app.py

import gradio as gr
import numpy as np
import random

# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)

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


# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    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,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
    ).images[0]

    return image, 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 = """
:root {
    --primary: #6e6af0;
    --secondary: #f5f5f7;
    --accent: #f5f5f7;
    --text: #1e1e1e;
    --shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
}

#col-container {
    margin: 0 auto;
    max-width: 800px;
    padding: 20px;
}

.header {
    text-align: center;
    margin-bottom: 20px;
}

.header h1 {
    font-size: 2.5rem;
    font-weight: 700;
    color: var(--primary);
    margin-bottom: 10px;
}

.prompt-container {
    background: white;
    border-radius: 12px;
    padding: 20px;
    box-shadow: var(--shadow);
    margin-bottom: 20px;
}

.result-container {
    background: white;
    border-radius: 12px;
    padding: 20px;
    box-shadow: var(--shadow);
    margin-bottom: 20px;
}

.advanced-settings {
    background: white;
    border-radius: 12px;
    padding: 20px;
    box-shadow: var(--shadow);
}

.btn-primary {
    background: var(--primary) !important;
    border: none !important;
}

.btn-primary:hover {
    opacity: 0.9 !important;
}

.examples {
    margin-top: 20px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        with gr.Column(visible=True) as header:
            gr.Markdown(
                """
                <div class="header">
                    <h1>Text-to-Image Generator</h1>
                </div>
                """,
                elem_classes="header"
            )

        with gr.Column(elem_classes="prompt-container"):
            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", elem_classes="btn-primary")

        with gr.Column(elem_classes="result-container"):
            result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False, elem_classes="advanced-settings"):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=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=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=0.0,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=2,
                )

        gr.Examples(examples=examples, inputs=[prompt], elem_classes="examples")
    
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )

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