app.py

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

# Load the model
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)

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

# Inference function
@spaces.GPU()
def infer(
    prompt,
    seed=42,
    randomize_seed=False,
    width=1024,
    height=1024,
    num_inference_steps=4,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)
    image = pipe(
        prompt=prompt,
        width=width,
        height=height,
        num_inference_steps=num_inference_steps,
        generator=generator,
        guidance_scale=0.0
    ).images[0]
    return image, seed

# Example prompts
examples = [
    "A heroic adventurer wielding a flaming sword standing on a cliff overlooking a burning battlefield",
    "A grand mystical library with ancient scrolls and a floating blue orb in the center of the room",
    "A menacing dragon perched on a mountain peak as storm clouds gather around",
]

# Custom CSS for a Dungeons & Dragons theme
css = """
body {
    background-color: #1b1b1b;
    font-family: 'Cinzel', serif; /* Fantasy-style font */
    color: #f5f5f5;
    background-image: url('https://www.transparenttextures.com/patterns/dark-matter.png'); /* Subtle texture for a medieval touch */
}

#col-container {
    margin: 0 auto;
    max-width: 550px;
    padding: 15px;
    border: 4px solid #8b4513;
    background: linear-gradient(145deg, #2e2b2a, #3a3433); /* Rustic parchment feel */
    border-radius: 15px;
    box-shadow: 0 0 20px rgba(0, 0, 0, 0.8);
}

h1, h2 {
    text-align: center;
    color: #ffd700;
    font-family: 'Uncial Antiqua', serif; /* Ancient script-like font */
    text-shadow: 3px 3px #7c5200; /* Glow effect for magical appeal */
}

button {
    background-color: #8b4513;
    border: none;
    color: #f0e6d2;
    padding: 12px 20px;
    border-radius: 8px;
    cursor: pointer;
    font-size: 18px;
    text-shadow: 1px 1px #000;
    transition: all 0.3s ease;
}

button:hover {
    background-color: #a0522d;
    box-shadow: 0 0 10px #ffd700;
}
"""

# Interface
with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        # Title and Description
        gr.Markdown(
            """
            # 🛡️ ChatDnD.net Dungeons & Dragons Image Generator ⚔️
            **Unleash Your Imagination!** Create heroes, maps, quests, and epic scenes to bring your campaigns to life. 
            Tailored for adventurers seeking inspiration or Dungeon Masters constructing their next grand story. <br>
            [Visit Our Website](https://chatdnd.net) | [Support Us](https://buymeacoffee.com/watchoutformike)
            """
        )

        # Prompt input and run button
        with gr.Row():
            prompt = gr.Textbox(
                label="🎲 Enter Your Quest:",
                lines=3,
                placeholder="Describe your scene, hero, or epic landscape..."
            )
            run_button = gr.Button("Generate Image")

        # Results
        result = gr.Image(label="🖼️ Your Legendary Vision")
        
        # Advanced settings
        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,
                )
            
            num_inference_steps = gr.Slider(
                label="Number of Inference Steps",
                minimum=1,
                maximum=50,
                step=1,
                value=4,
            )

        # Examples
        gr.Examples(
            examples=examples,
            inputs=[prompt],
            outputs=[result],
            fn=infer,
            cache_examples="lazy",
        )

    # Interactivity
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
        outputs=[result, seed]
    )

# Launch the demo
demo.launch()