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import gradio as gr
import sys
from starline import process
from utils import load_cn_model, load_cn_config, randomname
from convertor import pil2cv, cv2pil
from sd_model import get_cn_pipeline, generate, get_cn_detector
import cv2
import os
import numpy as np
from PIL import Image
import zipfile
import torch
zero = torch.Tensor([0]).cuda()
path = os.getcwd()
output_dir = f"{path}/output"
input_dir = f"{path}/input"
cn_lineart_dir = f"{path}/controlnet/lineart"
load_cn_model(cn_lineart_dir)
load_cn_config(cn_lineart_dir)
def zip_png_files(folder_path):
# Zipファイルの名前を設定（フォルダ名と同じにします）
zip_path = os.path.join(folder_path, 'output.zip')
# zipfileオブジェクトを作成し、書き込みモードで開く
with zipfile.ZipFile(zip_path, 'w') as zipf:
# フォルダ内のすべてのファイルをループ処理
for foldername, subfolders, filenames in os.walk(folder_path):
for filename in filenames:
# PNGファイルのみを対象にする
if filename.endswith('.png'):
# ファイルのフルパスを取得
file_path = os.path.join(foldername, filename)
# zipファイルに追加
zipf.write(file_path, arcname=os.path.relpath(file_path, folder_path))
class webui:
def __init__(self):
self.demo = gr.Blocks()
def undercoat(self, input_image, pos_prompt, neg_prompt, alpha_th, thickness):
org_line_image = input_image
image = pil2cv(input_image)
image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
index = np.where(image[:, :, 3] == 0)
image[index] = [255, 255, 255, 255]
input_image = cv2pil(image)
pipe = get_cn_pipeline()
detectors = get_cn_detector(input_image.resize((1024, 1024), Image.ANTIALIAS))
gen_image = generate(pipe, detectors, pos_prompt, neg_prompt)
color_img, unfinished = process(gen_image.resize((image.shape[1], image.shape[0]), Image.ANTIALIAS) , org_line_image, alpha_th, thickness)
color_img.save(f"{output_dir}/color_img.png")
#color_img = color_img.resize((image.shape[1], image.shape[0]) , Image.ANTIALIAS)
output_img = Image.alpha_composite(color_img, org_line_image)
name = randomname(10)
os.makedirs(f"{output_dir}/{name}")
output_img.save(f"{output_dir}/{name}/output_image.png")
org_line_image.save(f"{output_dir}/{name}/line_image.png")
color_img.save(f"{output_dir}/{name}/color_image.png")
unfinished.save(f"{output_dir}/{name}/unfinished_image.png")
outputs = [output_img, org_line_image, color_img, unfinished]
zip_png_files(f"{output_dir}/{name}")
filename = f"{output_dir}/{name}/output.zip"
return outputs, filename
def launch(self, share):
with self.demo:
with gr.Row():
with gr.Column():
input_image = gr.Image(type="pil", image_mode="RGBA")
pos_prompt = gr.Textbox(value="1girl, blue hair, pink shirts, bestquality, 4K", max_lines=1000, label="positive prompt")
neg_prompt = gr.Textbox(value=" (worst quality, low quality:1.2), (lowres:1.2), (bad anatomy:1.2), (greyscale, monochrome:1.4)", max_lines=1000, label="negative prompt")
alpha_th = gr.Slider(maximum = 255, value=100, label = "alpha threshold")
thickness = gr.Number(value=5, label="Thickness of correction area (Odd numbers need to be entered)")
#gr.Slider(maximum = 21, value=3, step=2, label = "Thickness of correction area")
submit = gr.Button(value="Start")
with gr.Row():
with gr.Column():
with gr.Tab("output"):
output_0 = gr.Gallery(format="png")
output_file = gr.File()
submit.click(
self.undercoat,
inputs=[input_image, pos_prompt, neg_prompt, alpha_th, thickness],
outputs=[output_0, output_file]
)
self.demo.queue()
self.demo.launch(share=share)
if __name__ == "__main__":
ui = webui()
if len(sys.argv) > 1:
if sys.argv[1] == "share":
ui.launch(share=True)
else:
ui.launch(share=False)
else:
ui.launch(share=False)
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