        height: height of the sample in pixels (should be a multiple of 16)
        width: width of the sample in pixels (should be a multiple of 16)
        seed: Set a seed for sampling
        output_name: where to save the output image, `{idx}` will be replaced
            by the index of the sample
        prompt: Prompt used for sampling
        device: Pytorch device
        num_steps: number of sampling steps (default 4 for schnell, 50 for guidance distilled)
        loop: start an interactive session and sample multiple times
        guidance: guidance value used for guidance distillation
        add_sampling_metadata: Add the prompt to the image Exif metadata
        trt: use TensorRT backend for optimized inference
        kwargs: additional arguments for TensorRT support
    """

    prompt = prompt.split("|")
    if len(prompt) == 1:
        prompt = prompt[0]
        additional_prompts = None
    else:
        additional_prompts = prompt[1:]
        prompt = prompt[0]

    assert not (
        (additional_prompts is not None) and loop
    ), "Do not provide additional prompts and set loop to True"

    nsfw_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection", device=device)

    if name not in configs:
        available = ", ".join(configs.keys())
        raise ValueError(f"Got unknown model name: {name}, chose from {available}")

    torch_device = torch.device(device)
    if num_steps is None:
        num_steps = 4 if name == "flux-schnell" else 50

    # allow for packing and conversion to latent space
    height = 16 * (height // 16)
    width = 16 * (width // 16)

    output_name = os.path.join(output_dir, "img_{idx}.jpg")
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)
        idx = 0
    else:
        fns = [fn for fn in iglob(output_name.format(idx="*")) if re.search(r"img_[0-9]+\.jpg$", fn)]
        if len(fns) > 0:
            idx = max(int(fn.split("_")[-1].split(".")[0]) for fn in fns) + 1
        else:
            idx = 0

    # init all components
    t5 = load_t5(torch_device, max_length=256 if name == "flux-schnell" else 512)
    clip = load_clip(torch_device)
    model = load_flow_model(name, device="cpu" if offload else torch_device)
    ae = load_ae(name, device="cpu" if offload else torch_device)

    if trt:
        # offload to CPU to save memory
        ae = ae.cpu()
        model = model.cpu()
        clip = clip.cpu()
        t5 = t5.cpu()

        torch.cuda.empty_cache()

        trt_ctx_manager = TRTManager(
            bf16=True,
            device=torch_device,
            static_batch=kwargs.get("static_batch", True),
            static_shape=kwargs.get("static_shape", True),
        )
        ae.decoder.params = ae.params
        engines = trt_ctx_manager.load_engines(
            models={
                "clip": clip,
                "transformer": model,
                "t5": t5,
                "vae": ae.decoder,
            },
            engine_dir=os.environ.get("TRT_ENGINE_DIR", "./engines"),
            onnx_dir=os.environ.get("ONNX_DIR", "./onnx"),
            opt_image_height=height,
            opt_image_width=width,
            transformer_precision=trt_transformer_precision,
        )

        torch.cuda.synchronize()

        trt_ctx_manager.init_runtime()
        # TODO: refactor. stream should be part of engine constructor maybe !!
        for _, engine in engines.items():
            engine.set_stream(stream=trt_ctx_manager.stream)

        if not offload:
            for _, engine in engines.items():
                engine.load()

            calculate_max_device_memory = trt_ctx_manager.calculate_max_device_memory(engines)
            _, shared_device_memory = cudart.cudaMalloc(calculate_max_device_memory)

            for _, engine in engines.items():
                engine.activate(device=torch_device, device_memory=shared_device_memory)

        ae = engines["vae"]
        model = engines["transformer"]
        clip = engines["clip"]
        t5 = engines["t5"]

    rng = torch.Generator(device="cpu")
    opts = SamplingOptions(
        prompt=prompt,
        width=width,
        height=height,
        num_steps=num_steps,
        guidance=guidance,
        seed=seed,
    )

    if loop:
        opts = parse_prompt(opts)

    while opts is not None:
        if opts.seed is None:
            opts.seed = rng.seed()
        print(f"Generating with seed {opts.seed}:\n{opts.prompt}")
        t0 = time.perf_counter()

        # prepare input
        x = get_noise(
            1,
            opts.height,
            opts.width,
            device=torch_device,
            dtype=torch.bfloat16,
            seed=opts.seed,
        )
        opts.seed = None
        if offload:
            ae = ae.cpu()
            torch.cuda.empty_cache()
            t5, clip = t5.to(torch_device), clip.to(torch_device)
        inp = prepare(t5, clip, x, prompt=opts.prompt)
        timesteps = get_schedule(opts.num_steps, inp["img"].shape[1], shift=(name != "flux-schnell"))

        # offload TEs to CPU, load model to gpu
        if offload:
            t5, clip = t5.cpu(), clip.cpu()
            torch.cuda.empty_cache()
            model = model.to(torch_device)

        # denoise initial noise
        x = denoise(model, **inp, timesteps=timesteps, guidance=opts.guidance)

        # offload model, load autoencoder to gpu
        if offload:
            model.cpu()
            torch.cuda.empty_cache()
            ae.decoder.to(x.device)

        # decode latents to pixel space
        x = unpack(x.float(), opts.height, opts.width)
        with torch.autocast(device_type=torch_device.type, dtype=torch.bfloat16):
            x = ae.decode(x)

        if torch.cuda.is_available():
            torch.cuda.synchronize()
        t1 = time.perf_counter()

        fn = output_name.format(idx=idx)
        print(f"Done in {t1 - t0:.1f}s. Saving {fn}")

        idx = save_image(nsfw_classifier, name, output_name, idx, x, add_sampling_metadata, prompt)

        if loop:
            print("-" * 80)
            opts = parse_prompt(opts)
        elif additional_prompts:
            next_prompt = additional_prompts.pop(0)
            opts.prompt = next_prompt
        else:
            opts = None

    if trt:
        trt_ctx_manager.stop_runtime()
Sample the flux model. Either interactively (set `--loop`) or run for a
single image.

Args:
name: Name of the model to load
height: height of the sample in pixels (should be a multiple of 16)
width: width of the sample in pixels (should be a multiple of 16)
seed: Set a seed for sampling
output_name: where to save the output image, `{idx}` will be replaced
by the index of the sample
prompt: Prompt used for sampling
device: Pytorch device
num_steps: number of sampling steps (default 4 for schnell, 50 for guidance distilled)
loop: start an interactive session and sample multiple times
guidance: guidance value used for guidance distillation
add_sampling_metadata: Add the prompt to the image Exif metadata
trt: use TensorRT backend for optimized inference
kwargs: additional arguments for TensorRT support
src/flux/cli.py