app.py

#!/usr/bin/env python

from __future__ import annotations

import os
import pathlib
import random
import shlex
import subprocess

if os.getenv("SYSTEM") == "spaces":
    subprocess.run(shlex.split("pip install click==7.1.2"))
    subprocess.run(shlex.split("pip install typer==0.9.4"))

    import mim

    mim.uninstall("mmcv-full", confirm_yes=True)
    mim.install("mmcv-full==1.5.2", is_yes=True)

    with open("patch") as f:
        subprocess.run(shlex.split("patch -p1"), cwd="Text2Human", stdin=f)


import gradio as gr
import numpy as np

from model import Model

DESCRIPTION = """# [Text2Human](https://github.com/yumingj/Text2Human)

You can modify sample steps and seeds. By varying seeds, you can sample different human images under the same pose, shape description, and texture description. The larger the sample steps, the better quality of the generated images. (The default value of sample steps is 256 in the original repo.)

Label image generation step can be skipped. However, in that case, the input label image must be 512x256 in size and must contain only the specified colors.
"""

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


def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed


model = Model()

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)

    with gr.Row():
        with gr.Column():
            with gr.Row():
                input_image = gr.Image(label="Input Pose Image", type="pil", elem_id="input-image")
                pose_data = gr.State()
            with gr.Row():
                paths = sorted(pathlib.Path("pose_images").glob("*.png"))
                gr.Examples(examples=[[path.as_posix()] for path in paths], inputs=input_image)

            with gr.Row():
                shape_text = gr.Textbox(
                    label="Shape Description",
                    placeholder="""<gender>, <sleeve length>, <length of lower clothing>, <outer clothing type>, <other accessories1>, ...
Note: The outer clothing type and accessories can be omitted.""",
                )
            with gr.Row():
                gr.Examples(
                    examples=[["man, sleeveless T-shirt, long pants"], ["woman, short-sleeve T-shirt, short jeans"]],
                    inputs=shape_text,
                )
            with gr.Row():
                generate_label_button = gr.Button("Generate Label Image")

        with gr.Column():
            with gr.Row():
                label_image = gr.Image(label="Label Image", type="numpy", format="png", elem_id="label-image")

            with gr.Row():
                texture_text = gr.Textbox(
                    label="Texture Description",
                    placeholder="""<upper clothing texture>, <lower clothing texture>, <outer clothing texture>
Note: Currently, only 5 types of textures are supported, i.e., pure color, stripe/spline, plaid/lattice, floral, denim.""",
                )
            with gr.Row():
                gr.Examples(
                    examples=[
                        ["pure color, denim"],
                        ["floral, stripe"],
                    ],
                    inputs=texture_text,
                )
            with gr.Row():
                sample_steps = gr.Slider(label="Sample Steps", minimum=10, maximum=300, step=1, value=256)
            with gr.Row():
                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():
                generate_human_button = gr.Button("Generate Human")

        with gr.Column():
            with gr.Row():
                result = gr.Image(label="Result")

    input_image.change(
        fn=model.process_pose_image,
        inputs=input_image,
        outputs=pose_data,
    )
    generate_label_button.click(
        fn=model.generate_label_image,
        inputs=[
            pose_data,
            shape_text,
        ],
        outputs=label_image,
    )
    generate_human_button.click(
        fn=randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
    ).then(
        fn=model.generate_human,
        inputs=[
            label_image,
            texture_text,
            sample_steps,
            seed,
        ],
        outputs=result,
    )

if __name__ == "__main__":
    demo.queue(max_size=10).launch()