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Add application file
Browse files- .gitignore +164 -0
- LICENSE +201 -0
- README.md +35 -13
- app.py +121 -0
- controlnet/lineart/__put_your_lineart_model +0 -0
- convertor.py +102 -0
- requirements.txt +13 -0
- sd_model.py +66 -0
- starline.py +416 -0
- utils.py +53 -0
.gitignore
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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output/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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*.safetensors
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*.json
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LICENSE
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Apache License
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|
README.md
CHANGED
@@ -1,13 +1,35 @@
|
|
1 |
-
|
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-
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3 |
-
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4 |
-
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-
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-
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-
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-
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|
1 |
+
# starline
|
2 |
+
**St**rict coloring m**a**chine fo**r** **line** drawings.
|
3 |
+
|
4 |
+
|
5 |
+
![image](https://github.com/mattyamonaca/starline/assets/48423148/eae07a6e-9c7b-4292-8c70-dac8ec8eeb7b)
|
6 |
+
|
7 |
+
|
8 |
+
https://github.com/mattyamonaca/starline/assets/48423148/8199c65c-a19f-42e9-aab7-df5ed6ef5b4c
|
9 |
+
|
10 |
+
# Installation
|
11 |
+
```
|
12 |
+
git clone https://github.com/mattyamonaca/starline.git
|
13 |
+
cd starline
|
14 |
+
conda create -n starline python=3.10
|
15 |
+
conda activate starline
|
16 |
+
conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia
|
17 |
+
pip install -r requirements.txt
|
18 |
+
```
|
19 |
+
|
20 |
+
# Usage
|
21 |
+
- ```python app.py```
|
22 |
+
- Input the line drawing you wish to color (The background should be transparent).
|
23 |
+
- Input a prompt describing the color you want to add.
|
24 |
+
|
25 |
+
- ่ๆฏใ้้ใใ็ถๆ
ใง็ท็ปใๅ
ฅๅใใพใ
|
26 |
+
- ไปใใใ่ฒใ่ชฌๆใใใใญใณใใใๅ
ฅๅใใพใ
|
27 |
+
|
28 |
+
# Precautions
|
29 |
+
- Image size 1024 x 1024 is recommended.
|
30 |
+
- Aliasing is a beta version.
|
31 |
+
- Areas finely surrounded by line drawings cannot be colored.
|
32 |
+
|
33 |
+
- ็ปๅใตใคใบใฏ1024ร1024ใๆจๅฅจใใพใ
|
34 |
+
- ใจใคใชใขในๅฆ็ใฏฮฒ็ใงใใใใ็ท็ปใซๅฟ ๅฎใงใใใใจใๆฑใใๅ ดๅใฏ2ๅค็ท็ปใๆจๅฅจใใพใ
|
35 |
+
- ็ท็ปใง็ดฐใใๅฒใพใใ้จๅใฏ็่ฒใงใใพใใใ็่ฒใงใใชใ้จๅใฏ้้ใใ็ถๆ
ใงๅบๅใใใพใใ
|
app.py
ADDED
@@ -0,0 +1,121 @@
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
1 |
+
import gradio as gr
|
2 |
+
import sys
|
3 |
+
from starline import process
|
4 |
+
|
5 |
+
from utils import load_cn_model, load_cn_config, randomname
|
6 |
+
from convertor import pil2cv, cv2pil
|
7 |
+
|
8 |
+
from sd_model import get_cn_pipeline, generate, get_cn_detector
|
9 |
+
import cv2
|
10 |
+
import os
|
11 |
+
import numpy as np
|
12 |
+
from PIL import Image
|
13 |
+
import zipfile
|
14 |
+
import torch
|
15 |
+
|
16 |
+
zero = torch.Tensor([0]).cuda()
|
17 |
+
|
18 |
+
path = os.getcwd()
|
19 |
+
output_dir = f"{path}/output"
|
20 |
+
input_dir = f"{path}/input"
|
21 |
+
cn_lineart_dir = f"{path}/controlnet/lineart"
|
22 |
+
|
23 |
+
load_cn_model(cn_lineart_dir)
|
24 |
+
load_cn_config(cn_lineart_dir)
|
25 |
+
|
26 |
+
|
27 |
+
def zip_png_files(folder_path):
|
28 |
+
# Zipใใกใคใซใฎๅๅใ่จญๅฎ๏ผใใฉใซใๅใจๅใใซใใพใ๏ผ
|
29 |
+
zip_path = os.path.join(folder_path, 'output.zip')
|
30 |
+
|
31 |
+
# zipfileใชใใธใงใฏใใไฝๆใใๆธใ่พผใฟใขใผใใง้ใ
|
32 |
+
with zipfile.ZipFile(zip_path, 'w') as zipf:
|
33 |
+
# ใใฉใซใๅ
ใฎใในใฆใฎใใกใคใซใใซใผใๅฆ็
|
34 |
+
for foldername, subfolders, filenames in os.walk(folder_path):
|
35 |
+
for filename in filenames:
|
36 |
+
# PNGใใกใคใซใฎใฟใๅฏพ่ฑกใซใใ
|
37 |
+
if filename.endswith('.png'):
|
38 |
+
# ใใกใคใซใฎใใซใในใๅๅพ
|
39 |
+
file_path = os.path.join(foldername, filename)
|
40 |
+
# zipใใกใคใซใซ่ฟฝๅ
|
41 |
+
zipf.write(file_path, arcname=os.path.relpath(file_path, folder_path))
|
42 |
+
|
43 |
+
|
44 |
+
class webui:
|
45 |
+
def __init__(self):
|
46 |
+
self.demo = gr.Blocks()
|
47 |
+
|
48 |
+
def undercoat(self, input_image, pos_prompt, neg_prompt, alpha_th, thickness):
|
49 |
+
org_line_image = input_image
|
50 |
+
image = pil2cv(input_image)
|
51 |
+
image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
|
52 |
+
|
53 |
+
index = np.where(image[:, :, 3] == 0)
|
54 |
+
image[index] = [255, 255, 255, 255]
|
55 |
+
input_image = cv2pil(image)
|
56 |
+
|
57 |
+
pipe = get_cn_pipeline()
|
58 |
+
detectors = get_cn_detector(input_image.resize((1024, 1024), Image.ANTIALIAS))
|
59 |
+
|
60 |
+
|
61 |
+
gen_image = generate(pipe, detectors, pos_prompt, neg_prompt)
|
62 |
+
color_img, unfinished = process(gen_image.resize((image.shape[1], image.shape[0]), Image.ANTIALIAS) , org_line_image, alpha_th, thickness)
|
63 |
+
color_img.save(f"{output_dir}/color_img.png")
|
64 |
+
|
65 |
+
#color_img = color_img.resize((image.shape[1], image.shape[0]) , Image.ANTIALIAS)
|
66 |
+
|
67 |
+
|
68 |
+
output_img = Image.alpha_composite(color_img, org_line_image)
|
69 |
+
name = randomname(10)
|
70 |
+
os.makedirs(f"{output_dir}/{name}")
|
71 |
+
output_img.save(f"{output_dir}/{name}/output_image.png")
|
72 |
+
org_line_image.save(f"{output_dir}/{name}/line_image.png")
|
73 |
+
color_img.save(f"{output_dir}/{name}/color_image.png")
|
74 |
+
unfinished.save(f"{output_dir}/{name}/unfinished_image.png")
|
75 |
+
|
76 |
+
outputs = [output_img, org_line_image, color_img, unfinished]
|
77 |
+
zip_png_files(f"{output_dir}/{name}")
|
78 |
+
filename = f"{output_dir}/{name}/output.zip"
|
79 |
+
|
80 |
+
return outputs, filename
|
81 |
+
|
82 |
+
|
83 |
+
|
84 |
+
def launch(self, share):
|
85 |
+
with self.demo:
|
86 |
+
with gr.Row():
|
87 |
+
with gr.Column():
|
88 |
+
input_image = gr.Image(type="pil", image_mode="RGBA")
|
89 |
+
|
90 |
+
pos_prompt = gr.Textbox(value="1girl, blue hair, pink shirts, bestquality, 4K", max_lines=1000, label="positive prompt")
|
91 |
+
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")
|
92 |
+
|
93 |
+
alpha_th = gr.Slider(maximum = 255, value=100, label = "alpha threshold")
|
94 |
+
thickness = gr.Number(value=5, label="Thickness of correction area (Odd numbers need to be entered)")
|
95 |
+
#gr.Slider(maximum = 21, value=3, step=2, label = "Thickness of correction area")
|
96 |
+
|
97 |
+
submit = gr.Button(value="Start")
|
98 |
+
with gr.Row():
|
99 |
+
with gr.Column():
|
100 |
+
with gr.Tab("output"):
|
101 |
+
output_0 = gr.Gallery(format="png")
|
102 |
+
output_file = gr.File()
|
103 |
+
submit.click(
|
104 |
+
self.undercoat,
|
105 |
+
inputs=[input_image, pos_prompt, neg_prompt, alpha_th, thickness],
|
106 |
+
outputs=[output_0, output_file]
|
107 |
+
)
|
108 |
+
|
109 |
+
self.demo.queue()
|
110 |
+
self.demo.launch(share=share)
|
111 |
+
|
112 |
+
|
113 |
+
if __name__ == "__main__":
|
114 |
+
ui = webui()
|
115 |
+
if len(sys.argv) > 1:
|
116 |
+
if sys.argv[1] == "share":
|
117 |
+
ui.launch(share=True)
|
118 |
+
else:
|
119 |
+
ui.launch(share=False)
|
120 |
+
else:
|
121 |
+
ui.launch(share=False)
|
controlnet/lineart/__put_your_lineart_model
ADDED
File without changes
|
convertor.py
ADDED
@@ -0,0 +1,102 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import pandas as pd
|
2 |
+
import numpy as np
|
3 |
+
from skimage import color
|
4 |
+
from PIL import Image
|
5 |
+
|
6 |
+
|
7 |
+
def skimage_rgb2lab(rgb):
|
8 |
+
return color.rgb2lab(rgb.reshape(1,1,3))
|
9 |
+
|
10 |
+
|
11 |
+
def rgb2df(img):
|
12 |
+
h, w, _ = img.shape
|
13 |
+
x_l, y_l = np.meshgrid(np.arange(h), np.arange(w), indexing='ij')
|
14 |
+
r, g, b = img[:,:,0], img[:,:,1], img[:,:,2]
|
15 |
+
df = pd.DataFrame({
|
16 |
+
"x_l": x_l.ravel(),
|
17 |
+
"y_l": y_l.ravel(),
|
18 |
+
"r": r.ravel(),
|
19 |
+
"g": g.ravel(),
|
20 |
+
"b": b.ravel(),
|
21 |
+
})
|
22 |
+
return df
|
23 |
+
|
24 |
+
def mask2df(mask):
|
25 |
+
h, w = mask.shape
|
26 |
+
x_l, y_l = np.meshgrid(np.arange(h), np.arange(w), indexing='ij')
|
27 |
+
flg = mask.astype(int)
|
28 |
+
df = pd.DataFrame({
|
29 |
+
"x_l_m": x_l.ravel(),
|
30 |
+
"y_l_m": y_l.ravel(),
|
31 |
+
"m_flg": flg.ravel(),
|
32 |
+
})
|
33 |
+
return df
|
34 |
+
|
35 |
+
|
36 |
+
def rgba2df(img):
|
37 |
+
h, w, _ = img.shape
|
38 |
+
x_l, y_l = np.meshgrid(np.arange(h), np.arange(w), indexing='ij')
|
39 |
+
r, g, b, a = img[:,:,0], img[:,:,1], img[:,:,2], img[:,:,3]
|
40 |
+
df = pd.DataFrame({
|
41 |
+
"x_l": x_l.ravel(),
|
42 |
+
"y_l": y_l.ravel(),
|
43 |
+
"r": r.ravel(),
|
44 |
+
"g": g.ravel(),
|
45 |
+
"b": b.ravel(),
|
46 |
+
"a": a.ravel()
|
47 |
+
})
|
48 |
+
return df
|
49 |
+
|
50 |
+
def hsv2df(img):
|
51 |
+
x_l, y_l = np.meshgrid(np.arange(img.shape[0]), np.arange(img.shape[1]), indexing='ij')
|
52 |
+
h, s, v = np.transpose(img, (2, 0, 1))
|
53 |
+
df = pd.DataFrame({'x_l': x_l.flatten(), 'y_l': y_l.flatten(), 'h': h.flatten(), 's': s.flatten(), 'v': v.flatten()})
|
54 |
+
return df
|
55 |
+
|
56 |
+
def df2rgba(img_df):
|
57 |
+
r_img = img_df.pivot_table(index="x_l", columns="y_l",values= "r").reset_index(drop=True).values
|
58 |
+
g_img = img_df.pivot_table(index="x_l", columns="y_l",values= "g").reset_index(drop=True).values
|
59 |
+
b_img = img_df.pivot_table(index="x_l", columns="y_l",values= "b").reset_index(drop=True).values
|
60 |
+
a_img = img_df.pivot_table(index="x_l", columns="y_l",values= "a").reset_index(drop=True).values
|
61 |
+
df_img = np.stack([r_img, g_img, b_img, a_img], 2).astype(np.uint8)
|
62 |
+
return df_img
|
63 |
+
|
64 |
+
def df2bgra(img_df):
|
65 |
+
r_img = img_df.pivot_table(index="x_l", columns="y_l",values= "r").reset_index(drop=True).values
|
66 |
+
g_img = img_df.pivot_table(index="x_l", columns="y_l",values= "g").reset_index(drop=True).values
|
67 |
+
b_img = img_df.pivot_table(index="x_l", columns="y_l",values= "b").reset_index(drop=True).values
|
68 |
+
a_img = img_df.pivot_table(index="x_l", columns="y_l",values= "a").reset_index(drop=True).values
|
69 |
+
df_img = np.stack([b_img, g_img, r_img, a_img], 2).astype(np.uint8)
|
70 |
+
return df_img
|
71 |
+
|
72 |
+
def df2rgb(img_df):
|
73 |
+
r_img = img_df.pivot_table(index="x_l", columns="y_l",values= "r").reset_index(drop=True).values
|
74 |
+
g_img = img_df.pivot_table(index="x_l", columns="y_l",values= "g").reset_index(drop=True).values
|
75 |
+
b_img = img_df.pivot_table(index="x_l", columns="y_l",values= "b").reset_index(drop=True).values
|
76 |
+
df_img = np.stack([r_img, g_img, b_img], 2).astype(np.uint8)
|
77 |
+
return df_img
|
78 |
+
|
79 |
+
def pil2cv(image):
|
80 |
+
new_image = np.array(image, dtype=np.uint8)
|
81 |
+
if new_image.ndim == 2:
|
82 |
+
pass
|
83 |
+
elif new_image.shape[2] == 3:
|
84 |
+
new_image = new_image[:, :, ::-1]
|
85 |
+
elif new_image.shape[2] == 4:
|
86 |
+
new_image = new_image[:, :, [2, 1, 0, 3]]
|
87 |
+
return new_image
|
88 |
+
|
89 |
+
def cv2pil(image):
|
90 |
+
new_image = image.copy()
|
91 |
+
if new_image.ndim == 2:
|
92 |
+
pass
|
93 |
+
elif new_image.shape[2] == 3:
|
94 |
+
new_image = new_image[:, :, ::-1]
|
95 |
+
elif new_image.shape[2] == 4:
|
96 |
+
new_image = new_image[:, :, [2, 1, 0, 3]]
|
97 |
+
new_image = Image.fromarray(new_image)
|
98 |
+
return new_image
|
99 |
+
|
100 |
+
|
101 |
+
|
102 |
+
|
requirements.txt
ADDED
@@ -0,0 +1,13 @@
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
opencv-python==4.7.0.68
|
2 |
+
pandas==1.5.3
|
3 |
+
scikit-learn==1.2.1
|
4 |
+
scikit-image==0.19.3
|
5 |
+
Pillow==9.4.0
|
6 |
+
tqdm==4.63.0
|
7 |
+
diffusers==0.27.2
|
8 |
+
gradio==4.32.1
|
9 |
+
gradio_client==0.17.0
|
10 |
+
transformers==4.40.1
|
11 |
+
accelerate==0.21.0
|
12 |
+
safetensors==0.4.2
|
13 |
+
|
sd_model.py
ADDED
@@ -0,0 +1,66 @@
|
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|
|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
|
2 |
+
from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL
|
3 |
+
import torch
|
4 |
+
import spaces
|
5 |
+
|
6 |
+
device = "cuda"
|
7 |
+
|
8 |
+
def get_cn_pipeline():
|
9 |
+
controlnets = [
|
10 |
+
ControlNetModel.from_pretrained("./controlnet/lineart", torch_dtype=torch.float16, use_safetensors=True),
|
11 |
+
ControlNetModel.from_pretrained("mattyamonaca/controlnet_line2line_xl", torch_dtype=torch.float16)
|
12 |
+
]
|
13 |
+
|
14 |
+
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
|
15 |
+
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
|
16 |
+
"cagliostrolab/animagine-xl-3.1", controlnet=controlnets, vae=vae, torch_dtype=torch.float16
|
17 |
+
)
|
18 |
+
|
19 |
+
pipe.enable_model_cpu_offload()
|
20 |
+
|
21 |
+
#if pipe.safety_checker is not None:
|
22 |
+
# pipe.safety_checker = lambda images, **kwargs: (images, [False])
|
23 |
+
|
24 |
+
#pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
|
25 |
+
#pipe.to(device)
|
26 |
+
|
27 |
+
return pipe
|
28 |
+
|
29 |
+
def invert_image(img):
|
30 |
+
# ็ปๅใ่ชญใฟ่พผใ
|
31 |
+
# ็ปๅใใฐใฌใผในใฑใผใซใซๅคๆ๏ผใใใใจใใจ็ฝ้ปใงใชใๅ ดๅ๏ผ
|
32 |
+
img = img.convert('L')
|
33 |
+
# ็ปๅใฎๅใใฏใปใซใๅ่ปข
|
34 |
+
inverted_img = img.point(lambda p: 255 - p)
|
35 |
+
# ๅ่ปขใใ็ปๅใไฟๅญ
|
36 |
+
return inverted_img
|
37 |
+
|
38 |
+
|
39 |
+
def get_cn_detector(image):
|
40 |
+
#lineart_anime = LineartAnimeDetector.from_pretrained("lllyasviel/Annotators")
|
41 |
+
#canny = CannyDetector()
|
42 |
+
#lineart_anime_img = lineart_anime(image)
|
43 |
+
#canny_img = canny(image)
|
44 |
+
#canny_img = canny_img.resize((lineart_anime(image).width, lineart_anime(image).height))
|
45 |
+
re_image = invert_image(image)
|
46 |
+
|
47 |
+
|
48 |
+
detectors = [re_image, image]
|
49 |
+
print(detectors)
|
50 |
+
return detectors
|
51 |
+
|
52 |
+
@spaces.GPU
|
53 |
+
def generate(pipe, detectors, prompt, negative_prompt):
|
54 |
+
default_pos = ""
|
55 |
+
default_neg = ""
|
56 |
+
prompt = default_pos + prompt
|
57 |
+
negative_prompt = default_neg + negative_prompt
|
58 |
+
print(type(pipe))
|
59 |
+
image = pipe(
|
60 |
+
prompt=prompt,
|
61 |
+
negative_prompt = negative_prompt,
|
62 |
+
image=detectors,
|
63 |
+
num_inference_steps=50,
|
64 |
+
controlnet_conditioning_scale=[1.0, 0.2],
|
65 |
+
).images[0]
|
66 |
+
return image
|
starline.py
ADDED
@@ -0,0 +1,416 @@
|
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|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from collections import defaultdict, deque
|
2 |
+
|
3 |
+
import cv2
|
4 |
+
import numpy as np
|
5 |
+
from PIL import Image
|
6 |
+
from skimage.color import deltaE_ciede2000, rgb2lab
|
7 |
+
from tqdm import tqdm
|
8 |
+
|
9 |
+
|
10 |
+
def modify_transparency(img, target_rgb):
|
11 |
+
# ็ปๅใ่ชญใฟ่พผใ
|
12 |
+
copy_img = img.copy()
|
13 |
+
data = copy_img.getdata()
|
14 |
+
|
15 |
+
# ๆฐใใใใฏใปใซใใผใฟใไฝๆ
|
16 |
+
new_data = []
|
17 |
+
for item in data:
|
18 |
+
# ๆๅฎใใใRGBๅคใฎใใฏใปใซใฎๅ ดๅใ้ๆๅบฆใ255ใซ่จญๅฎ
|
19 |
+
if item[:3] == target_rgb:
|
20 |
+
new_data.append((item[0], item[1], item[2], 255))
|
21 |
+
else:
|
22 |
+
# ใใไปฅๅคใฎๅ ดๅใ้ๆๅบฆใ0ใซ่จญๅฎ
|
23 |
+
new_data.append((item[0], item[1], item[2], 0))
|
24 |
+
|
25 |
+
# ๆฐใใใใผใฟใ็ปๅใซ่จญๅฎใ็ดใ
|
26 |
+
copy_img.putdata(new_data)
|
27 |
+
return copy_img
|
28 |
+
|
29 |
+
|
30 |
+
def replace_color(image, color_1, color_2, alpha_np):
|
31 |
+
# ็ปๅใใผใฟใ้
ๅใซๅคๆ
|
32 |
+
data = np.array(image)
|
33 |
+
|
34 |
+
# RGBAใขใผใใฎ็ปๅใงใใใใใๅฝข็ถๅคๆดๆใซ4ใใฃใใซใ่ๆ
ฎ
|
35 |
+
original_shape = data.shape
|
36 |
+
|
37 |
+
color_1 = np.array(color_1, dtype=np.uint8)
|
38 |
+
color_2 = np.array(color_2, dtype=np.uint8)
|
39 |
+
|
40 |
+
# ๅน
ๅชๅ
ๆข็ดขใง color_1 ใฎ้ ๅใๅคๅดใใๅกใใคใถใ
|
41 |
+
# color_2 ใงไฟ่ญทใใใใชใชใธใใซใฎ็ท็ป
|
42 |
+
protected = np.all(data[:, :, :3] == color_2, axis=2)
|
43 |
+
# color_1 ใงๅกใใใๅกใใคใถใใใ้ ๅ
|
44 |
+
fill_target = np.all(data[:, :, :3] == color_1, axis=2)
|
45 |
+
# ใใงใซๅกใใใฆใใ้ ๅ
|
46 |
+
colored = (protected | fill_target) == False
|
47 |
+
|
48 |
+
# bfs ใฎๅง็นใๅๆ
|
49 |
+
# colored ใใใฎใพใพไฝฟใฃใฆใใใใใpythonใฏ้
ใใฎใงnumpy็ต็ฑใฎใใฎๆนใ้ใ
|
50 |
+
# ไธไธๅทฆๅณใซใทใใใใ fill_target & colored == True ใซใชใใใค
|
51 |
+
adj_r = colored & np.roll(fill_target, -1, axis=0)
|
52 |
+
adj_r[:, -1] = False
|
53 |
+
adj_l = colored & np.roll(fill_target, 1, axis=0)
|
54 |
+
adj_l[:, 0] = False
|
55 |
+
adj_u = colored & np.roll(fill_target, 1, axis=1)
|
56 |
+
adj_u[:, 0] = False
|
57 |
+
adj_d = colored & np.roll(fill_target, -1, axis=1)
|
58 |
+
adj_d[:, -1] = False
|
59 |
+
|
60 |
+
# ใใฎใใฏใปใซใฏใใงใซๅกใใใฆใใฆใไธไธๅทฆๅณใใใใใฎใใฏใปใซใ color_1 ใงใใใใฎ
|
61 |
+
bfs_start = adj_r | adj_l | adj_u | adj_d
|
62 |
+
|
63 |
+
que = deque(
|
64 |
+
zip(*np.where(bfs_start)),
|
65 |
+
maxlen=original_shape[0] * original_shape[1] * 2,
|
66 |
+
)
|
67 |
+
|
68 |
+
with tqdm(total=original_shape[0] * original_shape[1]) as pbar:
|
69 |
+
pbar.update(np.sum(colored) - np.sum(bfs_start) + np.sum(protected))
|
70 |
+
while len(que) > 0:
|
71 |
+
y, x = que.popleft()
|
72 |
+
neighbors = [
|
73 |
+
(x - 1, y),
|
74 |
+
(x + 1, y),
|
75 |
+
(x, y - 1),
|
76 |
+
(x, y + 1), # ไธไธๅทฆๅณ
|
77 |
+
]
|
78 |
+
pbar.update(1)
|
79 |
+
# assert not fill_target[y, x] and not protected[y, x]
|
80 |
+
# assert colored[y, x]
|
81 |
+
color = data[y, x, :3]
|
82 |
+
|
83 |
+
for nx, ny in neighbors:
|
84 |
+
if (
|
85 |
+
nx < 0
|
86 |
+
or nx >= original_shape[1]
|
87 |
+
or ny < 0
|
88 |
+
or ny >= original_shape[0]
|
89 |
+
):
|
90 |
+
continue
|
91 |
+
if fill_target[ny, nx]:
|
92 |
+
fill_target[ny, nx] = False
|
93 |
+
# colored[ny, nx] = True
|
94 |
+
data[ny, nx, :3] = color
|
95 |
+
que.append((ny, nx))
|
96 |
+
pbar.update(pbar.total - pbar.n)
|
97 |
+
|
98 |
+
data[:, :, 3] = 255 - alpha_np
|
99 |
+
return Image.fromarray(data, "RGBA")
|
100 |
+
|
101 |
+
|
102 |
+
def recolor_lineart_and_composite(lineart_image, base_image, new_color, alpha_th):
|
103 |
+
"""
|
104 |
+
Recolor an RGBA lineart image to a single new color while preserving alpha, and composite it over a base image.
|
105 |
+
|
106 |
+
Args:
|
107 |
+
lineart_image (PIL.Image): The lineart image with RGBA channels.
|
108 |
+
base_image (PIL.Image): The base image to composite onto.
|
109 |
+
new_color (tuple): The new RGB color for the lineart (e.g., (255, 0, 0) for red).
|
110 |
+
|
111 |
+
Returns:
|
112 |
+
PIL.Image: The composited image with the recolored lineart on top.
|
113 |
+
"""
|
114 |
+
# Ensure images are in RGBA mode
|
115 |
+
if lineart_image.mode != "RGBA":
|
116 |
+
lineart_image = lineart_image.convert("RGBA")
|
117 |
+
if base_image.mode != "RGBA":
|
118 |
+
base_image = base_image.convert("RGBA")
|
119 |
+
|
120 |
+
# Extract the alpha channel from the lineart image
|
121 |
+
r, g, b, alpha = lineart_image.split()
|
122 |
+
|
123 |
+
alpha_np = np.array(alpha)
|
124 |
+
alpha_np[alpha_np < alpha_th] = 0
|
125 |
+
alpha_np[alpha_np >= alpha_th] = 255
|
126 |
+
|
127 |
+
new_alpha = Image.fromarray(alpha_np)
|
128 |
+
|
129 |
+
# Create a new image using the new color and the alpha channel from the original lineart
|
130 |
+
new_lineart_image = Image.merge(
|
131 |
+
"RGBA",
|
132 |
+
(
|
133 |
+
Image.new("L", lineart_image.size, int(new_color[0])),
|
134 |
+
Image.new("L", lineart_image.size, int(new_color[1])),
|
135 |
+
Image.new("L", lineart_image.size, int(new_color[2])),
|
136 |
+
new_alpha,
|
137 |
+
),
|
138 |
+
)
|
139 |
+
|
140 |
+
# Composite the new lineart image over the base image
|
141 |
+
composite_image = Image.alpha_composite(base_image, new_lineart_image)
|
142 |
+
|
143 |
+
return composite_image, alpha_np
|
144 |
+
|
145 |
+
|
146 |
+
def thicken_and_recolor_lines(base_image, lineart, thickness=3, new_color=(0, 0, 0)):
|
147 |
+
"""
|
148 |
+
Thicken the lines of a lineart image, recolor them, and composite onto another image,
|
149 |
+
while preserving the transparency of the original lineart.
|
150 |
+
|
151 |
+
Args:
|
152 |
+
base_image (PIL.Image): The base image to composite onto.
|
153 |
+
lineart (PIL.Image): The lineart image with transparent background.
|
154 |
+
thickness (int): The desired thickness of the lines.
|
155 |
+
new_color (tuple): The new color to apply to the lines (R, G, B).
|
156 |
+
|
157 |
+
Returns:
|
158 |
+
PIL.Image: The image with the recolored and thickened lineart composited on top.
|
159 |
+
"""
|
160 |
+
# Ensure both images are in RGBA format
|
161 |
+
if base_image.mode != "RGBA":
|
162 |
+
base_image = base_image.convert("RGBA")
|
163 |
+
if lineart.mode != "RGB":
|
164 |
+
lineart = lineart.convert("RGBA")
|
165 |
+
|
166 |
+
# Convert the lineart image to OpenCV format
|
167 |
+
lineart_cv = np.array(lineart)
|
168 |
+
|
169 |
+
white_pixels = np.sum(lineart_cv == 255)
|
170 |
+
black_pixels = np.sum(lineart_cv == 0)
|
171 |
+
|
172 |
+
lineart_gray = cv2.cvtColor(lineart_cv, cv2.COLOR_RGBA2GRAY)
|
173 |
+
|
174 |
+
if white_pixels > black_pixels:
|
175 |
+
lineart_gray = cv2.bitwise_not(lineart_gray)
|
176 |
+
|
177 |
+
# Thicken the lines using OpenCV
|
178 |
+
kernel = np.ones((thickness, thickness), np.uint8)
|
179 |
+
lineart_thickened = cv2.dilate(lineart_gray, kernel, iterations=1)
|
180 |
+
lineart_thickened = cv2.bitwise_not(lineart_thickened)
|
181 |
+
# Create a new RGBA image for the recolored lineart
|
182 |
+
lineart_recolored = np.zeros_like(lineart_cv)
|
183 |
+
lineart_recolored[:, :, :3] = new_color # Set new RGB color
|
184 |
+
|
185 |
+
lineart_recolored[:, :, 3] = np.where(
|
186 |
+
lineart_thickened < 250, 255, 0
|
187 |
+
) # Blend alpha with thickened lines
|
188 |
+
|
189 |
+
# Convert back to PIL Image
|
190 |
+
lineart_recolored_pil = Image.fromarray(lineart_recolored, "RGBA")
|
191 |
+
|
192 |
+
# Composite the thickened and recolored lineart onto the base image
|
193 |
+
combined_image = Image.alpha_composite(base_image, lineart_recolored_pil)
|
194 |
+
|
195 |
+
return combined_image
|
196 |
+
|
197 |
+
|
198 |
+
def generate_distant_colors(consolidated_colors, distance_threshold):
|
199 |
+
"""
|
200 |
+
Generate new RGB colors that are at least 'distance_threshold' CIEDE2000 units away from given colors.
|
201 |
+
|
202 |
+
Args:
|
203 |
+
consolidated_colors (list of tuples): List of ((R, G, B), count) tuples.
|
204 |
+
distance_threshold (float): The minimum CIEDE2000 distance from the given colors.
|
205 |
+
|
206 |
+
Returns:
|
207 |
+
list of tuples: List of new RGB colors that meet the distance requirement.
|
208 |
+
"""
|
209 |
+
# new_colors = []
|
210 |
+
# Convert the consolidated colors to LAB
|
211 |
+
consolidated_lab = [
|
212 |
+
rgb2lab(np.array([color], dtype=np.float32) / 255.0).reshape(3)
|
213 |
+
for color, _ in consolidated_colors
|
214 |
+
]
|
215 |
+
|
216 |
+
# Try to find a distant color
|
217 |
+
max_attempts = 1000
|
218 |
+
best_dist = 0.0
|
219 |
+
best_color = (0, 0, 0)
|
220 |
+
|
221 |
+
# np.random.seed(42)
|
222 |
+
for _ in range(max_attempts):
|
223 |
+
# Generate a random color in RGB and convert to LAB
|
224 |
+
random_rgb = np.random.randint(0, 256, size=3)
|
225 |
+
random_lab = rgb2lab(np.array([random_rgb], dtype=np.float32) / 255.0).reshape(
|
226 |
+
3
|
227 |
+
)
|
228 |
+
# consolidated_lab ใซใใ่ฒใใใงใใใ ใ้ ใ่ฒใ้ธใณใใ
|
229 |
+
min_distance = min(
|
230 |
+
map(
|
231 |
+
lambda base_color_lab: deltaE_ciede2000(base_color_lab, random_lab),
|
232 |
+
consolidated_lab,
|
233 |
+
)
|
234 |
+
)
|
235 |
+
if min_distance > distance_threshold:
|
236 |
+
return tuple(random_rgb)
|
237 |
+
# ้พๅคไปฅไธใฎใใฎใ่ฆใคใใใชใใฃใๅ ดๅใซๅใใฆไธ็ช่ฏใใฃใใใฎใ่ฆใใฆใใ
|
238 |
+
if best_dist < min_distance:
|
239 |
+
best_dist = min_distance
|
240 |
+
best_color = tuple(random_rgb)
|
241 |
+
return best_color
|
242 |
+
|
243 |
+
|
244 |
+
def consolidate_colors(major_colors, threshold):
|
245 |
+
"""
|
246 |
+
Consolidate similar colors in the major_colors list based on the CIEDE2000 metric.
|
247 |
+
|
248 |
+
Args:
|
249 |
+
major_colors (list of tuples): List of ((R, G, B), count) tuples.
|
250 |
+
threshold (float): Threshold for CIEDE2000 color difference.
|
251 |
+
|
252 |
+
Returns:
|
253 |
+
list of tuples: Consolidated list of ((R, G, B), count) tuples.
|
254 |
+
"""
|
255 |
+
# Convert RGB to LAB
|
256 |
+
colors_lab = [
|
257 |
+
rgb2lab(np.array([[color]], dtype=np.float32) / 255.0).reshape(3)
|
258 |
+
for color, _ in major_colors
|
259 |
+
]
|
260 |
+
n = len(colors_lab)
|
261 |
+
|
262 |
+
# Find similar colors and consolidate
|
263 |
+
i = 0
|
264 |
+
while i < n:
|
265 |
+
j = i + 1
|
266 |
+
while j < n:
|
267 |
+
delta_e = deltaE_ciede2000(colors_lab[i], colors_lab[j])
|
268 |
+
if delta_e < threshold:
|
269 |
+
# Compare counts and consolidate to the color with the higher count
|
270 |
+
if major_colors[i][1] >= major_colors[j][1]:
|
271 |
+
major_colors[i] = (
|
272 |
+
major_colors[i][0],
|
273 |
+
major_colors[i][1] + major_colors[j][1],
|
274 |
+
)
|
275 |
+
major_colors.pop(j)
|
276 |
+
colors_lab.pop(j)
|
277 |
+
else:
|
278 |
+
major_colors[j] = (
|
279 |
+
major_colors[j][0],
|
280 |
+
major_colors[j][1] + major_colors[i][1],
|
281 |
+
)
|
282 |
+
major_colors.pop(i)
|
283 |
+
colors_lab.pop(i)
|
284 |
+
n -= 1
|
285 |
+
continue
|
286 |
+
j += 1
|
287 |
+
i += 1
|
288 |
+
|
289 |
+
return major_colors
|
290 |
+
|
291 |
+
|
292 |
+
def get_major_colors(image, threshold_percentage=0.01):
|
293 |
+
"""
|
294 |
+
Analyze an image to find the major RGB values based on a threshold percentage.
|
295 |
+
|
296 |
+
Args:
|
297 |
+
image (PIL.Image): The image to analyze.
|
298 |
+
threshold_percentage (float): The percentage threshold to consider a color as major.
|
299 |
+
|
300 |
+
Returns:
|
301 |
+
list of tuples: A list of (color, count) tuples for colors that are more frequent than the threshold.
|
302 |
+
"""
|
303 |
+
# Convert image to RGB if it's not
|
304 |
+
if image.mode != "RGB":
|
305 |
+
image = image.convert("RGB")
|
306 |
+
|
307 |
+
# Count each color
|
308 |
+
color_count = defaultdict(int)
|
309 |
+
for pixel in image.getdata():
|
310 |
+
color_count[pixel] += 1
|
311 |
+
|
312 |
+
# Total number of pixels
|
313 |
+
total_pixels = image.width * image.height
|
314 |
+
|
315 |
+
# Filter colors to find those above the threshold
|
316 |
+
major_colors = [
|
317 |
+
(color, count)
|
318 |
+
for color, count in color_count.items()
|
319 |
+
if (count / total_pixels) >= threshold_percentage
|
320 |
+
]
|
321 |
+
|
322 |
+
return major_colors
|
323 |
+
|
324 |
+
|
325 |
+
def process(image, lineart, alpha_th, thickness):
|
326 |
+
org = image
|
327 |
+
image.save("tmp.png")
|
328 |
+
|
329 |
+
major_colors = get_major_colors(image, threshold_percentage=0.05)
|
330 |
+
major_colors = consolidate_colors(major_colors, 10)
|
331 |
+
|
332 |
+
th = 10
|
333 |
+
threshold_percentage = 0.05
|
334 |
+
while len(major_colors) < 1:
|
335 |
+
threshold_percentage = threshold_percentage - 0.001
|
336 |
+
major_colors = get_major_colors(image, threshold_percentage=threshold_percentage)
|
337 |
+
|
338 |
+
while len(major_colors) < 1:
|
339 |
+
th = th + 1
|
340 |
+
major_colors = consolidate_colors(major_colors, th)
|
341 |
+
|
342 |
+
new_color_1 = generate_distant_colors(major_colors, 50)
|
343 |
+
image = thicken_and_recolor_lines(
|
344 |
+
org, lineart, thickness=thickness, new_color=new_color_1
|
345 |
+
)
|
346 |
+
|
347 |
+
major_colors.append((new_color_1, 0))
|
348 |
+
new_color_2 = generate_distant_colors(major_colors, 40)
|
349 |
+
image, alpha_np = recolor_lineart_and_composite(
|
350 |
+
lineart, image, new_color_2, alpha_th
|
351 |
+
)
|
352 |
+
# import time
|
353 |
+
# start = time.time()
|
354 |
+
image = replace_color(image, new_color_1, new_color_2, alpha_np)
|
355 |
+
# end = time.time()
|
356 |
+
# print(f"{end-start} sec")
|
357 |
+
unfinished = modify_transparency(image, new_color_1)
|
358 |
+
|
359 |
+
return image, unfinished
|
360 |
+
|
361 |
+
|
362 |
+
def main():
|
363 |
+
import os
|
364 |
+
import sys
|
365 |
+
from argparse import ArgumentParser
|
366 |
+
|
367 |
+
from PIL import Image
|
368 |
+
|
369 |
+
from utils import randomname
|
370 |
+
|
371 |
+
args = ArgumentParser(
|
372 |
+
prog="starline",
|
373 |
+
description="Starline",
|
374 |
+
epilog="Starline",
|
375 |
+
)
|
376 |
+
args.add_argument("-c", "--colored_image", help="colored image", required=True)
|
377 |
+
args.add_argument("-l", "--lineart_image", help="lineart image", required=True)
|
378 |
+
args.add_argument("-o", "--output_dir", help="output directory", default="output")
|
379 |
+
args.add_argument("-a", "--alpha_th", help="alpha threshold", default=100, type=int)
|
380 |
+
args.add_argument("-t", "--thickness", help="line thickness", default=5, type=int)
|
381 |
+
|
382 |
+
args = args.parse_args(sys.argv[1:])
|
383 |
+
colored_image_path = args.colored_image
|
384 |
+
lineart_image_path = args.lineart_image
|
385 |
+
alpha = args.alpha_th
|
386 |
+
thickness = args.thickness
|
387 |
+
output_dir = args.output_dir
|
388 |
+
|
389 |
+
colored_image = Image.open(colored_image_path)
|
390 |
+
lineart_image = Image.open(lineart_image_path)
|
391 |
+
if lineart_image.mode == "P" or lineart_image.mode == "L":
|
392 |
+
# ็ท็ปใ 1-channel ็ปๅใฎใจใใฎๅฆ็
|
393 |
+
# alpha-channel ใฎๆ
ๅ ฑใๅ
ฅๅใใใใจไปฎๅฎใใฆ (้ๆ -> 0, ไธ้ๆ -> 255)
|
394 |
+
# RGB channel ใฏใใใๅ่ปขใใใใใฎใซใใ (้ๆ -> ็ฝ -> 255, ไธ้ๆ -> ้ป -> 0)
|
395 |
+
lineart_image = lineart_image.convert("RGBA")
|
396 |
+
lineart_image = np.array(lineart_image)
|
397 |
+
lineart_image[:, :, 0] = 255 - lineart_image[:, :, 3]
|
398 |
+
lineart_image[:, :, 1] = 255 - lineart_image[:, :, 3]
|
399 |
+
lineart_image[:, :, 2] = 255 - lineart_image[:, :, 3]
|
400 |
+
lineart_image = Image.fromarray(lineart_image)
|
401 |
+
lineart_image = lineart_image.convert("RGBA")
|
402 |
+
|
403 |
+
result_image, unfinished = process(colored_image, lineart_image, alpha, thickness)
|
404 |
+
|
405 |
+
output_image = Image.alpha_composite(result_image, lineart_image)
|
406 |
+
|
407 |
+
name = randomname(10)
|
408 |
+
|
409 |
+
os.makedirs(f"{output_dir}/{name}")
|
410 |
+
output_image.save(f"{output_dir}/{name}/output_image.png")
|
411 |
+
result_image.save(f"{output_dir}/{name}/color_image.png")
|
412 |
+
unfinished.save(f"{output_dir}/{name}/unfinished_image.png")
|
413 |
+
|
414 |
+
|
415 |
+
if __name__ == "__main__":
|
416 |
+
main()
|
utils.py
ADDED
@@ -0,0 +1,53 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import random
|
2 |
+
import string
|
3 |
+
import os
|
4 |
+
|
5 |
+
import requests
|
6 |
+
from tqdm import tqdm
|
7 |
+
|
8 |
+
|
9 |
+
def randomname(n):
|
10 |
+
randlst = [random.choice(string.ascii_letters + string.digits) for i in range(n)]
|
11 |
+
return ''.join(randlst)
|
12 |
+
|
13 |
+
def load_cn_model(model_dir):
|
14 |
+
folder = model_dir
|
15 |
+
file_name = 'diffusion_pytorch_model.safetensors'
|
16 |
+
url = "https://huggingface.co/kataragi/ControlNet-LineartXL/resolve/main/Katarag_lineartXL-fp16.safetensors"
|
17 |
+
|
18 |
+
file_path = os.path.join(folder, file_name)
|
19 |
+
if not os.path.exists(file_path):
|
20 |
+
response = requests.get(url, stream=True)
|
21 |
+
|
22 |
+
total_size = int(response.headers.get('content-length', 0))
|
23 |
+
with open(file_path, 'wb') as f, tqdm(
|
24 |
+
desc=file_name,
|
25 |
+
total=total_size,
|
26 |
+
unit='iB',
|
27 |
+
unit_scale=True,
|
28 |
+
unit_divisor=1024,
|
29 |
+
) as bar:
|
30 |
+
for data in response.iter_content(chunk_size=1024):
|
31 |
+
size = f.write(data)
|
32 |
+
bar.update(size)
|
33 |
+
|
34 |
+
def load_cn_config(model_dir):
|
35 |
+
folder = model_dir
|
36 |
+
file_name = 'config.json'
|
37 |
+
url = "https://huggingface.co/mattyamonaca/controlnet_line2line_xl/resolve/main/config.json"
|
38 |
+
|
39 |
+
file_path = os.path.join(folder, file_name)
|
40 |
+
if not os.path.exists(file_path):
|
41 |
+
response = requests.get(url, stream=True)
|
42 |
+
|
43 |
+
total_size = int(response.headers.get('content-length', 0))
|
44 |
+
with open(file_path, 'wb') as f, tqdm(
|
45 |
+
desc=file_name,
|
46 |
+
total=total_size,
|
47 |
+
unit='iB',
|
48 |
+
unit_scale=True,
|
49 |
+
unit_divisor=1024,
|
50 |
+
) as bar:
|
51 |
+
for data in response.iter_content(chunk_size=1024):
|
52 |
+
size = f.write(data)
|
53 |
+
bar.update(size)
|