app.py

import gradio as gr

from src.const import MODEL_CHOICES
from src.example import EXAMPLES
from src.inference import inference


def build_interface():
    """Build Gradio Interface"""

    theme = gr.themes.Default(primary_hue=gr.themes.colors.emerald)

    with gr.Blocks(theme=theme) as interface:
        gr.Markdown(f"# Stable Diffusion Demo")

        with gr.Row():
            with gr.Column():
                prompt = gr.Text(label="Prompt", placeholder="Enter a prompt here")

                model_id = gr.Dropdown(
                    label="Model ID",
                    choices=MODEL_CHOICES,
                    value="stabilityai/stable-diffusion-3-medium-diffusers",
                )

                # Additional Input Settings
                with gr.Accordion("Additional Settings", open=False):
                    negative_prompt = gr.Text(label="Negative Prompt", value="", )

                    with gr.Row():
                        width = gr.Number(label="Width", value=512, step=64, minimum=64, maximum=2048)
                        height = gr.Number(label="Height", value=512, step=64, minimum=64, maximum=2048)
                        num_images = gr.Number(label="Num Images", value=4, minimum=1, maximum=10, step=1)
                        seed = gr.Number(label="Seed", value=8888, step=1)

                    guidance_scale = gr.Slider(label="Guidance Scale", value=7.5, step=0.5, minimum=0, maximum=10)
                    num_inference_step = gr.Slider(
                        label="Num Inference Steps", value=50, minimum=1, maximum=100, step=2
                    )

                    with gr.Row():
                        use_safety_checker = gr.Checkbox(value=True, label='Use Safety Checker')
                        use_model_offload = gr.Checkbox(value=False, label='Use Model Offload')

                with gr.Accordion(label='Notes', open=False):
                    # language=HTML
                    notes = gr.HTML(
                        """
                            <h2>Negative Embeddings</h2>
                            <p>If you want to use negative embedding, use the following tokens in the prompt.</p>
                            <ul>
                                <li><a href='https://civitai.com/models/59614/badneganatomy-textual-inversion'>BadNegAnatomyV1-neg</a></li>
                                <li><a href='https://civitai.com/models/4629/deep-negative-v1x'>DeepNegativeV1</a> </li>
                                <li><a href='https://civitai.com/models/7808/easynegative'>EasyNegative</a></li>
                                <li><a href='https://civitai.com/models/56519/negativehand-negative-embedding'>negative_hand-neg</a></li>
                            </ul>
                        """
                    )

            with gr.Column():
                output_image = gr.Image(label="Image", type="pil")

        inputs = [
            prompt,
            model_id,
            negative_prompt,
            width,
            height,
            guidance_scale,
            num_inference_step,
            num_images,
            use_safety_checker,
            use_model_offload,
            seed,
        ]

        btn = gr.Button("Generate", variant='primary')
        btn.click(
            fn=inference,
            inputs=inputs,
            outputs=output_image
        )

        gr.Examples(
            examples=EXAMPLES,
            inputs=inputs,
            outputs=output_image,
            fn=inference,
            cache_examples='lazy'
        )
    return interface


if __name__ == "__main__":
    iface = build_interface()
    iface.queue().launch()