app.py

# -*- coding: UTF-8 -*-
"""
@Time : 28/05/2025 16:29
@Author : xiaoguangliang
@File : app.py
@Project : Faice_text2face
"""
import gradio as gr

from inference_models.unconditional_diffusion_inference import inference_unconditional
from inference_models.class_guidance_inference import inference_class_guidance, GENDER_CHOICES
from inference_models.stable_diffusion_inference import inference_sd, MAX_SEED
from inference_api import api_unconditional, api_class_guidance, api_sd
from utils import timer

MAX_IMAGE_SIZE = 1024

examples = [
    "Portrait of a young woman with long wavy hair, soft studio lighting, high contrast, 4k resolution, professional headshot",
    "Close-up of a smiling man with sharp jawline, cinematic lighting, shallow depth of field, bokeh background",
    "Candid portrait, natural light, slight smile, outdoor background, wind-blown hair",
    "Retro 80s style portrait, neon colors, grainy texture, bold shadows, high contrast",
    "Black and white portrait of an elderly woman with wrinkles, deep shadows, textured background"
]

css = """
body {
    background: linear-gradient(135deg, #f9e2e6 0%, #e8f3fc 50%, #e2f9f2 100%);
    min-height: 100vh;
}
.gradio-container {
    background-image: url('https://lh3.googleusercontent.com/d/1c4-K7_jQ4Yz_Jl_nqe2cf3IHC0OqmE5v');
    background-repeat: no-repeat;
    background-attachment: fixed;
    background-position: center;
    background-size: cover;
}
#col-container {
    margin: 0 auto;
    max-width: 960px;
    background-color: rgba(255, 255, 255, 0.85);
    border-radius: 20px;
    box-shadow: 0 8px 16px rgba(0, 0, 0, 0.1);
    padding: 24px;
    backdrop-filter: blur(10px);
}
.gr-button-primary {
    background: linear-gradient(90deg, #6b9dfc, #8c6bfc) !important;
    border: none !important;
    transition: all 0.3s ease;
}
.gr-button-primary:hover {
    transform: translateY(-2px);
    box-shadow: 0 5px 15px rgba(108, 99, 255, 0.3);
}
.gr-form {
    border-radius: 12px;
    background-color: rgba(255, 255, 255, 0.7);
}
.gr-accordion {
    border-radius: 12px;
    overflow: hidden;
}
h1 {
    background: linear-gradient(90deg, #6b9dfc, #8c6bfc);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    font-weight: 800;
}
"""

theme = gr.themes.Ocean(
    primary_hue="fuchsia",
)

with gr.Blocks(theme=theme, css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.HTML("""
                <div align="center" style="margin-bottom: 20px;">
                    <img src='https://lh3.googleusercontent.com/d/1dmZMaOhZxcC_93Rc0ReYTB9oQEUuFJFu' width="160">
                    <p style="font-size: 16px; max-width: 960px; margin: 5px auto;">
                        Human Faces Generation with Diffusion Models.
                    </p>
                    <p style="font-size: 15px;">
                        📜 <a href="https://github.com/frankcholula/faice/blob/main/paper/human_faces_generation_with_diffusion_models.pdf" target="_blank">Report</a>
                        &nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;
                        💻 <a href="https://github.com/frankcholula/faice" target="_blank">Code</a>
                    </p>
                </div>
                """)

        gr.Markdown("---")
        with gr.Row():
            gr.Markdown("## Part 1. Unconditional Face Generation")
            run_button_1 = gr.Button("Run", scale=0, variant="primary", elem_classes="gr-button-primary")

        result_1 = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            seed_1 = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )

            randomize_seed_1 = gr.Checkbox(label="Randomize seed", value=True)

            with gr.Row():
                num_inference_steps_1 = gr.Slider(
                    label="Number of inference steps",
                    minimum=20,
                    maximum=1000,
                    step=1,
                    value=100,
                )

        gr.Markdown("---")
        with gr.Row():
            gr.Markdown("## Part 2. Class Guidance Face Generation")
            run_button_2 = gr.Button("Run", scale=0, variant="primary")

        gender_select_radio = gr.Radio(
            label="Select Gender",
            choices=GENDER_CHOICES,
            value=GENDER_CHOICES[0],
        )
        result_2 = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            seed_2 = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )

            randomize_seed_2 = gr.Checkbox(label="Randomize seed", value=True)

            with gr.Row():
                num_inference_steps_2 = gr.Slider(
                    label="Number of inference steps",
                    minimum=20,
                    maximum=1000,
                    step=1,
                    value=100,
                )

        gr.Markdown("---")
        gr.Markdown("## Part 3. Text-to-Face Generation")
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )

            run_button_3 = gr.Button("Run", scale=0, variant="primary")

        result_3 = 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",
            )

            seed_3 = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )

            randomize_seed_3 = gr.Checkbox(label="Randomize seed", value=True)

            # with gr.Row():
            #     width = gr.Slider(
            #         label="Width",
            #         minimum=512,
            #         maximum=MAX_IMAGE_SIZE,
            #         step=32,
            #         value=1024,
            #     )
            #
            #     height = gr.Slider(
            #         label="Height",
            #         minimum=512,
            #         maximum=MAX_IMAGE_SIZE,
            #         step=32,
            #         value=1024,
            #     )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=7.5,
                    step=0.1,
                    value=7.5,
                )

                num_inference_steps_3 = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=100,
                    step=1,
                    value=50,
                )

        gr.Examples(examples=examples, inputs=[prompt], outputs=[result_3], fn=api_sd,
                    cache_examples=True, cache_mode="lazy")

    gr.on(
        triggers=[run_button_1.click],
        fn=api_unconditional,
        inputs=[
            seed_1,
            randomize_seed_1,
            num_inference_steps_1,
        ],
        outputs=[result_1],
    )

    gr.on(
        triggers=[run_button_2.click],
        fn=api_class_guidance,
        inputs=[
            gender_select_radio,
            seed_2,
            randomize_seed_2,
            num_inference_steps_2,
        ],
        outputs=[result_2],
    )

    gr.on(
        triggers=[run_button_3.click, prompt.submit],
        fn=api_sd,
        inputs=[
            prompt,
            negative_prompt,
            seed_3,
            randomize_seed_3,
            guidance_scale,
            num_inference_steps_3,
        ],
        outputs=[result_3],
    )

if __name__ == "__main__":
    with timer("All tasks"):
        # demo.launch(mcp_server=True)
        demo.launch(share=True, allowed_paths=["./"])