app.py

import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
import torch

# Load the pipeline
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev")
pipe.load_lora_weights("EvanZhouDev/open-genmoji")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

def infer(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
):
    # Handle seed randomization
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.manual_seed(seed)

    # Generate the image using the pipeline
    result = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt if negative_prompt else None,
        width=width,
        height=height,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=generator,
    ).images[0]

    return result, seed

examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # Text-to-Image Gradio Template")

        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )

            run_button = gr.Button("Run", scale=0, variant="primary")

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=True,
            )

            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=512,  # Default width for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,  # Default height for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=7.5,  # Default guidance scale for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=25,  # Default number of inference steps for your model
                )

        gr.Examples(examples=examples, inputs=[prompt])

        run_button.click(
            infer,
            inputs=[
                prompt,
                negative_prompt,
                seed,
                randomize_seed,
                width,
                height,
                guidance_scale,
                num_inference_steps,
            ],
            outputs=[result, seed],
        )

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