def main(
    name: str = "flux-dev",
    width: int = 1360,
    height: int = 768,
    seed: int | None = None,
    device: str = "cuda" if torch.cuda.is_available() else "cpu",
    num_steps: int | None = None,
    loop: bool = False,
    guidance: float = 2.5,
    offload: bool = False,
    output_dir: str = "output",
    add_sampling_metadata: bool = True,
    img_cond_path: str = "assets/robot.webp",
):
    """
    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
        img_cond_path: path to conditioning image (jpeg/png/webp)
    """
    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

    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)
    img_embedder = ReduxImageEncoder(torch_device)

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

    if loop:
        opts = parse_prompt(opts)
        opts = parse_img_cond_path(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_redux(
            t5,
            clip,
            x,
            prompt=opts.prompt,
            encoder=img_embedder,
            img_cond_path=opts.img_cond_path,
        )
        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()
        print(f"Done in {t1 - t0:.1f}s")

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

        if loop:
            print("-" * 80)
            opts = parse_prompt(opts)
            opts = parse_img_cond_path(opts)
        else:
            opts = 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
img_cond_path: path to conditioning image (jpeg/png/webp)
src/flux/cli_redux.py