app.py

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

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

pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)

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


def get_seed(randomize_seed: bool, seed: int) -> int:
    """Determine and return the random seed to use for model generation.

    Args:
        randomize_seed (bool): If True, a random seed (an integer in [0, MAX_SEED)) is generated using NumPy's default random number generator. If False, the provided seed argument is returned as-is.
        seed (int): The seed value to use if randomize_seed is False.

    Returns:
        int: The selected seed value. If randomize_seed is True, a randomly generated integer; otherwise, the value of the seed argument.

    Notes:
        - MAX_SEED is the maximum value for a 32-bit integer (np.iinfo(np.int32).max).
        - This function is typically used to ensure reproducibility or to introduce randomness in model generation.
    """
    rng = np.random.default_rng()
    return int(rng.integers(0, MAX_SEED)) if randomize_seed else seed


@spaces.GPU
def infer(
    prompt: str,
    seed: int,
    width: int = 1024,
    height: int = 1024,
    num_inference_steps: int = 4,
    progress: gr.Progress = gr.Progress(track_tqdm=True),  # noqa: ARG001, B008
) -> PIL.Image.Image:
    """Generate an image from a text prompt using the FLUX.1 [schnell] model.

    Note:
        - Prompts must be in English. Other languages are not currently supported.
        - Prompts are limited to a maximum of 77 tokens, due to the CLIP tokenizer constraint.

    Args:
        prompt: A text prompt in English used to guide the image generation. Limited to 77 tokens.
        seed: The seed used for deterministic random number generation.
        width: Width of the generated image in pixels. Defaults to 1024.
        height: Height of the generated image in pixels. Defaults to 1024.
        num_inference_steps: Number of inference steps to perform. A higher value may improve image quality. Defaults to 4.
        progress: (Internal) Used to display progress in the UI; should not be modified by the user.

    Returns:
        A PIL.Image.Image object representing the generated image.
    """
    generator = torch.Generator().manual_seed(seed)
    return pipe(
        prompt=prompt,
        width=width,
        height=height,
        num_inference_steps=num_inference_steps,
        generator=generator,
        guidance_scale=0.0,
    ).images[0]


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


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 [schnell]
12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation
[[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)]
        """)

        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():
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=4,
                )

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

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


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