app.py

import json
import os

import numpy as np
import torch
import gradio as gr

from config import PipelineConfig
from src.pipeline import FashionPipeline, PipelineOutput


config = PipelineConfig()
fashion_pipeline = FashionPipeline(config, device=torch.device('cuda'))


def process(input_image: np.ndarray, prompt: str):
    output: PipelineOutput = fashion_pipeline(
        control_image=input_image,
        prompt=prompt,
    )

    return [
        output.generated_image,
        output.segmentation_mask,
    ]


def read_content(file_path: str) -> str:
    """read the content of target file
    """
    with open(file_path, 'r', encoding='utf-8') as f:
        content = f.read()

    return content


image_dir = 'examples/images'
image_list = [os.path.join(image_dir, file) for file in os.listdir(image_dir)]
with open('examples/prompts.json', 'r') as f:
    prompts_list = json.load(f).values()
examples = [[image, prompt] for image, prompt in zip(image_list, prompts_list)]


block = gr.Blocks().queue()
with block:
    with gr.Row():
        gr.HTML(read_content("header.html"))
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(type="numpy")
            prompt = gr.Textbox(label="Prompt")
            run_button = gr.Button(value="Run")
            gr.Examples(examples=examples, inputs=[input_image, prompt], label="Examples - Input Images", examples_per_page=12)

            gr.HTML(
                """
                    <div class="footer">
                    
                        <p> This repo based on Unet from <a style="text-decoration: underline;" href="https://huggingface.co/spaces/wildoctopus/cloth-segmentation">cloth-segmentation</a>
        It's uses pre-trained U2NET to extract Upper body(red), Lower body(green), Full body(blue) masks, and then
        run StableDiffusionXLControlNetPipeline with trained controlnet_baseline to generate image conditioned on this masks.
                        </p>
                """)

        with gr.Column():
            generated_output = gr.Image(label="Generated", type="numpy", elem_id="generated")
            mask_output = gr.Image(label="Mask", type="numpy", elem_id="mask")

    ips = [input_image, prompt]
    run_button.click(fn=process, inputs=ips, outputs=[generated_output, mask_output])


block.launch()