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 = """
/* General Styles */
#col-container {
    margin: 0 auto;
    max-width: 640px;
    font-family: 'Arial', sans-serif;
    color: #333;
    background-color: #f0f4f8; /* Light gray background for better contrast */
    border-radius: 15px;
    padding: 20px;
}

#header {
    text-align: center;
    color: #1f5f99; /* Veshup Blue */
}

#title {
    font-size: 36px;
    font-weight: bold;
    margin-bottom: 10px;
}

#subtitle {
    font-size: 18px;
    color: #555;
    margin-bottom: 30px;
}

.gradio-button {
    background-color: #1f5f99;
    color: white;
    font-weight: bold;
    border-radius: 8px;
}

.gradio-button:hover {
    background-color: #155b89;
}

.gradio-slider {
    width: 100%;
}

.gradio-checkbox label {
    font-weight: normal;
}

.gradio-markdown {
    font-size: 16px;
    line-height: 1.6;
}

/* Dark Mode adjustments for browser default theme */
@media (prefers-color-scheme: dark) {
    #col-container {
        background-color: #2e2e2e; /* Dark background for dark mode */
        color: #e0e0e0; /* Light text for dark mode */
    }

    #header {
        color: #a5c4f6; /* Lighter blue for dark mode */
    }

    .gradio-button {
        background-color: #4f89b0;
    }

    .gradio-button:hover {
        background-color: #3a6a8b;
    }

    .gradio-slider,
    .gradio-checkbox {
        background-color: #444; /* Darker elements in dark mode */
    }

    .gradio-markdown {
        color: #d1d1d1; /* Lighter text for markdown */
    }
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("<div id='header'><h1 id='title'>Veginator: Veshup's Image Generation AI</h1><p id='subtitle'>Create stunning images with just a prompt. Powered by cutting-edge AI technology.</p></div>")

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

            run_button = gr.Button("Generate Image", scale=0, variant="primary", elem_classes="gradio-button")

        result = gr.Image(label="Generated Image", 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 if needed",
                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,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=0.0,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of Inference Steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=2,  # Replace with defaults that work for your model
                )

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