File size: 2,872 Bytes
f2a4883 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
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
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)
class webui:
def __init__(self):
self.demo = gr.Blocks()
def undercoat(self, input_image, pos_prompt, neg_prompt, alpha_th):
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)
output = process(gen_image.resize((image.shape[1], image.shape[0]), Image.ANTIALIAS) , org_line_image, alpha_th)
output = output.resize((image.shape[1], image.shape[0]) , Image.ANTIALIAS)
output = Image.alpha_composite(output, org_line_image)
name = randomname(10)
output.save(f"{output_dir}/output_{name}.png")
#output = pil2cv(output)
file_name = f"{output_dir}/output_{name}.png"
return output, file_name
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(max_lines=1000, label="positive prompt")
neg_prompt = gr.Textbox(max_lines=1000, label="negative prompt")
alpha_th = gr.Slider(maximum = 255, value=100, label = "alpha threshold")
submit = gr.Button(value="Start")
with gr.Row():
with gr.Column():
with gr.Tab("output"):
output_0 = gr.Image()
output_file = gr.File()
submit.click(
self.undercoat,
inputs=[input_image, pos_prompt, neg_prompt, alpha_th],
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)
|