app_haircolor_test.py

import spaces
import gradio as gr
import time
import torch

from PIL import Image
from segment_utils import(
    segment_image,
    restore_result,
)
from enhance_utils import enhance_image
from inversion_run_adapter_test import run as adapter_run


DEFAULT_SRC_PROMPT = "a woman, with hair"
DEFAULT_EDIT_PROMPT = "a woman, with red hair, 8k, high quality"

DEFAULT_CATEGORY = "hair"

device = "cuda" if torch.cuda.is_available() else "cpu"

@spaces.GPU(duration=15)
def image_to_image(
    input_image: Image,
    input_image_prompt: str,
    edit_prompt: str,
    seed: int,
    w1: float,
    num_steps: int,
    start_step: int,
    guidance_scale: float,
    generate_size: int,
    lineart_scale: float,
    canny_scale: float,
    lineart_detect: float,
    canny_detect: float,
):
    w2 = 1.0
    run_task_time = 0
    time_cost_str = ''
    run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
    run_model = adapter_run
    generated_image = run_model(
        input_image,
        input_image_prompt,
        edit_prompt,
        generate_size,
        seed,
        w1,
        w2,
        num_steps,
        start_step,
        guidance_scale,
        lineart_scale,
        canny_scale,
        lineart_detect,
        canny_detect,
    )
    run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
    enhanced_image = enhance_image(generated_image, False)
    run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)

    return enhanced_image, generated_image, time_cost_str

def get_time_cost(run_task_time, time_cost_str):
    now_time = int(time.time()*1000)
    if run_task_time == 0:
        time_cost_str = 'start'
    else:
        if time_cost_str != '': 
            time_cost_str += f'-->'
        time_cost_str += f'{now_time - run_task_time}'
    run_task_time = now_time
    return run_task_time, time_cost_str

def create_demo() -> gr.Blocks:
    with gr.Blocks() as demo:
        croper = gr.State()
        with gr.Row():
            with gr.Column():
                input_image_prompt = gr.Textbox(lines=1, label="Input Image Prompt", value=DEFAULT_SRC_PROMPT)
                edit_prompt = gr.Textbox(lines=1, label="Edit Prompt", value=DEFAULT_EDIT_PROMPT)
                category = gr.Textbox(label="Category", value=DEFAULT_CATEGORY, visible=False)
            with gr.Column():
                num_steps = gr.Slider(minimum=1, maximum=100, value=5, step=1, label="Num Steps")
                start_step = gr.Slider(minimum=1, maximum=100, value=1, step=1, label="Start Step")
                with gr.Accordion("Advanced Options", open=False):
                    guidance_scale = gr.Slider(minimum=0, maximum=20, value=0, step=0.5, label="Guidance Scale", visible=True)
                    generate_size = gr.Number(label="Generate Size", value=768)
                    mask_expansion = gr.Number(label="Mask Expansion", value=10, visible=True)
                    mask_dilation = gr.Slider(minimum=0, maximum=10, value=2, step=1, label="Mask Dilation")
                    lineart_scale = gr.Slider(minimum=0, maximum=5, value=1.2, step=0.1, label="Lineart Weights", visible=True)
                    canny_scale = gr.Slider(minimum=0, maximum=5, value=2, step=0.1, label="Canny Weights", visible=True)
                    lineart_detect = gr.Number(label="Lineart Detect", value=0.375, visible=True)
                    canny_detect = gr.Number(label="Canny Detect", value=0.375, visible=True)
            with gr.Column():
                seed = gr.Number(label="Seed", value=8)
                w1 = gr.Number(label="W1", value=2.5)
                g_btn = gr.Button("Edit Image")
                
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(label="Input Image", type="pil")
            with gr.Column():
                restored_image = gr.Image(label="Restored Image", type="pil", interactive=False)
                download_path = gr.File(label="Download the output image", interactive=False)
            with gr.Column():
                origin_area_image = gr.Image(label="Origin Area Image", type="pil", interactive=False)
                enhanced_image = gr.Image(label="Enhanced Image", type="pil", interactive=False)
                generated_cost = gr.Textbox(label="Time cost by step (ms):", visible=True, interactive=False)
                generated_image = gr.Image(label="Generated Image", type="pil", interactive=False)
        
        g_btn.click(
            fn=segment_image,
            inputs=[input_image, category, generate_size, mask_expansion, mask_dilation],
            outputs=[origin_area_image, croper],
        ).success(
            fn=image_to_image,
            inputs=[origin_area_image, input_image_prompt, edit_prompt,seed,w1, num_steps, start_step, guidance_scale, generate_size, lineart_scale, canny_scale, lineart_detect, canny_detect],
            outputs=[enhanced_image, generated_image, generated_cost],
        ).success(
            fn=restore_result,
            inputs=[croper, category, enhanced_image],
            outputs=[restored_image, download_path],
        )

    return demo