app.py

import random

import gradio as gr
import numpy as np
import PIL.Image
import spaces
import torch
from diffusers import AutoencoderTiny, DiffusionPipeline

dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"

taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048


@spaces.GPU(duration=75)
def infer(
    prompt: str,
    seed: int,
    randomize_seed: bool,
    width: int = 1024,
    height: int = 1024,
    guidance_scale: float = 3.5,
    num_inference_steps: int = 28,
    progress: gr.Progress = gr.Progress(track_tqdm=True),  # noqa: ARG001, B008
) -> tuple[PIL.Image.Image, int]:
    """Generate an image from a text prompt using the FLUX.1 [dev] model.

    Note:
        - Prompts must be written in English. Other languages are not currently supported.
        - Prompts are limited to 77 tokens due to CLIP tokenizer constraints.

    Args:
        prompt: A text prompt in English to guide the image generation. Limited to 77 tokens.
        seed: The seed value used for reproducible image generation.
        randomize_seed: If True, overrides the seed with a randomly generated one.
        width: Width of the output image in pixels. Defaults to 1024.
        height: Height of the output image in pixels. Defaults to 1024.
        guidance_scale: Controls how strongly the model follows the prompt.
            Higher values lead to images more closely aligned with the prompt. Defaults to 3.5.
        num_inference_steps: Number of denoising steps during generation. Higher values can improve quality. Defaults to 28.
        progress: (Internal) Progress tracker for UI integration; should not be manually set by users.

    Returns:
        A tuple containing the generated image and the seed value used.
    """
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)  # noqa: S311
    generator = torch.Generator().manual_seed(seed)

    image = pipe(
        prompt=prompt,
        width=width,
        height=height,
        num_inference_steps=num_inference_steps,
        generator=generator,
        guidance_scale=guidance_scale,
    ).images[0]
    return image, seed


def run_example(prompt: str) -> tuple[PIL.Image.Image, int]:
    return infer(prompt, seed=42, randomize_seed=False)


examples = [
    "a tiny astronaut hatching from an egg on the moon",
    "a cat holding a sign that says hello world",
    "an anime illustration of a wiener schnitzel",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("""# FLUX.1 [dev]
12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)
[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
        """)

        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                submit_btn=True,
            )
        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=1,
                    maximum=15,
                    step=0.1,
                    value=3.5,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=28,
                )

        gr.Examples(
            examples=examples,
            fn=run_example,
            inputs=prompt,
            outputs=[result, seed],
        )

    prompt.submit(
        fn=infer,
        inputs=[prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs=[result, seed],
    )

if __name__ == "__main__":
    demo.launch(mcp_server=True)