app.py

import spaces
import time
import os

import gradio as gr
import torch
from einops import rearrange
from PIL import Image

from flux.details import SamplingOptions
from flux.sampling import denoise, get_noise, get_schedule, prepare, unpack
from flux.util import load_ae, load_clip, load_flow_model, load_t5
from eva_clip.model_configs.fluxpipeline import ToonMagePipeline
from toonmage.utils import resize_numpy_image_long


def get_models(name: str, device: torch.device, offload: bool):
    t5 = load_t5(device, max_length=128)
    clip = load_clip(device)
    model = load_flow_model(name, device="cpu" if offload else device)
    model.eval()
    ae = load_ae(name, device="cpu" if offload else device)
    return model, ae, t5, clip


class FluxGenerator:
    def __init__(self):
        self.device = torch.device('cuda')
        self.offload = False
        self.model_name = 'flux-dev'
        self.model, self.ae, self.t5, self.clip = get_models(
            self.model_name,
            device=self.device,
            offload=self.offload,
        )
        self.toonmage_model = ToonMagePipeline(self.model, 'cuda', weight_dtype=torch.bfloat16)
        self.toonmage_model.load_pretrain()


flux_generator = FluxGenerator()


@spaces.GPU
@torch.inference_mode()
def generate_image(
        width,
        height,
        num_steps,
        start_step,
        guidance,
        seed,
        prompt,
        id_image=None,
        id_weight=1.0,
        neg_prompt="",
        true_cfg=1.0,
        timestep_to_start_cfg=1,
        max_sequence_length=128,
):
    flux_generator.t5.max_length = max_sequence_length

    seed = int(seed)
    if seed == -1:
        seed = None

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

    if opts.seed is None:
        opts.seed = torch.Generator(device="cpu").seed()
    print(f"Generating '{opts.prompt}' with seed {opts.seed}")
    t0 = time.perf_counter()

    use_true_cfg = abs(true_cfg - 1.0) > 1e-2

    if id_image is not None:
        id_image = resize_numpy_image_long(id_image, 1024)
        id_embeddings, uncond_id_embeddings = flux_generator.toonmage_model.get_id_embedding(id_image, cal_uncond=use_true_cfg)
    else:
        id_embeddings = None
        uncond_id_embeddings = None

    print(id_embeddings)

    # prepare input
    x = get_noise(
        1,
        opts.height,
        opts.width,
        device=flux_generator.device,
        dtype=torch.bfloat16,
        seed=opts.seed,
    )
    print(x)
    timesteps = get_schedule(
        opts.num_steps,
        x.shape[-1] * x.shape[-2] // 4,
        shift=True,
    )

    if flux_generator.offload:
        flux_generator.t5, flux_generator.clip = flux_generator.t5.to(flux_generator.device), flux_generator.clip.to(flux_generator.device)
    inp = prepare(t5=flux_generator.t5, clip=flux_generator.clip, img=x, prompt=opts.prompt)
    inp_neg = prepare(t5=flux_generator.t5, clip=flux_generator.clip, img=x, prompt=neg_prompt) if use_true_cfg else None

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

    # denoise initial noise
    x = denoise(
        flux_generator.model, **inp, timesteps=timesteps, guidance=opts.guidance, id=id_embeddings, id_weight=id_weight,
        start_step=start_step, uncond_id=uncond_id_embeddings, true_cfg=true_cfg,
        timestep_to_start_cfg=timestep_to_start_cfg,
        neg_txt=inp_neg["txt"] if use_true_cfg else None,
        neg_txt_ids=inp_neg["txt_ids"] if use_true_cfg else None,
        neg_vec=inp_neg["vec"] if use_true_cfg else None,
    )

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

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

    if flux_generator.offload:
        flux_generator.ae.decoder.cpu()
        torch.cuda.empty_cache()

    t1 = time.perf_counter()

    print(f"Done in {t1 - t0:.1f}s.")
    # bring into PIL format
    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())
    return img, str(opts.seed), flux_generator.toonmage_model.debug_img_list

MARKDOWN = """
This demo utilizes <a href="https://huggingface.co/black-forest-labs/FLUX.1-dev">FLUX Pipeline</a> for Image to Image Translation

**Tips**

- Smaller value of timestep to start inserting ID would lead to higher fidelity, however, it will reduce the editability; and vice versa.
Its value range is from 0 - 4. If you want to generate a stylized scene; use the value of 0 - 1. If you want to generate a photorealistic image; use the value of 4. 

-It is recommended to use fake CFG by setting the true CFG scale value to 1 while you can vary the guidance scale. However, in a few cases, utilizing a true CFG can yield better results.

Try out with different prompts using your image and do provide your feedback.

**Demo by [Sunder Ali Khowaja](https://sander-ali.github.io) - [X](https://x.com/SunderAKhowaja) -[Github](https://github.com/sander-ali) -[Hugging Face](https://huggingface.co/SunderAli17)**
"""

theme = gr.themes.Soft(
    font=[gr.themes.GoogleFont('Source Code Pro'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
)
js_func = """
function refresh() {
    const url = new URL(window.location);
    if (url.searchParams.get('__theme') !== 'dark') {
        url.searchParams.set('__theme', 'dark');
        window.location.href = url.href;
    }
}
"""


def create_demo(args, model_name: str, device: str = "cuda" if torch.cuda.is_available() else "cpu",
                offload: bool = False):
    with gr.Blocks(js = js_func, theme = theme) as demo:
        gr.Markdown(MARKDOWN)

        with gr.Row():
            with gr.Column():
                prompt = gr.Textbox(label="Prompt", value="portrait, color, cinematic")
                id_image = gr.Image(label="ID Image")
                id_weight = gr.Slider(0.0, 3.0, 1, step=0.05, label="id weight")

                width = gr.Slider(256, 1536, 896, step=16, label="Width")
                height = gr.Slider(256, 1536, 1152, step=16, label="Height")
                num_steps = gr.Slider(1, 20, 20, step=1, label="Number of steps")
                start_step = gr.Slider(0, 10, 0, step=1, label="timestep to start inserting ID")
                guidance = gr.Slider(1.0, 10.0, 4, step=0.1, label="Guidance")
                seed = gr.Textbox(-1, label="Seed (-1 for random)")
                max_sequence_length = gr.Slider(128, 512, 128, step=128,
                                                label="max_sequence_length for prompt (T5), small will be faster")

                with gr.Accordion("Advanced Options (True CFG, true_cfg_scale=1 means use fake CFG, >1 means use true CFG, if using true CFG, we recommend set the guidance scale to 1)", open=False):    # noqa E501
                    neg_prompt = gr.Textbox(
                        label="Negative Prompt",
                        value="bad quality, worst quality, text, signature, watermark, extra limbs")
                    true_cfg = gr.Slider(1.0, 10.0, 1, step=0.1, label="true CFG scale")
                    timestep_to_start_cfg = gr.Slider(0, 20, 1, step=1, label="timestep to start cfg", visible=args.dev)

                generate_btn = gr.Button("Generate")

            with gr.Column():
                output_image = gr.Image(label="Generated Image")
                seed_output = gr.Textbox(label="Used Seed")
                intermediate_output = gr.Gallery(label='Output', elem_id="gallery", visible=args.dev)


        with gr.Row(), gr.Column():
                gr.Markdown("## Examples")
                example_inps = [
                    [
                        'a high quality digital avatar, eating icecream',
                        'sample_img/image1.png',
                        4, 4, 2680261499100305976, 1
                    ],
                    [
                        'white-haired man with vr technology', 
                        'sample_img/image1.png',
                        4, 4, 6349424134217931066, 1
                    ],
                    [
                        'a young child is eating Icecream',
                        'sample_img/image1.png',
                        4, 4, 10606046113565776207, 1
                    ],
                    [
                        'a digital avatar with mountains and lakes in the background',
                        'sample_img/test1.jpg',
                        0, 4, 2410129802683836089, 1
                    ],
                    [
                        'portrait, candle light',
                        'sample_img/test1.jpg',
                        4, 4, 17522759474323955700, 1
                    ],
                    [
                        'profile shot dark photo of a 25-year-old male with smoke escaping from his mouth, the backlit smoke gives the image an ephemeral quality, natural face, natural eyebrows, natural skin texture, award winning photo, highly detailed face, atmospheric lighting, film grain, monochrome',  # noqa E501
                        'sample_img/test1.jpg',
                        4, 4, 17733156847328193625, 1
                    ],
                    [
                        'DC comics, Flash',
                        'sample_img/test1.jpg',
                        1, 4, 13223174453874179686, 1
                    ],
                    [
                        'portrait, pixar',
                        'sample_img/test1.jpg',
                        1, 4, 9445036702517583939, 1
                    ],
                ]
                gr.Examples(examples=example_inps, inputs=[prompt, id_image, start_step, guidance, seed, true_cfg],
                            label='fake CFG')

                example_inps = [
                    [
                        'portrait, made of ice sculpture',
                        'sample_img/test1.jpg',
                        1, 1, 3811899118709451814, 5
                    ],
                ]
                gr.Examples(examples=example_inps, inputs=[prompt, id_image, start_step, guidance, seed, true_cfg],
                            label='true CFG')


        generate_btn.click(
            fn=generate_image,
            inputs=[width, height, num_steps, start_step, guidance, seed, prompt, id_image, id_weight, neg_prompt,
                    true_cfg, timestep_to_start_cfg, max_sequence_length],
            outputs=[output_image, seed_output, intermediate_output],
        )

    return demo


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description="ToonMage with FLUX")
    parser.add_argument("--name", type=str, default="flux-dev", choices=list('flux-dev'),
                        help="currently only support flux-dev")
    parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu",
                        help="Device to use")
    parser.add_argument("--offload", action="store_true", help="Offload model to CPU when not in use")
    parser.add_argument("--port", type=int, default=8080, help="Port to use")
    parser.add_argument("--dev", action='store_true', help="Development mode")
    parser.add_argument("--pretrained_model", type=str, help='for development')
    args = parser.parse_args()

    import huggingface_hub
    huggingface_hub.login(os.getenv('HF_TOKEN'))

    demo = create_demo(args, args.name, args.device, args.offload)
    demo.launch()