src/flux/cli.py

import os
import re
import time
from dataclasses import dataclass
from glob import iglob

import torch
from einops import rearrange
from fire import Fire
from PIL import ExifTags, Image

from flux.sampling import denoise, get_noise, get_schedule, prepare, unpack
from flux.util import (configs, embed_watermark, load_ae, load_clip,
                       load_flow_model, load_t5)
from transformers import pipeline

NSFW_THRESHOLD = 0.85

@dataclass
class SamplingOptions:
    prompt: str
    width: int
    height: int
    num_steps: int
    guidance: float
    seed: int | None


def parse_prompt(options: SamplingOptions) -> SamplingOptions | None:
    user_question = "Next prompt (write /h for help, /q to quit and leave empty to repeat):\n"
    usage = (
        "Usage: Either write your prompt directly, leave this field empty "
        "to repeat the prompt or write a command starting with a slash:\n"
        "- '/w <width>' will set the width of the generated image\n"
        "- '/h <height>' will set the height of the generated image\n"
        "- '/s <seed>' sets the next seed\n"
        "- '/g <guidance>' sets the guidance (flux-dev only)\n"
        "- '/n <steps>' sets the number of steps\n"
        "- '/q' to quit"
    )

    while (prompt := input(user_question)).startswith("/"):
        if prompt.startswith("/w"):
            if prompt.count(" ") != 1:
                print(f"Got invalid command '{prompt}'\n{usage}")
                continue
            _, width = prompt.split()
            options.width = 16 * (int(width) // 16)
            print(
                f"Setting resolution to {options.width} x {options.height} "
                f"({options.height *options.width/1e6:.2f}MP)"
            )
        elif prompt.startswith("/h"):
            if prompt.count(" ") != 1:
                print(f"Got invalid command '{prompt}'\n{usage}")
                continue
            _, height = prompt.split()
            options.height = 16 * (int(height) // 16)
            print(
                f"Setting resolution to {options.width} x {options.height} "
                f"({options.height *options.width/1e6:.2f}MP)"
            )
        elif prompt.startswith("/g"):
            if prompt.count(" ") != 1:
                print(f"Got invalid command '{prompt}'\n{usage}")
                continue
            _, guidance = prompt.split()
            options.guidance = float(guidance)
            print(f"Setting guidance to {options.guidance}")
        elif prompt.startswith("/s"):
            if prompt.count(" ") != 1:
                print(f"Got invalid command '{prompt}'\n{usage}")
                continue
            _, seed = prompt.split()
            options.seed = int(seed)
            print(f"Setting seed to {options.seed}")
        elif prompt.startswith("/n"):
            if prompt.count(" ") != 1:
                print(f"Got invalid command '{prompt}'\n{usage}")
                continue
            _, steps = prompt.split()
            options.num_steps = int(steps)
            print(f"Setting seed to {options.num_steps}")
        elif prompt.startswith("/q"):
            print("Quitting")
            return None
        else:
            if not prompt.startswith("/h"):
                print(f"Got invalid command '{prompt}'\n{usage}")
            print(usage)
    if prompt != "":
        options.prompt = prompt
    return options


@torch.inference_mode()
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,
):
    """
    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
    """
    nsfw_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection")

    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)

    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)
        t1 = time.perf_counter()

        fn = output_name.format(idx=idx)
        print(f"Done in {t1 - t0:.1f}s. Saving {fn}")
        # bring into PIL format and save
        x = x.clamp(-1, 1)
        x = embed_watermark(x.float())
        x = rearrange(x[0], "c h w -> h w c")

        img = Image.fromarray((127.5 * (x + 1.0)).cpu().byte().numpy())
        nsfw_score = [x["score"] for x in nsfw_classifier(img) if x["label"] == "nsfw"][0]
        
        if nsfw_score < NSFW_THRESHOLD:
            exif_data = Image.Exif()
            exif_data[ExifTags.Base.Software] = "AI generated;txt2img;flux"
            exif_data[ExifTags.Base.Make] = "Black Forest Labs"
            exif_data[ExifTags.Base.Model] = name
            if add_sampling_metadata:
                exif_data[ExifTags.Base.ImageDescription] = prompt
            img.save(fn, exif=exif_data, quality=95, subsampling=0)
            idx += 1
        else:
            print("Your generated image may contain NSFW content.")

        if loop:
            print("-" * 80)
            opts = parse_prompt(opts)
        else:
            opts = None


def app():
    Fire(main)


if __name__ == "__main__":
    app()