data/flux/demo_st.py

import os
import re
import time
from glob import iglob
from io import BytesIO

import streamlit as st
import torch
from einops import rearrange
from fire import Fire
from PIL import ExifTags, Image
from st_keyup import st_keyup
from torchvision import transforms
from transformers import pipeline

from flux.cli import SamplingOptions
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,
)

NSFW_THRESHOLD = 0.85


@st.cache_resource()
def get_models(name: str, device: torch.device, offload: bool, is_schnell: bool):
    t5 = load_t5(device, max_length=256 if is_schnell else 512)
    clip = load_clip(device)
    model = load_flow_model(name, device="cpu" if offload else device)
    ae = load_ae(name, device="cpu" if offload else device)
    nsfw_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection", device=device)
    return model, ae, t5, clip, nsfw_classifier


def get_image() -> torch.Tensor | None:
    image = st.file_uploader("Input", type=["jpg", "JPEG", "png"])
    if image is None:
        return None
    image = Image.open(image).convert("RGB")

    transform = transforms.Compose(
        [
            transforms.ToTensor(),
            transforms.Lambda(lambda x: 2.0 * x - 1.0),
        ]
    )
    img: torch.Tensor = transform(image)
    return img[None, ...]


@torch.inference_mode()
def main(

    device: str = "cuda" if torch.cuda.is_available() else "cpu",

    offload: bool = False,

    output_dir: str = "output",

):
    torch_device = torch.device(device)
    names = list(configs.keys())
    name = st.selectbox("Which model to load?", names)
    if name is None or not st.checkbox("Load model", False):
        return

    is_schnell = name == "flux-schnell"
    model, ae, t5, clip, nsfw_classifier = get_models(
        name,
        device=torch_device,
        offload=offload,
        is_schnell=is_schnell,
    )

    do_img2img = (
        st.checkbox(
            "Image to Image",
            False,
            disabled=is_schnell,
            help="Partially noise an image and denoise again to get variations.\n\nOnly works for flux-dev",
        )
        and not is_schnell
    )
    if do_img2img:
        init_image = get_image()
        if init_image is None:
            st.warning("Please add an image to do image to image")
        image2image_strength = st.number_input("Noising strength", min_value=0.0, max_value=1.0, value=0.8)
        if init_image is not None:
            h, w = init_image.shape[-2:]
            st.write(f"Got image of size {w}x{h} ({h*w/1e6:.2f}MP)")
        resize_img = st.checkbox("Resize image", False) or init_image is None
    else:
        init_image = None
        resize_img = True
        image2image_strength = 0.0

    # allow for packing and conversion to latent space
    width = int(
        16 * (st.number_input("Width", min_value=128, value=1360, step=16, disabled=not resize_img) // 16)
    )
    height = int(
        16 * (st.number_input("Height", min_value=128, value=768, step=16, disabled=not resize_img) // 16)
    )
    num_steps = int(st.number_input("Number of steps", min_value=1, value=(4 if is_schnell else 50)))
    guidance = float(st.number_input("Guidance", min_value=1.0, value=3.5, disabled=is_schnell))
    seed_str = st.text_input("Seed", disabled=is_schnell)
    if seed_str.isdecimal():
        seed = int(seed_str)
    else:
        st.info("No seed set, set to positive integer to enable")
        seed = None
    save_samples = st.checkbox("Save samples?", not is_schnell)
    add_sampling_metadata = st.checkbox("Add sampling parameters to metadata?", True)

    default_prompt = (
        "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'
    )
    prompt = st_keyup("Enter a prompt", value=default_prompt, debounce=300, key="interactive_text")

    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

    rng = torch.Generator(device="cpu")

    if "seed" not in st.session_state:
        st.session_state.seed = rng.seed()

    def increment_counter():
        st.session_state.seed += 1

    def decrement_counter():
        if st.session_state.seed > 0:
            st.session_state.seed -= 1

    opts = SamplingOptions(
        prompt=prompt,
        width=width,
        height=height,
        num_steps=num_steps,
        guidance=guidance,
        seed=seed,
    )

    if name == "flux-schnell":
        cols = st.columns([5, 1, 1, 5])
        with cols[1]:
            st.button("↩", on_click=increment_counter)
        with cols[2]:
            st.button("↪", on_click=decrement_counter)
    if is_schnell or st.button("Sample"):
        if is_schnell:
            opts.seed = st.session_state.seed
        elif opts.seed is None:
            opts.seed = rng.seed()
        print(f"Generating '{opts.prompt}' with seed {opts.seed}")
        t0 = time.perf_counter()

        if init_image is not None:
            if resize_img:
                init_image = torch.nn.functional.interpolate(init_image, (opts.height, opts.width))
            else:
                h, w = init_image.shape[-2:]
                init_image = init_image[..., : 16 * (h // 16), : 16 * (w // 16)]
                opts.height = init_image.shape[-2]
                opts.width = init_image.shape[-1]
            if offload:
                ae.encoder.to(torch_device)
            init_image = ae.encode(init_image.to(torch_device))
            if offload:
                ae = ae.cpu()
                torch.cuda.empty_cache()

        # prepare input
        x = get_noise(
            1,
            opts.height,
            opts.width,
            device=torch_device,
            dtype=torch.bfloat16,
            seed=opts.seed,
        )
        # divide pixel space by 16**2 to account for latent space conversion
        timesteps = get_schedule(
            opts.num_steps,
            (x.shape[-1] * x.shape[-2]) // 4,
            shift=(not is_schnell),
        )
        if init_image is not None:
            t_idx = int((1 - image2image_strength) * num_steps)
            t = timesteps[t_idx]
            timesteps = timesteps[t_idx:]
            x = t * x + (1.0 - t) * init_image.to(x.dtype)

        if offload:
            t5, clip = t5.to(torch_device), clip.to(torch_device)
        inp = prepare(t5=t5, clip=clip, img=x, prompt=opts.prompt)

        # 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 offload:
            ae.decoder.cpu()
            torch.cuda.empty_cache()

        t1 = time.perf_counter()

        fn = output_name.format(idx=idx)
        print(f"Done in {t1 - t0:.1f}s.")
        # 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:
            buffer = BytesIO()
            exif_data = Image.Exif()
            if init_image is None:
                exif_data[ExifTags.Base.Software] = "AI generated;txt2img;flux"
            else:
                exif_data[ExifTags.Base.Software] = "AI generated;img2img;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(buffer, format="jpeg", exif=exif_data, quality=95, subsampling=0)

            img_bytes = buffer.getvalue()
            if save_samples:
                print(f"Saving {fn}")
                with open(fn, "wb") as file:
                    file.write(img_bytes)
                idx += 1

            st.session_state["samples"] = {
                "prompt": opts.prompt,
                "img": img,
                "seed": opts.seed,
                "bytes": img_bytes,
            }
            opts.seed = None
        else:
            st.warning("Your generated image may contain NSFW content.")
            st.session_state["samples"] = None

    samples = st.session_state.get("samples", None)
    if samples is not None:
        st.image(samples["img"], caption=samples["prompt"])
        st.download_button(
            "Download full-resolution",
            samples["bytes"],
            file_name="generated.jpg",
            mime="image/jpg",
        )
        st.write(f"Seed: {samples['seed']}")


def app():
    Fire(main)


if __name__ == "__main__":
    app()