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Duplicate from hysts/ControlNet-v1-1
Browse filesCo-authored-by: hysts <hysts@users.noreply.huggingface.co>
- .gitattributes +34 -0
- .gitignore +162 -0
- .pre-commit-config.yaml +36 -0
- .style.yapf +5 -0
- LICENSE +21 -0
- LICENSE.ControlNet +201 -0
- README.md +16 -0
- app.py +130 -0
- app_canny.py +108 -0
- app_depth.py +107 -0
- app_ip2p.py +94 -0
- app_lineart.py +116 -0
- app_mlsd.py +115 -0
- app_normal.py +106 -0
- app_openpose.py +106 -0
- app_scribble.py +107 -0
- app_scribble_interactive.py +114 -0
- app_segmentation.py +106 -0
- app_shuffle.py +100 -0
- app_softedge.py +112 -0
- cv_utils.py +17 -0
- depth_estimator.py +25 -0
- image_segmentor.py +39 -0
- model.py +591 -0
- notebooks/notebook.ipynb +69 -0
- preprocessor.py +77 -0
- requirements.txt +12 -0
- style.css +3 -0
- utils.py +7 -0
.gitattributes
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gradio_cached_examples/
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*.so
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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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share/python-wheels/
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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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.cache
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nosetests.xml
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coverage.xml
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cover/
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*.pot
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*.log
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local_settings.py
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instance/
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target/
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profile_default/
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ipython_config.py
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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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# 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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#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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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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.pdm.toml
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__pypackages__/
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celerybeat-schedule
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celerybeat.pid
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*.sage.py
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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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.ropeproject
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# mkdocs documentation
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/site
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.mypy_cache/
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.dmypy.json
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dmypy.json
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cython_debug/
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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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.pre-commit-config.yaml
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repos:
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rev: v4.2.0
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.style.yapf
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[style]
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based_on_style = pep8
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blank_line_before_nested_class_or_def = false
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LICENSE
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MIT License
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Copyright (c) 2023 hysts
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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LICENSE.ControlNet
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|
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README.md
ADDED
@@ -0,0 +1,16 @@
|
|
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|
|
|
1 |
+
---
|
2 |
+
title: ControlNet V1.1
|
3 |
+
emoji: 📉
|
4 |
+
colorFrom: yellow
|
5 |
+
colorTo: green
|
6 |
+
sdk: gradio
|
7 |
+
sdk_version: 3.34.0
|
8 |
+
python_version: 3.10.11
|
9 |
+
app_file: app.py
|
10 |
+
pinned: false
|
11 |
+
license: mit
|
12 |
+
suggested_hardware: t4-medium
|
13 |
+
duplicated_from: hysts/ControlNet-v1-1
|
14 |
+
---
|
15 |
+
|
16 |
+
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
app.py
ADDED
@@ -0,0 +1,130 @@
|
|
|
|
|
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|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
from __future__ import annotations
|
4 |
+
|
5 |
+
import os
|
6 |
+
|
7 |
+
import gradio as gr
|
8 |
+
import torch
|
9 |
+
|
10 |
+
from app_canny import create_demo as create_demo_canny
|
11 |
+
from app_depth import create_demo as create_demo_depth
|
12 |
+
from app_ip2p import create_demo as create_demo_ip2p
|
13 |
+
from app_lineart import create_demo as create_demo_lineart
|
14 |
+
from app_mlsd import create_demo as create_demo_mlsd
|
15 |
+
from app_normal import create_demo as create_demo_normal
|
16 |
+
from app_openpose import create_demo as create_demo_openpose
|
17 |
+
from app_scribble import create_demo as create_demo_scribble
|
18 |
+
from app_scribble_interactive import \
|
19 |
+
create_demo as create_demo_scribble_interactive
|
20 |
+
from app_segmentation import create_demo as create_demo_segmentation
|
21 |
+
from app_shuffle import create_demo as create_demo_shuffle
|
22 |
+
from app_softedge import create_demo as create_demo_softedge
|
23 |
+
from model import Model
|
24 |
+
|
25 |
+
DESCRIPTION = '# ControlNet v1.1'
|
26 |
+
|
27 |
+
SPACE_ID = os.getenv('SPACE_ID')
|
28 |
+
ALLOW_CHANGING_BASE_MODEL = SPACE_ID != 'hysts/ControlNet-v1-1'
|
29 |
+
|
30 |
+
if SPACE_ID is not None:
|
31 |
+
DESCRIPTION += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
|
32 |
+
|
33 |
+
if not torch.cuda.is_available():
|
34 |
+
DESCRIPTION += '\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>'
|
35 |
+
|
36 |
+
MAX_NUM_IMAGES = int(os.getenv('MAX_NUM_IMAGES', '3'))
|
37 |
+
DEFAULT_NUM_IMAGES = min(MAX_NUM_IMAGES,
|
38 |
+
int(os.getenv('DEFAULT_NUM_IMAGES', '1')))
|
39 |
+
|
40 |
+
DEFAULT_MODEL_ID = os.getenv('DEFAULT_MODEL_ID',
|
41 |
+
'runwayml/stable-diffusion-v1-5')
|
42 |
+
model = Model(base_model_id=DEFAULT_MODEL_ID, task_name='Canny')
|
43 |
+
|
44 |
+
with gr.Blocks(css='style.css') as demo:
|
45 |
+
gr.Markdown(DESCRIPTION)
|
46 |
+
with gr.Tabs():
|
47 |
+
with gr.TabItem('Canny'):
|
48 |
+
create_demo_canny(model.process_canny,
|
49 |
+
max_images=MAX_NUM_IMAGES,
|
50 |
+
default_num_images=DEFAULT_NUM_IMAGES)
|
51 |
+
with gr.TabItem('MLSD'):
|
52 |
+
create_demo_mlsd(model.process_mlsd,
|
53 |
+
max_images=MAX_NUM_IMAGES,
|
54 |
+
default_num_images=DEFAULT_NUM_IMAGES)
|
55 |
+
with gr.TabItem('Scribble'):
|
56 |
+
create_demo_scribble(model.process_scribble,
|
57 |
+
max_images=MAX_NUM_IMAGES,
|
58 |
+
default_num_images=DEFAULT_NUM_IMAGES)
|
59 |
+
with gr.TabItem('Scribble Interactive'):
|
60 |
+
create_demo_scribble_interactive(
|
61 |
+
model.process_scribble_interactive,
|
62 |
+
max_images=MAX_NUM_IMAGES,
|
63 |
+
default_num_images=DEFAULT_NUM_IMAGES)
|
64 |
+
with gr.TabItem('SoftEdge'):
|
65 |
+
create_demo_softedge(model.process_softedge,
|
66 |
+
max_images=MAX_NUM_IMAGES,
|
67 |
+
default_num_images=DEFAULT_NUM_IMAGES)
|
68 |
+
with gr.TabItem('OpenPose'):
|
69 |
+
create_demo_openpose(model.process_openpose,
|
70 |
+
max_images=MAX_NUM_IMAGES,
|
71 |
+
default_num_images=DEFAULT_NUM_IMAGES)
|
72 |
+
with gr.TabItem('Segmentation'):
|
73 |
+
create_demo_segmentation(model.process_segmentation,
|
74 |
+
max_images=MAX_NUM_IMAGES,
|
75 |
+
default_num_images=DEFAULT_NUM_IMAGES)
|
76 |
+
with gr.TabItem('Depth'):
|
77 |
+
create_demo_depth(model.process_depth,
|
78 |
+
max_images=MAX_NUM_IMAGES,
|
79 |
+
default_num_images=DEFAULT_NUM_IMAGES)
|
80 |
+
with gr.TabItem('Normal map'):
|
81 |
+
create_demo_normal(model.process_normal,
|
82 |
+
max_images=MAX_NUM_IMAGES,
|
83 |
+
default_num_images=DEFAULT_NUM_IMAGES)
|
84 |
+
with gr.TabItem('Lineart'):
|
85 |
+
create_demo_lineart(model.process_lineart,
|
86 |
+
max_images=MAX_NUM_IMAGES,
|
87 |
+
default_num_images=DEFAULT_NUM_IMAGES)
|
88 |
+
with gr.TabItem('Content Shuffle'):
|
89 |
+
create_demo_shuffle(model.process_shuffle,
|
90 |
+
max_images=MAX_NUM_IMAGES,
|
91 |
+
default_num_images=DEFAULT_NUM_IMAGES)
|
92 |
+
with gr.TabItem('Instruct Pix2Pix'):
|
93 |
+
create_demo_ip2p(model.process_ip2p,
|
94 |
+
max_images=MAX_NUM_IMAGES,
|
95 |
+
default_num_images=DEFAULT_NUM_IMAGES)
|
96 |
+
|
97 |
+
with gr.Accordion(label='Base model', open=False):
|
98 |
+
with gr.Row():
|
99 |
+
with gr.Column():
|
100 |
+
current_base_model = gr.Text(label='Current base model')
|
101 |
+
with gr.Column(scale=0.3):
|
102 |
+
check_base_model_button = gr.Button('Check current base model')
|
103 |
+
with gr.Row():
|
104 |
+
with gr.Column():
|
105 |
+
new_base_model_id = gr.Text(
|
106 |
+
label='New base model',
|
107 |
+
max_lines=1,
|
108 |
+
placeholder='runwayml/stable-diffusion-v1-5',
|
109 |
+
info=
|
110 |
+
'The base model must be compatible with Stable Diffusion v1.5.',
|
111 |
+
interactive=ALLOW_CHANGING_BASE_MODEL)
|
112 |
+
with gr.Column(scale=0.3):
|
113 |
+
change_base_model_button = gr.Button(
|
114 |
+
'Change base model', interactive=ALLOW_CHANGING_BASE_MODEL)
|
115 |
+
if not ALLOW_CHANGING_BASE_MODEL:
|
116 |
+
gr.Markdown(
|
117 |
+
'''The base model is not allowed to be changed in this Space so as not to slow down the demo, but it can be changed if you duplicate the Space. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a>'''
|
118 |
+
)
|
119 |
+
|
120 |
+
check_base_model_button.click(fn=lambda: model.base_model_id,
|
121 |
+
outputs=current_base_model,
|
122 |
+
queue=False)
|
123 |
+
new_base_model_id.submit(fn=model.set_base_model,
|
124 |
+
inputs=new_base_model_id,
|
125 |
+
outputs=current_base_model)
|
126 |
+
change_base_model_button.click(fn=model.set_base_model,
|
127 |
+
inputs=new_base_model_id,
|
128 |
+
outputs=current_base_model)
|
129 |
+
|
130 |
+
demo.queue(max_size=20).launch()
|
app_canny.py
ADDED
@@ -0,0 +1,108 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
import gradio as gr
|
4 |
+
|
5 |
+
from utils import randomize_seed_fn
|
6 |
+
|
7 |
+
|
8 |
+
def create_demo(process, max_images=12, default_num_images=3):
|
9 |
+
with gr.Blocks() as demo:
|
10 |
+
with gr.Row():
|
11 |
+
with gr.Column():
|
12 |
+
image = gr.Image()
|
13 |
+
prompt = gr.Textbox(label='Prompt')
|
14 |
+
run_button = gr.Button('Run')
|
15 |
+
with gr.Accordion('Advanced options', open=False):
|
16 |
+
num_samples = gr.Slider(label='Number of images',
|
17 |
+
minimum=1,
|
18 |
+
maximum=max_images,
|
19 |
+
value=default_num_images,
|
20 |
+
step=1)
|
21 |
+
image_resolution = gr.Slider(label='Image resolution',
|
22 |
+
minimum=256,
|
23 |
+
maximum=512,
|
24 |
+
value=512,
|
25 |
+
step=256)
|
26 |
+
canny_low_threshold = gr.Slider(
|
27 |
+
label='Canny low threshold',
|
28 |
+
minimum=1,
|
29 |
+
maximum=255,
|
30 |
+
value=100,
|
31 |
+
step=1)
|
32 |
+
canny_high_threshold = gr.Slider(
|
33 |
+
label='Canny high threshold',
|
34 |
+
minimum=1,
|
35 |
+
maximum=255,
|
36 |
+
value=200,
|
37 |
+
step=1)
|
38 |
+
num_steps = gr.Slider(label='Number of steps',
|
39 |
+
minimum=1,
|
40 |
+
maximum=100,
|
41 |
+
value=20,
|
42 |
+
step=1)
|
43 |
+
guidance_scale = gr.Slider(label='Guidance scale',
|
44 |
+
minimum=0.1,
|
45 |
+
maximum=30.0,
|
46 |
+
value=9.0,
|
47 |
+
step=0.1)
|
48 |
+
seed = gr.Slider(label='Seed',
|
49 |
+
minimum=0,
|
50 |
+
maximum=1000000,
|
51 |
+
step=1,
|
52 |
+
value=0,
|
53 |
+
randomize=True)
|
54 |
+
randomize_seed = gr.Checkbox(label='Randomize seed',
|
55 |
+
value=True)
|
56 |
+
a_prompt = gr.Textbox(
|
57 |
+
label='Additional prompt',
|
58 |
+
value='best quality, extremely detailed')
|
59 |
+
n_prompt = gr.Textbox(
|
60 |
+
label='Negative prompt',
|
61 |
+
value=
|
62 |
+
'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
|
63 |
+
)
|
64 |
+
with gr.Column():
|
65 |
+
result = gr.Gallery(label='Output', show_label=False).style(
|
66 |
+
columns=2, object_fit='scale-down')
|
67 |
+
inputs = [
|
68 |
+
image,
|
69 |
+
prompt,
|
70 |
+
a_prompt,
|
71 |
+
n_prompt,
|
72 |
+
num_samples,
|
73 |
+
image_resolution,
|
74 |
+
num_steps,
|
75 |
+
guidance_scale,
|
76 |
+
seed,
|
77 |
+
canny_low_threshold,
|
78 |
+
canny_high_threshold,
|
79 |
+
]
|
80 |
+
prompt.submit(
|
81 |
+
fn=randomize_seed_fn,
|
82 |
+
inputs=[seed, randomize_seed],
|
83 |
+
outputs=seed,
|
84 |
+
queue=False,
|
85 |
+
).then(
|
86 |
+
fn=process,
|
87 |
+
inputs=inputs,
|
88 |
+
outputs=result,
|
89 |
+
)
|
90 |
+
run_button.click(
|
91 |
+
fn=randomize_seed_fn,
|
92 |
+
inputs=[seed, randomize_seed],
|
93 |
+
outputs=seed,
|
94 |
+
queue=False,
|
95 |
+
).then(
|
96 |
+
fn=process,
|
97 |
+
inputs=inputs,
|
98 |
+
outputs=result,
|
99 |
+
api_name='canny',
|
100 |
+
)
|
101 |
+
return demo
|
102 |
+
|
103 |
+
|
104 |
+
if __name__ == '__main__':
|
105 |
+
from model import Model
|
106 |
+
model = Model(task_name='Canny')
|
107 |
+
demo = create_demo(model.process_canny)
|
108 |
+
demo.queue().launch()
|
app_depth.py
ADDED
@@ -0,0 +1,107 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
import gradio as gr
|
4 |
+
|
5 |
+
from utils import randomize_seed_fn
|
6 |
+
|
7 |
+
|
8 |
+
def create_demo(process, max_images=12, default_num_images=3):
|
9 |
+
with gr.Blocks() as demo:
|
10 |
+
with gr.Row():
|
11 |
+
with gr.Column():
|
12 |
+
image = gr.Image()
|
13 |
+
prompt = gr.Textbox(label='Prompt')
|
14 |
+
run_button = gr.Button('Run')
|
15 |
+
with gr.Accordion('Advanced options', open=False):
|
16 |
+
preprocessor_name = gr.Radio(
|
17 |
+
label='Preprocessor',
|
18 |
+
choices=['Midas', 'DPT', 'None'],
|
19 |
+
type='value',
|
20 |
+
value='DPT')
|
21 |
+
num_samples = gr.Slider(label='Number of images',
|
22 |
+
minimum=1,
|
23 |
+
maximum=max_images,
|
24 |
+
value=default_num_images,
|
25 |
+
step=1)
|
26 |
+
image_resolution = gr.Slider(label='Image resolution',
|
27 |
+
minimum=256,
|
28 |
+
maximum=512,
|
29 |
+
value=512,
|
30 |
+
step=256)
|
31 |
+
preprocess_resolution = gr.Slider(
|
32 |
+
label='Preprocess resolution',
|
33 |
+
minimum=128,
|
34 |
+
maximum=512,
|
35 |
+
value=384,
|
36 |
+
step=1)
|
37 |
+
num_steps = gr.Slider(label='Number of steps',
|
38 |
+
minimum=1,
|
39 |
+
maximum=100,
|
40 |
+
value=20,
|
41 |
+
step=1)
|
42 |
+
guidance_scale = gr.Slider(label='Guidance scale',
|
43 |
+
minimum=0.1,
|
44 |
+
maximum=30.0,
|
45 |
+
value=9.0,
|
46 |
+
step=0.1)
|
47 |
+
seed = gr.Slider(label='Seed',
|
48 |
+
minimum=0,
|
49 |
+
maximum=1000000,
|
50 |
+
step=1,
|
51 |
+
value=0,
|
52 |
+
randomize=True)
|
53 |
+
randomize_seed = gr.Checkbox(label='Randomize seed',
|
54 |
+
value=True)
|
55 |
+
a_prompt = gr.Textbox(
|
56 |
+
label='Additional prompt',
|
57 |
+
value='best quality, extremely detailed')
|
58 |
+
n_prompt = gr.Textbox(
|
59 |
+
label='Negative prompt',
|
60 |
+
value=
|
61 |
+
'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
|
62 |
+
)
|
63 |
+
with gr.Column():
|
64 |
+
result = gr.Gallery(label='Output', show_label=False).style(
|
65 |
+
columns=2, object_fit='scale-down')
|
66 |
+
inputs = [
|
67 |
+
image,
|
68 |
+
prompt,
|
69 |
+
a_prompt,
|
70 |
+
n_prompt,
|
71 |
+
num_samples,
|
72 |
+
image_resolution,
|
73 |
+
preprocess_resolution,
|
74 |
+
num_steps,
|
75 |
+
guidance_scale,
|
76 |
+
seed,
|
77 |
+
preprocessor_name,
|
78 |
+
]
|
79 |
+
prompt.submit(
|
80 |
+
fn=randomize_seed_fn,
|
81 |
+
inputs=[seed, randomize_seed],
|
82 |
+
outputs=seed,
|
83 |
+
queue=False,
|
84 |
+
).then(
|
85 |
+
fn=process,
|
86 |
+
inputs=inputs,
|
87 |
+
outputs=result,
|
88 |
+
)
|
89 |
+
run_button.click(
|
90 |
+
fn=randomize_seed_fn,
|
91 |
+
inputs=[seed, randomize_seed],
|
92 |
+
outputs=seed,
|
93 |
+
queue=False,
|
94 |
+
).then(
|
95 |
+
fn=process,
|
96 |
+
inputs=inputs,
|
97 |
+
outputs=result,
|
98 |
+
api_name='depth',
|
99 |
+
)
|
100 |
+
return demo
|
101 |
+
|
102 |
+
|
103 |
+
if __name__ == '__main__':
|
104 |
+
from model import Model
|
105 |
+
model = Model(task_name='depth')
|
106 |
+
demo = create_demo(model.process_depth)
|
107 |
+
demo.queue().launch()
|
app_ip2p.py
ADDED
@@ -0,0 +1,94 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
import gradio as gr
|
4 |
+
|
5 |
+
from utils import randomize_seed_fn
|
6 |
+
|
7 |
+
|
8 |
+
def create_demo(process, max_images=12, default_num_images=3):
|
9 |
+
with gr.Blocks() as demo:
|
10 |
+
with gr.Row():
|
11 |
+
with gr.Column():
|
12 |
+
image = gr.Image()
|
13 |
+
prompt = gr.Textbox(label='Prompt')
|
14 |
+
run_button = gr.Button('Run')
|
15 |
+
with gr.Accordion('Advanced options', open=False):
|
16 |
+
num_samples = gr.Slider(label='Number of images',
|
17 |
+
minimum=1,
|
18 |
+
maximum=max_images,
|
19 |
+
value=default_num_images,
|
20 |
+
step=1)
|
21 |
+
image_resolution = gr.Slider(label='Image resolution',
|
22 |
+
minimum=256,
|
23 |
+
maximum=512,
|
24 |
+
value=512,
|
25 |
+
step=256)
|
26 |
+
num_steps = gr.Slider(label='Number of steps',
|
27 |
+
minimum=1,
|
28 |
+
maximum=100,
|
29 |
+
value=20,
|
30 |
+
step=1)
|
31 |
+
guidance_scale = gr.Slider(label='Guidance scale',
|
32 |
+
minimum=0.1,
|
33 |
+
maximum=30.0,
|
34 |
+
value=9.0,
|
35 |
+
step=0.1)
|
36 |
+
seed = gr.Slider(label='Seed',
|
37 |
+
minimum=0,
|
38 |
+
maximum=1000000,
|
39 |
+
step=1,
|
40 |
+
value=0,
|
41 |
+
randomize=True)
|
42 |
+
randomize_seed = gr.Checkbox(label='Randomize seed',
|
43 |
+
value=True)
|
44 |
+
a_prompt = gr.Textbox(
|
45 |
+
label='Additional prompt',
|
46 |
+
value='best quality, extremely detailed')
|
47 |
+
n_prompt = gr.Textbox(
|
48 |
+
label='Negative prompt',
|
49 |
+
value=
|
50 |
+
'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
|
51 |
+
)
|
52 |
+
with gr.Column():
|
53 |
+
result = gr.Gallery(label='Output', show_label=False).style(
|
54 |
+
columns=2, object_fit='scale-down')
|
55 |
+
inputs = [
|
56 |
+
image,
|
57 |
+
prompt,
|
58 |
+
a_prompt,
|
59 |
+
n_prompt,
|
60 |
+
num_samples,
|
61 |
+
image_resolution,
|
62 |
+
num_steps,
|
63 |
+
guidance_scale,
|
64 |
+
seed,
|
65 |
+
]
|
66 |
+
prompt.submit(
|
67 |
+
fn=randomize_seed_fn,
|
68 |
+
inputs=[seed, randomize_seed],
|
69 |
+
outputs=seed,
|
70 |
+
queue=False,
|
71 |
+
).then(
|
72 |
+
fn=process,
|
73 |
+
inputs=inputs,
|
74 |
+
outputs=result,
|
75 |
+
)
|
76 |
+
run_button.click(
|
77 |
+
fn=randomize_seed_fn,
|
78 |
+
inputs=[seed, randomize_seed],
|
79 |
+
outputs=seed,
|
80 |
+
queue=False,
|
81 |
+
).then(
|
82 |
+
fn=process,
|
83 |
+
inputs=inputs,
|
84 |
+
outputs=result,
|
85 |
+
api_name='ip2p',
|
86 |
+
)
|
87 |
+
return demo
|
88 |
+
|
89 |
+
|
90 |
+
if __name__ == '__main__':
|
91 |
+
from model import Model
|
92 |
+
model = Model(task_name='ip2p')
|
93 |
+
demo = create_demo(model.process_ip2p)
|
94 |
+
demo.queue().launch()
|
app_lineart.py
ADDED
@@ -0,0 +1,116 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
import gradio as gr
|
4 |
+
|
5 |
+
from utils import randomize_seed_fn
|
6 |
+
|
7 |
+
|
8 |
+
def create_demo(process, max_images=12, default_num_images=3):
|
9 |
+
with gr.Blocks() as demo:
|
10 |
+
with gr.Row():
|
11 |
+
with gr.Column():
|
12 |
+
image = gr.Image()
|
13 |
+
prompt = gr.Textbox(label='Prompt')
|
14 |
+
run_button = gr.Button('Run')
|
15 |
+
with gr.Accordion('Advanced options', open=False):
|
16 |
+
preprocessor_name = gr.Radio(
|
17 |
+
label='Preprocessor',
|
18 |
+
choices=[
|
19 |
+
'Lineart',
|
20 |
+
'Lineart coarse',
|
21 |
+
'None',
|
22 |
+
'Lineart (anime)',
|
23 |
+
'None (anime)',
|
24 |
+
],
|
25 |
+
type='value',
|
26 |
+
value='Lineart',
|
27 |
+
info=
|
28 |
+
'Note that "Lineart (anime)" and "None (anime)" are for anime base models like Anything-v3.'
|
29 |
+
)
|
30 |
+
num_samples = gr.Slider(label='Number of images',
|
31 |
+
minimum=1,
|
32 |
+
maximum=max_images,
|
33 |
+
value=default_num_images,
|
34 |
+
step=1)
|
35 |
+
image_resolution = gr.Slider(label='Image resolution',
|
36 |
+
minimum=256,
|
37 |
+
maximum=512,
|
38 |
+
value=512,
|
39 |
+
step=256)
|
40 |
+
preprocess_resolution = gr.Slider(
|
41 |
+
label='Preprocess resolution',
|
42 |
+
minimum=128,
|
43 |
+
maximum=512,
|
44 |
+
value=512,
|
45 |
+
step=1)
|
46 |
+
num_steps = gr.Slider(label='Number of steps',
|
47 |
+
minimum=1,
|
48 |
+
maximum=100,
|
49 |
+
value=20,
|
50 |
+
step=1)
|
51 |
+
guidance_scale = gr.Slider(label='Guidance scale',
|
52 |
+
minimum=0.1,
|
53 |
+
maximum=30.0,
|
54 |
+
value=9.0,
|
55 |
+
step=0.1)
|
56 |
+
seed = gr.Slider(label='Seed',
|
57 |
+
minimum=0,
|
58 |
+
maximum=1000000,
|
59 |
+
step=1,
|
60 |
+
value=0,
|
61 |
+
randomize=True)
|
62 |
+
randomize_seed = gr.Checkbox(label='Randomize seed',
|
63 |
+
value=True)
|
64 |
+
a_prompt = gr.Textbox(
|
65 |
+
label='Additional prompt',
|
66 |
+
value='best quality, extremely detailed')
|
67 |
+
n_prompt = gr.Textbox(
|
68 |
+
label='Negative prompt',
|
69 |
+
value=
|
70 |
+
'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
|
71 |
+
)
|
72 |
+
with gr.Column():
|
73 |
+
result = gr.Gallery(label='Output', show_label=False).style(
|
74 |
+
columns=2, object_fit='scale-down')
|
75 |
+
inputs = [
|
76 |
+
image,
|
77 |
+
prompt,
|
78 |
+
a_prompt,
|
79 |
+
n_prompt,
|
80 |
+
num_samples,
|
81 |
+
image_resolution,
|
82 |
+
preprocess_resolution,
|
83 |
+
num_steps,
|
84 |
+
guidance_scale,
|
85 |
+
seed,
|
86 |
+
preprocessor_name,
|
87 |
+
]
|
88 |
+
prompt.submit(
|
89 |
+
fn=randomize_seed_fn,
|
90 |
+
inputs=[seed, randomize_seed],
|
91 |
+
outputs=seed,
|
92 |
+
queue=False,
|
93 |
+
).then(
|
94 |
+
fn=process,
|
95 |
+
inputs=inputs,
|
96 |
+
outputs=result,
|
97 |
+
)
|
98 |
+
run_button.click(
|
99 |
+
fn=randomize_seed_fn,
|
100 |
+
inputs=[seed, randomize_seed],
|
101 |
+
outputs=seed,
|
102 |
+
queue=False,
|
103 |
+
).then(
|
104 |
+
fn=process,
|
105 |
+
inputs=inputs,
|
106 |
+
outputs=result,
|
107 |
+
api_name='lineart',
|
108 |
+
)
|
109 |
+
return demo
|
110 |
+
|
111 |
+
|
112 |
+
if __name__ == '__main__':
|
113 |
+
from model import Model
|
114 |
+
model = Model(task_name='lineart')
|
115 |
+
demo = create_demo(model.process_lineart)
|
116 |
+
demo.queue().launch()
|
app_mlsd.py
ADDED
@@ -0,0 +1,115 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
import gradio as gr
|
4 |
+
|
5 |
+
from utils import randomize_seed_fn
|
6 |
+
|
7 |
+
|
8 |
+
def create_demo(process, max_images=12, default_num_images=3):
|
9 |
+
with gr.Blocks() as demo:
|
10 |
+
with gr.Row():
|
11 |
+
with gr.Column():
|
12 |
+
image = gr.Image()
|
13 |
+
prompt = gr.Textbox(label='Prompt')
|
14 |
+
run_button = gr.Button('Run')
|
15 |
+
with gr.Accordion('Advanced options', open=False):
|
16 |
+
num_samples = gr.Slider(label='Number of images',
|
17 |
+
minimum=1,
|
18 |
+
maximum=max_images,
|
19 |
+
value=default_num_images,
|
20 |
+
step=1)
|
21 |
+
image_resolution = gr.Slider(label='Image resolution',
|
22 |
+
minimum=256,
|
23 |
+
maximum=512,
|
24 |
+
value=512,
|
25 |
+
step=256)
|
26 |
+
preprocess_resolution = gr.Slider(
|
27 |
+
label='Preprocess resolution',
|
28 |
+
minimum=128,
|
29 |
+
maximum=512,
|
30 |
+
value=512,
|
31 |
+
step=1)
|
32 |
+
mlsd_value_threshold = gr.Slider(
|
33 |
+
label='Hough value threshold (MLSD)',
|
34 |
+
minimum=0.01,
|
35 |
+
maximum=2.0,
|
36 |
+
value=0.1,
|
37 |
+
step=0.01)
|
38 |
+
mlsd_distance_threshold = gr.Slider(
|
39 |
+
label='Hough distance threshold (MLSD)',
|
40 |
+
minimum=0.01,
|
41 |
+
maximum=20.0,
|
42 |
+
value=0.1,
|
43 |
+
step=0.01)
|
44 |
+
num_steps = gr.Slider(label='Number of steps',
|
45 |
+
minimum=1,
|
46 |
+
maximum=100,
|
47 |
+
value=20,
|
48 |
+
step=1)
|
49 |
+
guidance_scale = gr.Slider(label='Guidance scale',
|
50 |
+
minimum=0.1,
|
51 |
+
maximum=30.0,
|
52 |
+
value=9.0,
|
53 |
+
step=0.1)
|
54 |
+
seed = gr.Slider(label='Seed',
|
55 |
+
minimum=0,
|
56 |
+
maximum=1000000,
|
57 |
+
step=1,
|
58 |
+
value=0,
|
59 |
+
randomize=True)
|
60 |
+
randomize_seed = gr.Checkbox(label='Randomize seed',
|
61 |
+
value=True)
|
62 |
+
a_prompt = gr.Textbox(
|
63 |
+
label='Additional prompt',
|
64 |
+
value='best quality, extremely detailed')
|
65 |
+
n_prompt = gr.Textbox(
|
66 |
+
label='Negative prompt',
|
67 |
+
value=
|
68 |
+
'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
|
69 |
+
)
|
70 |
+
with gr.Column():
|
71 |
+
result = gr.Gallery(label='Output', show_label=False).style(
|
72 |
+
columns=2, object_fit='scale-down')
|
73 |
+
inputs = [
|
74 |
+
image,
|
75 |
+
prompt,
|
76 |
+
a_prompt,
|
77 |
+
n_prompt,
|
78 |
+
num_samples,
|
79 |
+
image_resolution,
|
80 |
+
preprocess_resolution,
|
81 |
+
num_steps,
|
82 |
+
guidance_scale,
|
83 |
+
seed,
|
84 |
+
mlsd_value_threshold,
|
85 |
+
mlsd_distance_threshold,
|
86 |
+
]
|
87 |
+
prompt.submit(
|
88 |
+
fn=randomize_seed_fn,
|
89 |
+
inputs=[seed, randomize_seed],
|
90 |
+
outputs=seed,
|
91 |
+
queue=False,
|
92 |
+
).then(
|
93 |
+
fn=process,
|
94 |
+
inputs=inputs,
|
95 |
+
outputs=result,
|
96 |
+
)
|
97 |
+
run_button.click(
|
98 |
+
fn=randomize_seed_fn,
|
99 |
+
inputs=[seed, randomize_seed],
|
100 |
+
outputs=seed,
|
101 |
+
queue=False,
|
102 |
+
).then(
|
103 |
+
fn=process,
|
104 |
+
inputs=inputs,
|
105 |
+
outputs=result,
|
106 |
+
api_name='mlsd',
|
107 |
+
)
|
108 |
+
return demo
|
109 |
+
|
110 |
+
|
111 |
+
if __name__ == '__main__':
|
112 |
+
from model import Model
|
113 |
+
model = Model(task_name='MLSD')
|
114 |
+
demo = create_demo(model.process_mlsd)
|
115 |
+
demo.queue().launch()
|
app_normal.py
ADDED
@@ -0,0 +1,106 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
import gradio as gr
|
4 |
+
|
5 |
+
from utils import randomize_seed_fn
|
6 |
+
|
7 |
+
|
8 |
+
def create_demo(process, max_images=12, default_num_images=3):
|
9 |
+
with gr.Blocks() as demo:
|
10 |
+
with gr.Row():
|
11 |
+
with gr.Column():
|
12 |
+
image = gr.Image()
|
13 |
+
prompt = gr.Textbox(label='Prompt')
|
14 |
+
run_button = gr.Button('Run')
|
15 |
+
with gr.Accordion('Advanced options', open=False):
|
16 |
+
preprocessor_name = gr.Radio(label='Preprocessor',
|
17 |
+
choices=['NormalBae', 'None'],
|
18 |
+
type='value',
|
19 |
+
value='NormalBae')
|
20 |
+
num_samples = gr.Slider(label='Images',
|
21 |
+
minimum=1,
|
22 |
+
maximum=max_images,
|
23 |
+
value=default_num_images,
|
24 |
+
step=1)
|
25 |
+
image_resolution = gr.Slider(label='Image resolution',
|
26 |
+
minimum=256,
|
27 |
+
maximum=512,
|
28 |
+
value=512,
|
29 |
+
step=256)
|
30 |
+
preprocess_resolution = gr.Slider(
|
31 |
+
label='Preprocess resolution',
|
32 |
+
minimum=128,
|
33 |
+
maximum=512,
|
34 |
+
value=384,
|
35 |
+
step=1)
|
36 |
+
num_steps = gr.Slider(label='Number of steps',
|
37 |
+
minimum=1,
|
38 |
+
maximum=100,
|
39 |
+
value=20,
|
40 |
+
step=1)
|
41 |
+
guidance_scale = gr.Slider(label='Guidance scale',
|
42 |
+
minimum=0.1,
|
43 |
+
maximum=30.0,
|
44 |
+
value=9.0,
|
45 |
+
step=0.1)
|
46 |
+
seed = gr.Slider(label='Seed',
|
47 |
+
minimum=0,
|
48 |
+
maximum=1000000,
|
49 |
+
step=1,
|
50 |
+
value=0,
|
51 |
+
randomize=True)
|
52 |
+
randomize_seed = gr.Checkbox(label='Randomize seed',
|
53 |
+
value=True)
|
54 |
+
a_prompt = gr.Textbox(
|
55 |
+
label='Additional prompt',
|
56 |
+
value='best quality, extremely detailed')
|
57 |
+
n_prompt = gr.Textbox(
|
58 |
+
label='Negative prompt',
|
59 |
+
value=
|
60 |
+
'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
|
61 |
+
)
|
62 |
+
with gr.Column():
|
63 |
+
result = gr.Gallery(label='Output', show_label=False).style(
|
64 |
+
columns=2, object_fit='scale-down')
|
65 |
+
inputs = [
|
66 |
+
image,
|
67 |
+
prompt,
|
68 |
+
a_prompt,
|
69 |
+
n_prompt,
|
70 |
+
num_samples,
|
71 |
+
image_resolution,
|
72 |
+
preprocess_resolution,
|
73 |
+
num_steps,
|
74 |
+
guidance_scale,
|
75 |
+
seed,
|
76 |
+
preprocessor_name,
|
77 |
+
]
|
78 |
+
prompt.submit(
|
79 |
+
fn=randomize_seed_fn,
|
80 |
+
inputs=[seed, randomize_seed],
|
81 |
+
outputs=seed,
|
82 |
+
queue=False,
|
83 |
+
).then(
|
84 |
+
fn=process,
|
85 |
+
inputs=inputs,
|
86 |
+
outputs=result,
|
87 |
+
)
|
88 |
+
run_button.click(
|
89 |
+
fn=randomize_seed_fn,
|
90 |
+
inputs=[seed, randomize_seed],
|
91 |
+
outputs=seed,
|
92 |
+
queue=False,
|
93 |
+
).then(
|
94 |
+
fn=process,
|
95 |
+
inputs=inputs,
|
96 |
+
outputs=result,
|
97 |
+
api_name='normal',
|
98 |
+
)
|
99 |
+
return demo
|
100 |
+
|
101 |
+
|
102 |
+
if __name__ == '__main__':
|
103 |
+
from model import Model
|
104 |
+
model = Model(task_name='NormalBae')
|
105 |
+
demo = create_demo(model.process_normal)
|
106 |
+
demo.queue().launch()
|
app_openpose.py
ADDED
@@ -0,0 +1,106 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
import gradio as gr
|
4 |
+
|
5 |
+
from utils import randomize_seed_fn
|
6 |
+
|
7 |
+
|
8 |
+
def create_demo(process, max_images=12, default_num_images=3):
|
9 |
+
with gr.Blocks() as demo:
|
10 |
+
with gr.Row():
|
11 |
+
with gr.Column():
|
12 |
+
image = gr.Image()
|
13 |
+
prompt = gr.Textbox(label='Prompt')
|
14 |
+
run_button = gr.Button(label='Run')
|
15 |
+
with gr.Accordion('Advanced options', open=False):
|
16 |
+
preprocessor_name = gr.Radio(label='Preprocessor',
|
17 |
+
choices=['Openpose', 'None'],
|
18 |
+
type='value',
|
19 |
+
value='Openpose')
|
20 |
+
num_samples = gr.Slider(label='Number of images',
|
21 |
+
minimum=1,
|
22 |
+
maximum=max_images,
|
23 |
+
value=default_num_images,
|
24 |
+
step=1)
|
25 |
+
image_resolution = gr.Slider(label='Image resolution',
|
26 |
+
minimum=256,
|
27 |
+
maximum=512,
|
28 |
+
value=512,
|
29 |
+
step=256)
|
30 |
+
preprocess_resolution = gr.Slider(
|
31 |
+
label='Preprocess resolution',
|
32 |
+
minimum=128,
|
33 |
+
maximum=512,
|
34 |
+
value=512,
|
35 |
+
step=1)
|
36 |
+
num_steps = gr.Slider(label='Number of steps',
|
37 |
+
minimum=1,
|
38 |
+
maximum=100,
|
39 |
+
value=20,
|
40 |
+
step=1)
|
41 |
+
guidance_scale = gr.Slider(label='Guidance scale',
|
42 |
+
minimum=0.1,
|
43 |
+
maximum=30.0,
|
44 |
+
value=9.0,
|
45 |
+
step=0.1)
|
46 |
+
seed = gr.Slider(label='Seed',
|
47 |
+
minimum=0,
|
48 |
+
maximum=1000000,
|
49 |
+
step=1,
|
50 |
+
value=0,
|
51 |
+
randomize=True)
|
52 |
+
randomize_seed = gr.Checkbox(label='Randomize seed',
|
53 |
+
value=True)
|
54 |
+
a_prompt = gr.Textbox(
|
55 |
+
label='Additional prompt',
|
56 |
+
value='best quality, extremely detailed')
|
57 |
+
n_prompt = gr.Textbox(
|
58 |
+
label='Negative prompt',
|
59 |
+
value=
|
60 |
+
'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
|
61 |
+
)
|
62 |
+
with gr.Column():
|
63 |
+
result = gr.Gallery(label='Output', show_label=False).style(
|
64 |
+
columns=2, object_fit='scale-down')
|
65 |
+
inputs = [
|
66 |
+
image,
|
67 |
+
prompt,
|
68 |
+
a_prompt,
|
69 |
+
n_prompt,
|
70 |
+
num_samples,
|
71 |
+
image_resolution,
|
72 |
+
preprocess_resolution,
|
73 |
+
num_steps,
|
74 |
+
guidance_scale,
|
75 |
+
seed,
|
76 |
+
preprocessor_name,
|
77 |
+
]
|
78 |
+
prompt.submit(
|
79 |
+
fn=randomize_seed_fn,
|
80 |
+
inputs=[seed, randomize_seed],
|
81 |
+
outputs=seed,
|
82 |
+
queue=False,
|
83 |
+
).then(
|
84 |
+
fn=process,
|
85 |
+
inputs=inputs,
|
86 |
+
outputs=result,
|
87 |
+
)
|
88 |
+
run_button.click(
|
89 |
+
fn=randomize_seed_fn,
|
90 |
+
inputs=[seed, randomize_seed],
|
91 |
+
outputs=seed,
|
92 |
+
queue=False,
|
93 |
+
).then(
|
94 |
+
fn=process,
|
95 |
+
inputs=inputs,
|
96 |
+
outputs=result,
|
97 |
+
api_name='openpose',
|
98 |
+
)
|
99 |
+
return demo
|
100 |
+
|
101 |
+
|
102 |
+
if __name__ == '__main__':
|
103 |
+
from model import Model
|
104 |
+
model = Model(task_name='Openpose')
|
105 |
+
demo = create_demo(model.process_openpose)
|
106 |
+
demo.queue().launch()
|
app_scribble.py
ADDED
@@ -0,0 +1,107 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
import gradio as gr
|
4 |
+
|
5 |
+
from utils import randomize_seed_fn
|
6 |
+
|
7 |
+
|
8 |
+
def create_demo(process, max_images=12, default_num_images=3):
|
9 |
+
with gr.Blocks() as demo:
|
10 |
+
with gr.Row():
|
11 |
+
with gr.Column():
|
12 |
+
image = gr.Image()
|
13 |
+
prompt = gr.Textbox(label='Prompt')
|
14 |
+
run_button = gr.Button('Run')
|
15 |
+
with gr.Accordion('Advanced options', open=False):
|
16 |
+
preprocessor_name = gr.Radio(
|
17 |
+
label='Preprocessor',
|
18 |
+
choices=['HED', 'PidiNet', 'None'],
|
19 |
+
type='value',
|
20 |
+
value='HED')
|
21 |
+
num_samples = gr.Slider(label='Number of images',
|
22 |
+
minimum=1,
|
23 |
+
maximum=max_images,
|
24 |
+
value=default_num_images,
|
25 |
+
step=1)
|
26 |
+
image_resolution = gr.Slider(label='Image resolution',
|
27 |
+
minimum=256,
|
28 |
+
maximum=512,
|
29 |
+
value=512,
|
30 |
+
step=256)
|
31 |
+
preprocess_resolution = gr.Slider(
|
32 |
+
label='Preprocess resolution',
|
33 |
+
minimum=128,
|
34 |
+
maximum=512,
|
35 |
+
value=512,
|
36 |
+
step=1)
|
37 |
+
num_steps = gr.Slider(label='Number of steps',
|
38 |
+
minimum=1,
|
39 |
+
maximum=100,
|
40 |
+
value=20,
|
41 |
+
step=1)
|
42 |
+
guidance_scale = gr.Slider(label='Guidance scale',
|
43 |
+
minimum=0.1,
|
44 |
+
maximum=30.0,
|
45 |
+
value=9.0,
|
46 |
+
step=0.1)
|
47 |
+
seed = gr.Slider(label='Seed',
|
48 |
+
minimum=0,
|
49 |
+
maximum=1000000,
|
50 |
+
step=1,
|
51 |
+
value=0,
|
52 |
+
randomize=True)
|
53 |
+
randomize_seed = gr.Checkbox(label='Randomize seed',
|
54 |
+
value=True)
|
55 |
+
a_prompt = gr.Textbox(
|
56 |
+
label='Additional prompt',
|
57 |
+
value='best quality, extremely detailed')
|
58 |
+
n_prompt = gr.Textbox(
|
59 |
+
label='Negative prompt',
|
60 |
+
value=
|
61 |
+
'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
|
62 |
+
)
|
63 |
+
with gr.Column():
|
64 |
+
result = gr.Gallery(label='Output', show_label=False).style(
|
65 |
+
columns=2, object_fit='scale-down')
|
66 |
+
inputs = [
|
67 |
+
image,
|
68 |
+
prompt,
|
69 |
+
a_prompt,
|
70 |
+
n_prompt,
|
71 |
+
num_samples,
|
72 |
+
image_resolution,
|
73 |
+
preprocess_resolution,
|
74 |
+
num_steps,
|
75 |
+
guidance_scale,
|
76 |
+
seed,
|
77 |
+
preprocessor_name,
|
78 |
+
]
|
79 |
+
prompt.submit(
|
80 |
+
fn=randomize_seed_fn,
|
81 |
+
inputs=[seed, randomize_seed],
|
82 |
+
outputs=seed,
|
83 |
+
queue=False,
|
84 |
+
).then(
|
85 |
+
fn=process,
|
86 |
+
inputs=inputs,
|
87 |
+
outputs=result,
|
88 |
+
)
|
89 |
+
run_button.click(
|
90 |
+
fn=randomize_seed_fn,
|
91 |
+
inputs=[seed, randomize_seed],
|
92 |
+
outputs=seed,
|
93 |
+
queue=False,
|
94 |
+
).then(
|
95 |
+
fn=process,
|
96 |
+
inputs=inputs,
|
97 |
+
outputs=result,
|
98 |
+
api_name='scribble',
|
99 |
+
)
|
100 |
+
return demo
|
101 |
+
|
102 |
+
|
103 |
+
if __name__ == '__main__':
|
104 |
+
from model import Model
|
105 |
+
model = Model(task_name='scribble')
|
106 |
+
demo = create_demo(model.process_scribble)
|
107 |
+
demo.queue().launch()
|
app_scribble_interactive.py
ADDED
@@ -0,0 +1,114 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
import gradio as gr
|
4 |
+
import numpy as np
|
5 |
+
|
6 |
+
from utils import randomize_seed_fn
|
7 |
+
|
8 |
+
|
9 |
+
def create_canvas(w, h):
|
10 |
+
return np.zeros(shape=(h, w, 3), dtype=np.uint8) + 255
|
11 |
+
|
12 |
+
|
13 |
+
def create_demo(process, max_images=12, default_num_images=3):
|
14 |
+
with gr.Blocks() as demo:
|
15 |
+
with gr.Row():
|
16 |
+
with gr.Column():
|
17 |
+
canvas_width = gr.Slider(label='Canvas width',
|
18 |
+
minimum=256,
|
19 |
+
maximum=512,
|
20 |
+
value=512,
|
21 |
+
step=1)
|
22 |
+
canvas_height = gr.Slider(label='Canvas height',
|
23 |
+
minimum=256,
|
24 |
+
maximum=512,
|
25 |
+
value=512,
|
26 |
+
step=1)
|
27 |
+
create_button = gr.Button('Open drawing canvas!')
|
28 |
+
image = gr.Image(tool='sketch', brush_radius=10)
|
29 |
+
prompt = gr.Textbox(label='Prompt')
|
30 |
+
run_button = gr.Button('Run')
|
31 |
+
with gr.Accordion('Advanced options', open=False):
|
32 |
+
num_samples = gr.Slider(label='Number of images',
|
33 |
+
minimum=1,
|
34 |
+
maximum=max_images,
|
35 |
+
value=default_num_images,
|
36 |
+
step=1)
|
37 |
+
image_resolution = gr.Slider(label='Image resolution',
|
38 |
+
minimum=256,
|
39 |
+
maximum=512,
|
40 |
+
value=512,
|
41 |
+
step=256)
|
42 |
+
num_steps = gr.Slider(label='Number of steps',
|
43 |
+
minimum=1,
|
44 |
+
maximum=100,
|
45 |
+
value=20,
|
46 |
+
step=1)
|
47 |
+
guidance_scale = gr.Slider(label='Guidance scale',
|
48 |
+
minimum=0.1,
|
49 |
+
maximum=30.0,
|
50 |
+
value=9.0,
|
51 |
+
step=0.1)
|
52 |
+
seed = gr.Slider(label='Seed',
|
53 |
+
minimum=0,
|
54 |
+
maximum=1000000,
|
55 |
+
step=1,
|
56 |
+
value=0,
|
57 |
+
randomize=True)
|
58 |
+
randomize_seed = gr.Checkbox(label='Randomize seed',
|
59 |
+
value=True)
|
60 |
+
a_prompt = gr.Textbox(
|
61 |
+
label='Additional prompt',
|
62 |
+
value='best quality, extremely detailed')
|
63 |
+
n_prompt = gr.Textbox(
|
64 |
+
label='Negative prompt',
|
65 |
+
value=
|
66 |
+
'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
|
67 |
+
)
|
68 |
+
with gr.Column():
|
69 |
+
result = gr.Gallery(label='Output', show_label=False).style(
|
70 |
+
columns=2, object_fit='scale-down')
|
71 |
+
|
72 |
+
create_button.click(fn=create_canvas,
|
73 |
+
inputs=[canvas_width, canvas_height],
|
74 |
+
outputs=image,
|
75 |
+
queue=False)
|
76 |
+
inputs = [
|
77 |
+
image,
|
78 |
+
prompt,
|
79 |
+
a_prompt,
|
80 |
+
n_prompt,
|
81 |
+
num_samples,
|
82 |
+
image_resolution,
|
83 |
+
num_steps,
|
84 |
+
guidance_scale,
|
85 |
+
seed,
|
86 |
+
]
|
87 |
+
prompt.submit(
|
88 |
+
fn=randomize_seed_fn,
|
89 |
+
inputs=[seed, randomize_seed],
|
90 |
+
outputs=seed,
|
91 |
+
queue=False,
|
92 |
+
).then(
|
93 |
+
fn=process,
|
94 |
+
inputs=inputs,
|
95 |
+
outputs=result,
|
96 |
+
)
|
97 |
+
run_button.click(
|
98 |
+
fn=randomize_seed_fn,
|
99 |
+
inputs=[seed, randomize_seed],
|
100 |
+
outputs=seed,
|
101 |
+
queue=False,
|
102 |
+
).then(
|
103 |
+
fn=process,
|
104 |
+
inputs=inputs,
|
105 |
+
outputs=result,
|
106 |
+
)
|
107 |
+
return demo
|
108 |
+
|
109 |
+
|
110 |
+
if __name__ == '__main__':
|
111 |
+
from model import Model
|
112 |
+
model = Model(task_name='scribble')
|
113 |
+
demo = create_demo(model.process_scribble_interactive)
|
114 |
+
demo.queue().launch()
|
app_segmentation.py
ADDED
@@ -0,0 +1,106 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
import gradio as gr
|
4 |
+
|
5 |
+
from utils import randomize_seed_fn
|
6 |
+
|
7 |
+
|
8 |
+
def create_demo(process, max_images=12, default_num_images=3):
|
9 |
+
with gr.Blocks() as demo:
|
10 |
+
with gr.Row():
|
11 |
+
with gr.Column():
|
12 |
+
image = gr.Image()
|
13 |
+
prompt = gr.Textbox(label='Prompt')
|
14 |
+
run_button = gr.Button('Run')
|
15 |
+
with gr.Accordion('Advanced options', open=False):
|
16 |
+
preprocessor_name = gr.Radio(label='Preprocessor',
|
17 |
+
choices=['UPerNet', 'None'],
|
18 |
+
type='value',
|
19 |
+
value='UPerNet')
|
20 |
+
num_samples = gr.Slider(label='Number of images',
|
21 |
+
minimum=1,
|
22 |
+
maximum=max_images,
|
23 |
+
value=default_num_images,
|
24 |
+
step=1)
|
25 |
+
image_resolution = gr.Slider(label='Image resolution',
|
26 |
+
minimum=256,
|
27 |
+
maximum=512,
|
28 |
+
value=512,
|
29 |
+
step=256)
|
30 |
+
preprocess_resolution = gr.Slider(
|
31 |
+
label='Preprocess resolution',
|
32 |
+
minimum=128,
|
33 |
+
maximum=512,
|
34 |
+
value=512,
|
35 |
+
step=1)
|
36 |
+
num_steps = gr.Slider(label='Number of steps',
|
37 |
+
minimum=1,
|
38 |
+
maximum=100,
|
39 |
+
value=20,
|
40 |
+
step=1)
|
41 |
+
guidance_scale = gr.Slider(label='Guidance scale',
|
42 |
+
minimum=0.1,
|
43 |
+
maximum=30.0,
|
44 |
+
value=9.0,
|
45 |
+
step=0.1)
|
46 |
+
seed = gr.Slider(label='Seed',
|
47 |
+
minimum=0,
|
48 |
+
maximum=1000000,
|
49 |
+
step=1,
|
50 |
+
value=0,
|
51 |
+
randomize=True)
|
52 |
+
randomize_seed = gr.Checkbox(label='Randomize seed',
|
53 |
+
value=True)
|
54 |
+
a_prompt = gr.Textbox(
|
55 |
+
label='Additional prompt',
|
56 |
+
value='best quality, extremely detailed')
|
57 |
+
n_prompt = gr.Textbox(
|
58 |
+
label='Negative prompt',
|
59 |
+
value=
|
60 |
+
'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
|
61 |
+
)
|
62 |
+
with gr.Column():
|
63 |
+
result = gr.Gallery(label='Output', show_label=False).style(
|
64 |
+
columns=2, object_fit='scale-down')
|
65 |
+
inputs = [
|
66 |
+
image,
|
67 |
+
prompt,
|
68 |
+
a_prompt,
|
69 |
+
n_prompt,
|
70 |
+
num_samples,
|
71 |
+
image_resolution,
|
72 |
+
preprocess_resolution,
|
73 |
+
num_steps,
|
74 |
+
guidance_scale,
|
75 |
+
seed,
|
76 |
+
preprocessor_name,
|
77 |
+
]
|
78 |
+
prompt.submit(
|
79 |
+
fn=randomize_seed_fn,
|
80 |
+
inputs=[seed, randomize_seed],
|
81 |
+
outputs=seed,
|
82 |
+
queue=False,
|
83 |
+
).then(
|
84 |
+
fn=process,
|
85 |
+
inputs=inputs,
|
86 |
+
outputs=result,
|
87 |
+
)
|
88 |
+
run_button.click(
|
89 |
+
fn=randomize_seed_fn,
|
90 |
+
inputs=[seed, randomize_seed],
|
91 |
+
outputs=seed,
|
92 |
+
queue=False,
|
93 |
+
).then(
|
94 |
+
fn=process,
|
95 |
+
inputs=inputs,
|
96 |
+
outputs=result,
|
97 |
+
api_name='segmentation',
|
98 |
+
)
|
99 |
+
return demo
|
100 |
+
|
101 |
+
|
102 |
+
if __name__ == '__main__':
|
103 |
+
from model import Model
|
104 |
+
model = Model(task_name='segmentation')
|
105 |
+
demo = create_demo(model.process_segmentation)
|
106 |
+
demo.queue().launch()
|
app_shuffle.py
ADDED
@@ -0,0 +1,100 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
import gradio as gr
|
4 |
+
|
5 |
+
from utils import randomize_seed_fn
|
6 |
+
|
7 |
+
|
8 |
+
def create_demo(process, max_images=12, default_num_images=3):
|
9 |
+
with gr.Blocks() as demo:
|
10 |
+
with gr.Row():
|
11 |
+
with gr.Column():
|
12 |
+
image = gr.Image()
|
13 |
+
prompt = gr.Textbox(label='Prompt')
|
14 |
+
run_button = gr.Button('Run')
|
15 |
+
with gr.Accordion('Advanced options', open=False):
|
16 |
+
preprocessor_name = gr.Radio(
|
17 |
+
label='Preprocessor',
|
18 |
+
choices=['ContentShuffle', 'None'],
|
19 |
+
type='value',
|
20 |
+
value='ContentShuffle')
|
21 |
+
num_samples = gr.Slider(label='Number of images',
|
22 |
+
minimum=1,
|
23 |
+
maximum=max_images,
|
24 |
+
value=default_num_images,
|
25 |
+
step=1)
|
26 |
+
image_resolution = gr.Slider(label='Image resolution',
|
27 |
+
minimum=256,
|
28 |
+
maximum=512,
|
29 |
+
value=512,
|
30 |
+
step=256)
|
31 |
+
num_steps = gr.Slider(label='Number of steps',
|
32 |
+
minimum=1,
|
33 |
+
maximum=100,
|
34 |
+
value=20,
|
35 |
+
step=1)
|
36 |
+
guidance_scale = gr.Slider(label='Guidance scale',
|
37 |
+
minimum=0.1,
|
38 |
+
maximum=30.0,
|
39 |
+
value=9.0,
|
40 |
+
step=0.1)
|
41 |
+
seed = gr.Slider(label='Seed',
|
42 |
+
minimum=0,
|
43 |
+
maximum=1000000,
|
44 |
+
step=1,
|
45 |
+
value=0,
|
46 |
+
randomize=True)
|
47 |
+
randomize_seed = gr.Checkbox(label='Randomize seed',
|
48 |
+
value=True)
|
49 |
+
a_prompt = gr.Textbox(
|
50 |
+
label='Additional prompt',
|
51 |
+
value='best quality, extremely detailed')
|
52 |
+
n_prompt = gr.Textbox(
|
53 |
+
label='Negative prompt',
|
54 |
+
value=
|
55 |
+
'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
|
56 |
+
)
|
57 |
+
with gr.Column():
|
58 |
+
result = gr.Gallery(label='Output', show_label=False).style(
|
59 |
+
columns=2, object_fit='scale-down')
|
60 |
+
inputs = [
|
61 |
+
image,
|
62 |
+
prompt,
|
63 |
+
a_prompt,
|
64 |
+
n_prompt,
|
65 |
+
num_samples,
|
66 |
+
image_resolution,
|
67 |
+
num_steps,
|
68 |
+
guidance_scale,
|
69 |
+
seed,
|
70 |
+
preprocessor_name,
|
71 |
+
]
|
72 |
+
prompt.submit(
|
73 |
+
fn=randomize_seed_fn,
|
74 |
+
inputs=[seed, randomize_seed],
|
75 |
+
outputs=seed,
|
76 |
+
queue=False,
|
77 |
+
).then(
|
78 |
+
fn=process,
|
79 |
+
inputs=inputs,
|
80 |
+
outputs=result,
|
81 |
+
)
|
82 |
+
run_button.click(
|
83 |
+
fn=randomize_seed_fn,
|
84 |
+
inputs=[seed, randomize_seed],
|
85 |
+
outputs=seed,
|
86 |
+
queue=False,
|
87 |
+
).then(
|
88 |
+
fn=process,
|
89 |
+
inputs=inputs,
|
90 |
+
outputs=result,
|
91 |
+
api_name='content-shuffle',
|
92 |
+
)
|
93 |
+
return demo
|
94 |
+
|
95 |
+
|
96 |
+
if __name__ == '__main__':
|
97 |
+
from model import Model
|
98 |
+
model = Model(task_name='shuffle')
|
99 |
+
demo = create_demo(model.process_shuffle)
|
100 |
+
demo.queue().launch()
|
app_softedge.py
ADDED
@@ -0,0 +1,112 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
import gradio as gr
|
4 |
+
|
5 |
+
from utils import randomize_seed_fn
|
6 |
+
|
7 |
+
|
8 |
+
def create_demo(process, max_images=12, default_num_images=3):
|
9 |
+
with gr.Blocks() as demo:
|
10 |
+
with gr.Row():
|
11 |
+
with gr.Column():
|
12 |
+
image = gr.Image()
|
13 |
+
prompt = gr.Textbox(label='Prompt')
|
14 |
+
run_button = gr.Button('Run')
|
15 |
+
with gr.Accordion('Advanced options', open=False):
|
16 |
+
preprocessor_name = gr.Radio(label='Preprocessor',
|
17 |
+
choices=[
|
18 |
+
'HED',
|
19 |
+
'PidiNet',
|
20 |
+
'HED safe',
|
21 |
+
'PidiNet safe',
|
22 |
+
'None',
|
23 |
+
],
|
24 |
+
type='value',
|
25 |
+
value='PidiNet')
|
26 |
+
num_samples = gr.Slider(label='Number of images',
|
27 |
+
minimum=1,
|
28 |
+
maximum=max_images,
|
29 |
+
value=default_num_images,
|
30 |
+
step=1)
|
31 |
+
image_resolution = gr.Slider(label='Image resolution',
|
32 |
+
minimum=256,
|
33 |
+
maximum=512,
|
34 |
+
value=512,
|
35 |
+
step=256)
|
36 |
+
preprocess_resolution = gr.Slider(
|
37 |
+
label='Preprocess resolution',
|
38 |
+
minimum=128,
|
39 |
+
maximum=512,
|
40 |
+
value=512,
|
41 |
+
step=1)
|
42 |
+
num_steps = gr.Slider(label='Number of steps',
|
43 |
+
minimum=1,
|
44 |
+
maximum=100,
|
45 |
+
value=20,
|
46 |
+
step=1)
|
47 |
+
guidance_scale = gr.Slider(label='Guidance scale',
|
48 |
+
minimum=0.1,
|
49 |
+
maximum=30.0,
|
50 |
+
value=9.0,
|
51 |
+
step=0.1)
|
52 |
+
seed = gr.Slider(label='Seed',
|
53 |
+
minimum=0,
|
54 |
+
maximum=1000000,
|
55 |
+
step=1,
|
56 |
+
value=0,
|
57 |
+
randomize=True)
|
58 |
+
randomize_seed = gr.Checkbox(label='Randomize seed',
|
59 |
+
value=True)
|
60 |
+
a_prompt = gr.Textbox(
|
61 |
+
label='Additional prompt',
|
62 |
+
value='best quality, extremely detailed')
|
63 |
+
n_prompt = gr.Textbox(
|
64 |
+
label='Negative prompt',
|
65 |
+
value=
|
66 |
+
'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
|
67 |
+
)
|
68 |
+
with gr.Column():
|
69 |
+
result = gr.Gallery(label='Output', show_label=False).style(
|
70 |
+
columns=2, object_fit='scale-down')
|
71 |
+
inputs = [
|
72 |
+
image,
|
73 |
+
prompt,
|
74 |
+
a_prompt,
|
75 |
+
n_prompt,
|
76 |
+
num_samples,
|
77 |
+
image_resolution,
|
78 |
+
preprocess_resolution,
|
79 |
+
num_steps,
|
80 |
+
guidance_scale,
|
81 |
+
seed,
|
82 |
+
preprocessor_name,
|
83 |
+
]
|
84 |
+
prompt.submit(
|
85 |
+
fn=randomize_seed_fn,
|
86 |
+
inputs=[seed, randomize_seed],
|
87 |
+
outputs=seed,
|
88 |
+
queue=False,
|
89 |
+
).then(
|
90 |
+
fn=process,
|
91 |
+
inputs=inputs,
|
92 |
+
outputs=result,
|
93 |
+
)
|
94 |
+
run_button.click(
|
95 |
+
fn=randomize_seed_fn,
|
96 |
+
inputs=[seed, randomize_seed],
|
97 |
+
outputs=seed,
|
98 |
+
queue=False,
|
99 |
+
).then(
|
100 |
+
fn=process,
|
101 |
+
inputs=inputs,
|
102 |
+
outputs=result,
|
103 |
+
api_name='softedge',
|
104 |
+
)
|
105 |
+
return demo
|
106 |
+
|
107 |
+
|
108 |
+
if __name__ == '__main__':
|
109 |
+
from model import Model
|
110 |
+
model = Model(task_name='softedge')
|
111 |
+
demo = create_demo(model.process_softedge)
|
112 |
+
demo.queue().launch()
|
cv_utils.py
ADDED
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import cv2
|
2 |
+
import numpy as np
|
3 |
+
|
4 |
+
|
5 |
+
def resize_image(input_image, resolution, interpolation=None):
|
6 |
+
H, W, C = input_image.shape
|
7 |
+
H = float(H)
|
8 |
+
W = float(W)
|
9 |
+
k = float(resolution) / max(H, W)
|
10 |
+
H *= k
|
11 |
+
W *= k
|
12 |
+
H = int(np.round(H / 64.0)) * 64
|
13 |
+
W = int(np.round(W / 64.0)) * 64
|
14 |
+
if interpolation is None:
|
15 |
+
interpolation = cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA
|
16 |
+
img = cv2.resize(input_image, (W, H), interpolation=interpolation)
|
17 |
+
return img
|
depth_estimator.py
ADDED
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import numpy as np
|
2 |
+
import PIL.Image
|
3 |
+
from controlnet_aux.util import HWC3
|
4 |
+
from transformers import pipeline
|
5 |
+
|
6 |
+
from cv_utils import resize_image
|
7 |
+
|
8 |
+
|
9 |
+
class DepthEstimator:
|
10 |
+
def __init__(self):
|
11 |
+
self.model = pipeline('depth-estimation')
|
12 |
+
|
13 |
+
def __call__(self, image: np.ndarray, **kwargs) -> PIL.Image.Image:
|
14 |
+
detect_resolution = kwargs.pop('detect_resolution', 512)
|
15 |
+
image_resolution = kwargs.pop('image_resolution', 512)
|
16 |
+
image = np.array(image)
|
17 |
+
image = HWC3(image)
|
18 |
+
image = resize_image(image, resolution=detect_resolution)
|
19 |
+
image = PIL.Image.fromarray(image)
|
20 |
+
image = self.model(image)
|
21 |
+
image = image['depth']
|
22 |
+
image = np.array(image)
|
23 |
+
image = HWC3(image)
|
24 |
+
image = resize_image(image, resolution=image_resolution)
|
25 |
+
return PIL.Image.fromarray(image)
|
image_segmentor.py
ADDED
@@ -0,0 +1,39 @@
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|
|
|
1 |
+
import cv2
|
2 |
+
import numpy as np
|
3 |
+
import PIL.Image
|
4 |
+
import torch
|
5 |
+
from controlnet_aux.util import HWC3, ade_palette
|
6 |
+
from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
|
7 |
+
|
8 |
+
from cv_utils import resize_image
|
9 |
+
|
10 |
+
|
11 |
+
class ImageSegmentor:
|
12 |
+
def __init__(self):
|
13 |
+
self.image_processor = AutoImageProcessor.from_pretrained(
|
14 |
+
'openmmlab/upernet-convnext-small')
|
15 |
+
self.image_segmentor = UperNetForSemanticSegmentation.from_pretrained(
|
16 |
+
'openmmlab/upernet-convnext-small')
|
17 |
+
|
18 |
+
@torch.inference_mode()
|
19 |
+
def __call__(self, image: np.ndarray, **kwargs) -> PIL.Image.Image:
|
20 |
+
detect_resolution = kwargs.pop('detect_resolution', 512)
|
21 |
+
image_resolution = kwargs.pop('image_resolution', 512)
|
22 |
+
image = HWC3(image)
|
23 |
+
image = resize_image(image, resolution=detect_resolution)
|
24 |
+
image = PIL.Image.fromarray(image)
|
25 |
+
|
26 |
+
pixel_values = self.image_processor(image,
|
27 |
+
return_tensors='pt').pixel_values
|
28 |
+
outputs = self.image_segmentor(pixel_values)
|
29 |
+
seg = self.image_processor.post_process_semantic_segmentation(
|
30 |
+
outputs, target_sizes=[image.size[::-1]])[0]
|
31 |
+
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
|
32 |
+
for label, color in enumerate(ade_palette()):
|
33 |
+
color_seg[seg == label, :] = color
|
34 |
+
color_seg = color_seg.astype(np.uint8)
|
35 |
+
|
36 |
+
color_seg = resize_image(color_seg,
|
37 |
+
resolution=image_resolution,
|
38 |
+
interpolation=cv2.INTER_NEAREST)
|
39 |
+
return PIL.Image.fromarray(color_seg)
|
model.py
ADDED
@@ -0,0 +1,591 @@
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|
|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from __future__ import annotations
|
2 |
+
|
3 |
+
import gc
|
4 |
+
|
5 |
+
import numpy as np
|
6 |
+
import PIL.Image
|
7 |
+
import torch
|
8 |
+
from controlnet_aux.util import HWC3
|
9 |
+
from diffusers import (ControlNetModel, DiffusionPipeline,
|
10 |
+
StableDiffusionControlNetPipeline,
|
11 |
+
UniPCMultistepScheduler)
|
12 |
+
|
13 |
+
from cv_utils import resize_image
|
14 |
+
from preprocessor import Preprocessor
|
15 |
+
|
16 |
+
CONTROLNET_MODEL_IDS = {
|
17 |
+
'Openpose': 'lllyasviel/control_v11p_sd15_openpose',
|
18 |
+
'Canny': 'lllyasviel/control_v11p_sd15_canny',
|
19 |
+
'MLSD': 'lllyasviel/control_v11p_sd15_mlsd',
|
20 |
+
'scribble': 'lllyasviel/control_v11p_sd15_scribble',
|
21 |
+
'softedge': 'lllyasviel/control_v11p_sd15_softedge',
|
22 |
+
'segmentation': 'lllyasviel/control_v11p_sd15_seg',
|
23 |
+
'depth': 'lllyasviel/control_v11f1p_sd15_depth',
|
24 |
+
'NormalBae': 'lllyasviel/control_v11p_sd15_normalbae',
|
25 |
+
'lineart': 'lllyasviel/control_v11p_sd15_lineart',
|
26 |
+
'lineart_anime': 'lllyasviel/control_v11p_sd15s2_lineart_anime',
|
27 |
+
'shuffle': 'lllyasviel/control_v11e_sd15_shuffle',
|
28 |
+
'ip2p': 'lllyasviel/control_v11e_sd15_ip2p',
|
29 |
+
'inpaint': 'lllyasviel/control_v11e_sd15_inpaint',
|
30 |
+
}
|
31 |
+
|
32 |
+
|
33 |
+
def download_all_controlnet_weights() -> None:
|
34 |
+
for model_id in CONTROLNET_MODEL_IDS.values():
|
35 |
+
ControlNetModel.from_pretrained(model_id)
|
36 |
+
|
37 |
+
|
38 |
+
class Model:
|
39 |
+
def __init__(self,
|
40 |
+
base_model_id: str = 'runwayml/stable-diffusion-v1-5',
|
41 |
+
task_name: str = 'Canny'):
|
42 |
+
self.device = torch.device(
|
43 |
+
'cuda:0' if torch.cuda.is_available() else 'cpu')
|
44 |
+
self.base_model_id = ''
|
45 |
+
self.task_name = ''
|
46 |
+
self.pipe = self.load_pipe(base_model_id, task_name)
|
47 |
+
self.preprocessor = Preprocessor()
|
48 |
+
|
49 |
+
def load_pipe(self, base_model_id: str, task_name) -> DiffusionPipeline:
|
50 |
+
if base_model_id == self.base_model_id and task_name == self.task_name and hasattr(
|
51 |
+
self, 'pipe') and self.pipe is not None:
|
52 |
+
return self.pipe
|
53 |
+
model_id = CONTROLNET_MODEL_IDS[task_name]
|
54 |
+
controlnet = ControlNetModel.from_pretrained(model_id,
|
55 |
+
torch_dtype=torch.float16)
|
56 |
+
pipe = StableDiffusionControlNetPipeline.from_pretrained(
|
57 |
+
base_model_id,
|
58 |
+
safety_checker=None,
|
59 |
+
controlnet=controlnet,
|
60 |
+
torch_dtype=torch.float16)
|
61 |
+
pipe.scheduler = UniPCMultistepScheduler.from_config(
|
62 |
+
pipe.scheduler.config)
|
63 |
+
if self.device.type == 'cuda':
|
64 |
+
pipe.enable_xformers_memory_efficient_attention()
|
65 |
+
pipe.to(self.device)
|
66 |
+
torch.cuda.empty_cache()
|
67 |
+
gc.collect()
|
68 |
+
self.base_model_id = base_model_id
|
69 |
+
self.task_name = task_name
|
70 |
+
return pipe
|
71 |
+
|
72 |
+
def set_base_model(self, base_model_id: str) -> str:
|
73 |
+
if not base_model_id or base_model_id == self.base_model_id:
|
74 |
+
return self.base_model_id
|
75 |
+
del self.pipe
|
76 |
+
torch.cuda.empty_cache()
|
77 |
+
gc.collect()
|
78 |
+
try:
|
79 |
+
self.pipe = self.load_pipe(base_model_id, self.task_name)
|
80 |
+
except Exception:
|
81 |
+
self.pipe = self.load_pipe(self.base_model_id, self.task_name)
|
82 |
+
return self.base_model_id
|
83 |
+
|
84 |
+
def load_controlnet_weight(self, task_name: str) -> None:
|
85 |
+
if task_name == self.task_name:
|
86 |
+
return
|
87 |
+
if self.pipe is not None and hasattr(self.pipe, 'controlnet'):
|
88 |
+
del self.pipe.controlnet
|
89 |
+
torch.cuda.empty_cache()
|
90 |
+
gc.collect()
|
91 |
+
model_id = CONTROLNET_MODEL_IDS[task_name]
|
92 |
+
controlnet = ControlNetModel.from_pretrained(model_id,
|
93 |
+
torch_dtype=torch.float16)
|
94 |
+
controlnet.to(self.device)
|
95 |
+
torch.cuda.empty_cache()
|
96 |
+
gc.collect()
|
97 |
+
self.pipe.controlnet = controlnet
|
98 |
+
self.task_name = task_name
|
99 |
+
|
100 |
+
def get_prompt(self, prompt: str, additional_prompt: str) -> str:
|
101 |
+
if not prompt:
|
102 |
+
prompt = additional_prompt
|
103 |
+
else:
|
104 |
+
prompt = f'{prompt}, {additional_prompt}'
|
105 |
+
return prompt
|
106 |
+
|
107 |
+
@torch.autocast('cuda')
|
108 |
+
def run_pipe(
|
109 |
+
self,
|
110 |
+
prompt: str,
|
111 |
+
negative_prompt: str,
|
112 |
+
control_image: PIL.Image.Image,
|
113 |
+
num_images: int,
|
114 |
+
num_steps: int,
|
115 |
+
guidance_scale: float,
|
116 |
+
seed: int,
|
117 |
+
) -> list[PIL.Image.Image]:
|
118 |
+
if seed == -1:
|
119 |
+
seed = np.random.randint(0, np.iinfo(np.int64).max)
|
120 |
+
generator = torch.Generator().manual_seed(seed)
|
121 |
+
return self.pipe(prompt=prompt,
|
122 |
+
negative_prompt=negative_prompt,
|
123 |
+
guidance_scale=guidance_scale,
|
124 |
+
num_images_per_prompt=num_images,
|
125 |
+
num_inference_steps=num_steps,
|
126 |
+
generator=generator,
|
127 |
+
image=control_image).images
|
128 |
+
|
129 |
+
@torch.inference_mode()
|
130 |
+
def process_canny(
|
131 |
+
self,
|
132 |
+
image: np.ndarray,
|
133 |
+
prompt: str,
|
134 |
+
additional_prompt: str,
|
135 |
+
negative_prompt: str,
|
136 |
+
num_images: int,
|
137 |
+
image_resolution: int,
|
138 |
+
num_steps: int,
|
139 |
+
guidance_scale: float,
|
140 |
+
seed: int,
|
141 |
+
low_threshold: int,
|
142 |
+
high_threshold: int,
|
143 |
+
) -> list[PIL.Image.Image]:
|
144 |
+
self.preprocessor.load('Canny')
|
145 |
+
control_image = self.preprocessor(image=image,
|
146 |
+
low_threshold=low_threshold,
|
147 |
+
high_threshold=high_threshold,
|
148 |
+
detect_resolution=image_resolution)
|
149 |
+
|
150 |
+
self.load_controlnet_weight('Canny')
|
151 |
+
results = self.run_pipe(
|
152 |
+
prompt=self.get_prompt(prompt, additional_prompt),
|
153 |
+
negative_prompt=negative_prompt,
|
154 |
+
control_image=control_image,
|
155 |
+
num_images=num_images,
|
156 |
+
num_steps=num_steps,
|
157 |
+
guidance_scale=guidance_scale,
|
158 |
+
seed=seed,
|
159 |
+
)
|
160 |
+
return [control_image] + results
|
161 |
+
|
162 |
+
@torch.inference_mode()
|
163 |
+
def process_mlsd(
|
164 |
+
self,
|
165 |
+
image: np.ndarray,
|
166 |
+
prompt: str,
|
167 |
+
additional_prompt: str,
|
168 |
+
negative_prompt: str,
|
169 |
+
num_images: int,
|
170 |
+
image_resolution: int,
|
171 |
+
preprocess_resolution: int,
|
172 |
+
num_steps: int,
|
173 |
+
guidance_scale: float,
|
174 |
+
seed: int,
|
175 |
+
value_threshold: float,
|
176 |
+
distance_threshold: float,
|
177 |
+
) -> list[PIL.Image.Image]:
|
178 |
+
self.preprocessor.load('MLSD')
|
179 |
+
control_image = self.preprocessor(
|
180 |
+
image=image,
|
181 |
+
image_resolution=image_resolution,
|
182 |
+
detect_resolution=preprocess_resolution,
|
183 |
+
thr_v=value_threshold,
|
184 |
+
thr_d=distance_threshold,
|
185 |
+
)
|
186 |
+
self.load_controlnet_weight('MLSD')
|
187 |
+
results = self.run_pipe(
|
188 |
+
prompt=self.get_prompt(prompt, additional_prompt),
|
189 |
+
negative_prompt=negative_prompt,
|
190 |
+
control_image=control_image,
|
191 |
+
num_images=num_images,
|
192 |
+
num_steps=num_steps,
|
193 |
+
guidance_scale=guidance_scale,
|
194 |
+
seed=seed,
|
195 |
+
)
|
196 |
+
return [control_image] + results
|
197 |
+
|
198 |
+
@torch.inference_mode()
|
199 |
+
def process_scribble(
|
200 |
+
self,
|
201 |
+
image: np.ndarray,
|
202 |
+
prompt: str,
|
203 |
+
additional_prompt: str,
|
204 |
+
negative_prompt: str,
|
205 |
+
num_images: int,
|
206 |
+
image_resolution: int,
|
207 |
+
preprocess_resolution: int,
|
208 |
+
num_steps: int,
|
209 |
+
guidance_scale: float,
|
210 |
+
seed: int,
|
211 |
+
preprocessor_name: str,
|
212 |
+
) -> list[PIL.Image.Image]:
|
213 |
+
if preprocessor_name == 'None':
|
214 |
+
image = HWC3(image)
|
215 |
+
image = resize_image(image, resolution=image_resolution)
|
216 |
+
control_image = PIL.Image.fromarray(image)
|
217 |
+
elif preprocessor_name == 'HED':
|
218 |
+
self.preprocessor.load(preprocessor_name)
|
219 |
+
control_image = self.preprocessor(
|
220 |
+
image=image,
|
221 |
+
image_resolution=image_resolution,
|
222 |
+
detect_resolution=preprocess_resolution,
|
223 |
+
scribble=False,
|
224 |
+
)
|
225 |
+
elif preprocessor_name == 'PidiNet':
|
226 |
+
self.preprocessor.load(preprocessor_name)
|
227 |
+
control_image = self.preprocessor(
|
228 |
+
image=image,
|
229 |
+
image_resolution=image_resolution,
|
230 |
+
detect_resolution=preprocess_resolution,
|
231 |
+
safe=False,
|
232 |
+
)
|
233 |
+
self.load_controlnet_weight('scribble')
|
234 |
+
results = self.run_pipe(
|
235 |
+
prompt=self.get_prompt(prompt, additional_prompt),
|
236 |
+
negative_prompt=negative_prompt,
|
237 |
+
control_image=control_image,
|
238 |
+
num_images=num_images,
|
239 |
+
num_steps=num_steps,
|
240 |
+
guidance_scale=guidance_scale,
|
241 |
+
seed=seed,
|
242 |
+
)
|
243 |
+
return [control_image] + results
|
244 |
+
|
245 |
+
@torch.inference_mode()
|
246 |
+
def process_scribble_interactive(
|
247 |
+
self,
|
248 |
+
image_and_mask: dict[str, np.ndarray],
|
249 |
+
prompt: str,
|
250 |
+
additional_prompt: str,
|
251 |
+
negative_prompt: str,
|
252 |
+
num_images: int,
|
253 |
+
image_resolution: int,
|
254 |
+
num_steps: int,
|
255 |
+
guidance_scale: float,
|
256 |
+
seed: int,
|
257 |
+
) -> list[PIL.Image.Image]:
|
258 |
+
image = image_and_mask['mask']
|
259 |
+
image = HWC3(image)
|
260 |
+
image = resize_image(image, resolution=image_resolution)
|
261 |
+
control_image = PIL.Image.fromarray(image)
|
262 |
+
|
263 |
+
self.load_controlnet_weight('scribble')
|
264 |
+
results = self.run_pipe(
|
265 |
+
prompt=self.get_prompt(prompt, additional_prompt),
|
266 |
+
negative_prompt=negative_prompt,
|
267 |
+
control_image=control_image,
|
268 |
+
num_images=num_images,
|
269 |
+
num_steps=num_steps,
|
270 |
+
guidance_scale=guidance_scale,
|
271 |
+
seed=seed,
|
272 |
+
)
|
273 |
+
return [control_image] + results
|
274 |
+
|
275 |
+
@torch.inference_mode()
|
276 |
+
def process_softedge(
|
277 |
+
self,
|
278 |
+
image: np.ndarray,
|
279 |
+
prompt: str,
|
280 |
+
additional_prompt: str,
|
281 |
+
negative_prompt: str,
|
282 |
+
num_images: int,
|
283 |
+
image_resolution: int,
|
284 |
+
preprocess_resolution: int,
|
285 |
+
num_steps: int,
|
286 |
+
guidance_scale: float,
|
287 |
+
seed: int,
|
288 |
+
preprocessor_name: str,
|
289 |
+
) -> list[PIL.Image.Image]:
|
290 |
+
if preprocessor_name == 'None':
|
291 |
+
image = HWC3(image)
|
292 |
+
image = resize_image(image, resolution=image_resolution)
|
293 |
+
control_image = PIL.Image.fromarray(image)
|
294 |
+
elif preprocessor_name in ['HED', 'HED safe']:
|
295 |
+
safe = 'safe' in preprocessor_name
|
296 |
+
self.preprocessor.load('HED')
|
297 |
+
control_image = self.preprocessor(
|
298 |
+
image=image,
|
299 |
+
image_resolution=image_resolution,
|
300 |
+
detect_resolution=preprocess_resolution,
|
301 |
+
scribble=safe,
|
302 |
+
)
|
303 |
+
elif preprocessor_name in ['PidiNet', 'PidiNet safe']:
|
304 |
+
safe = 'safe' in preprocessor_name
|
305 |
+
self.preprocessor.load('PidiNet')
|
306 |
+
control_image = self.preprocessor(
|
307 |
+
image=image,
|
308 |
+
image_resolution=image_resolution,
|
309 |
+
detect_resolution=preprocess_resolution,
|
310 |
+
safe=safe,
|
311 |
+
)
|
312 |
+
else:
|
313 |
+
raise ValueError
|
314 |
+
self.load_controlnet_weight('softedge')
|
315 |
+
results = self.run_pipe(
|
316 |
+
prompt=self.get_prompt(prompt, additional_prompt),
|
317 |
+
negative_prompt=negative_prompt,
|
318 |
+
control_image=control_image,
|
319 |
+
num_images=num_images,
|
320 |
+
num_steps=num_steps,
|
321 |
+
guidance_scale=guidance_scale,
|
322 |
+
seed=seed,
|
323 |
+
)
|
324 |
+
return [control_image] + results
|
325 |
+
|
326 |
+
@torch.inference_mode()
|
327 |
+
def process_openpose(
|
328 |
+
self,
|
329 |
+
image: np.ndarray,
|
330 |
+
prompt: str,
|
331 |
+
additional_prompt: str,
|
332 |
+
negative_prompt: str,
|
333 |
+
num_images: int,
|
334 |
+
image_resolution: int,
|
335 |
+
preprocess_resolution: int,
|
336 |
+
num_steps: int,
|
337 |
+
guidance_scale: float,
|
338 |
+
seed: int,
|
339 |
+
preprocessor_name: str,
|
340 |
+
) -> list[PIL.Image.Image]:
|
341 |
+
if preprocessor_name == 'None':
|
342 |
+
image = HWC3(image)
|
343 |
+
image = resize_image(image, resolution=image_resolution)
|
344 |
+
control_image = PIL.Image.fromarray(image)
|
345 |
+
else:
|
346 |
+
self.preprocessor.load('Openpose')
|
347 |
+
control_image = self.preprocessor(
|
348 |
+
image=image,
|
349 |
+
image_resolution=image_resolution,
|
350 |
+
detect_resolution=preprocess_resolution,
|
351 |
+
hand_and_face=True,
|
352 |
+
)
|
353 |
+
self.load_controlnet_weight('Openpose')
|
354 |
+
results = self.run_pipe(
|
355 |
+
prompt=self.get_prompt(prompt, additional_prompt),
|
356 |
+
negative_prompt=negative_prompt,
|
357 |
+
control_image=control_image,
|
358 |
+
num_images=num_images,
|
359 |
+
num_steps=num_steps,
|
360 |
+
guidance_scale=guidance_scale,
|
361 |
+
seed=seed,
|
362 |
+
)
|
363 |
+
return [control_image] + results
|
364 |
+
|
365 |
+
@torch.inference_mode()
|
366 |
+
def process_segmentation(
|
367 |
+
self,
|
368 |
+
image: np.ndarray,
|
369 |
+
prompt: str,
|
370 |
+
additional_prompt: str,
|
371 |
+
negative_prompt: str,
|
372 |
+
num_images: int,
|
373 |
+
image_resolution: int,
|
374 |
+
preprocess_resolution: int,
|
375 |
+
num_steps: int,
|
376 |
+
guidance_scale: float,
|
377 |
+
seed: int,
|
378 |
+
preprocessor_name: str,
|
379 |
+
) -> list[PIL.Image.Image]:
|
380 |
+
if preprocessor_name == 'None':
|
381 |
+
image = HWC3(image)
|
382 |
+
image = resize_image(image, resolution=image_resolution)
|
383 |
+
control_image = PIL.Image.fromarray(image)
|
384 |
+
else:
|
385 |
+
self.preprocessor.load(preprocessor_name)
|
386 |
+
control_image = self.preprocessor(
|
387 |
+
image=image,
|
388 |
+
image_resolution=image_resolution,
|
389 |
+
detect_resolution=preprocess_resolution,
|
390 |
+
)
|
391 |
+
self.load_controlnet_weight('segmentation')
|
392 |
+
results = self.run_pipe(
|
393 |
+
prompt=self.get_prompt(prompt, additional_prompt),
|
394 |
+
negative_prompt=negative_prompt,
|
395 |
+
control_image=control_image,
|
396 |
+
num_images=num_images,
|
397 |
+
num_steps=num_steps,
|
398 |
+
guidance_scale=guidance_scale,
|
399 |
+
seed=seed,
|
400 |
+
)
|
401 |
+
return [control_image] + results
|
402 |
+
|
403 |
+
@torch.inference_mode()
|
404 |
+
def process_depth(
|
405 |
+
self,
|
406 |
+
image: np.ndarray,
|
407 |
+
prompt: str,
|
408 |
+
additional_prompt: str,
|
409 |
+
negative_prompt: str,
|
410 |
+
num_images: int,
|
411 |
+
image_resolution: int,
|
412 |
+
preprocess_resolution: int,
|
413 |
+
num_steps: int,
|
414 |
+
guidance_scale: float,
|
415 |
+
seed: int,
|
416 |
+
preprocessor_name: str,
|
417 |
+
) -> list[PIL.Image.Image]:
|
418 |
+
if preprocessor_name == 'None':
|
419 |
+
image = HWC3(image)
|
420 |
+
image = resize_image(image, resolution=image_resolution)
|
421 |
+
control_image = PIL.Image.fromarray(image)
|
422 |
+
else:
|
423 |
+
self.preprocessor.load(preprocessor_name)
|
424 |
+
control_image = self.preprocessor(
|
425 |
+
image=image,
|
426 |
+
image_resolution=image_resolution,
|
427 |
+
detect_resolution=preprocess_resolution,
|
428 |
+
)
|
429 |
+
self.load_controlnet_weight('depth')
|
430 |
+
results = self.run_pipe(
|
431 |
+
prompt=self.get_prompt(prompt, additional_prompt),
|
432 |
+
negative_prompt=negative_prompt,
|
433 |
+
control_image=control_image,
|
434 |
+
num_images=num_images,
|
435 |
+
num_steps=num_steps,
|
436 |
+
guidance_scale=guidance_scale,
|
437 |
+
seed=seed,
|
438 |
+
)
|
439 |
+
return [control_image] + results
|
440 |
+
|
441 |
+
@torch.inference_mode()
|
442 |
+
def process_normal(
|
443 |
+
self,
|
444 |
+
image: np.ndarray,
|
445 |
+
prompt: str,
|
446 |
+
additional_prompt: str,
|
447 |
+
negative_prompt: str,
|
448 |
+
num_images: int,
|
449 |
+
image_resolution: int,
|
450 |
+
preprocess_resolution: int,
|
451 |
+
num_steps: int,
|
452 |
+
guidance_scale: float,
|
453 |
+
seed: int,
|
454 |
+
preprocessor_name: str,
|
455 |
+
) -> list[PIL.Image.Image]:
|
456 |
+
if preprocessor_name == 'None':
|
457 |
+
image = HWC3(image)
|
458 |
+
image = resize_image(image, resolution=image_resolution)
|
459 |
+
control_image = PIL.Image.fromarray(image)
|
460 |
+
else:
|
461 |
+
self.preprocessor.load('NormalBae')
|
462 |
+
control_image = self.preprocessor(
|
463 |
+
image=image,
|
464 |
+
image_resolution=image_resolution,
|
465 |
+
detect_resolution=preprocess_resolution,
|
466 |
+
)
|
467 |
+
self.load_controlnet_weight('NormalBae')
|
468 |
+
results = self.run_pipe(
|
469 |
+
prompt=self.get_prompt(prompt, additional_prompt),
|
470 |
+
negative_prompt=negative_prompt,
|
471 |
+
control_image=control_image,
|
472 |
+
num_images=num_images,
|
473 |
+
num_steps=num_steps,
|
474 |
+
guidance_scale=guidance_scale,
|
475 |
+
seed=seed,
|
476 |
+
)
|
477 |
+
return [control_image] + results
|
478 |
+
|
479 |
+
@torch.inference_mode()
|
480 |
+
def process_lineart(
|
481 |
+
self,
|
482 |
+
image: np.ndarray,
|
483 |
+
prompt: str,
|
484 |
+
additional_prompt: str,
|
485 |
+
negative_prompt: str,
|
486 |
+
num_images: int,
|
487 |
+
image_resolution: int,
|
488 |
+
preprocess_resolution: int,
|
489 |
+
num_steps: int,
|
490 |
+
guidance_scale: float,
|
491 |
+
seed: int,
|
492 |
+
preprocessor_name: str,
|
493 |
+
) -> list[PIL.Image.Image]:
|
494 |
+
if preprocessor_name in ['None', 'None (anime)']:
|
495 |
+
image = HWC3(image)
|
496 |
+
image = resize_image(image, resolution=image_resolution)
|
497 |
+
control_image = PIL.Image.fromarray(image)
|
498 |
+
elif preprocessor_name in ['Lineart', 'Lineart coarse']:
|
499 |
+
coarse = 'coarse' in preprocessor_name
|
500 |
+
self.preprocessor.load('Lineart')
|
501 |
+
control_image = self.preprocessor(
|
502 |
+
image=image,
|
503 |
+
image_resolution=image_resolution,
|
504 |
+
detect_resolution=preprocess_resolution,
|
505 |
+
coarse=coarse,
|
506 |
+
)
|
507 |
+
elif preprocessor_name == 'Lineart (anime)':
|
508 |
+
self.preprocessor.load('LineartAnime')
|
509 |
+
control_image = self.preprocessor(
|
510 |
+
image=image,
|
511 |
+
image_resolution=image_resolution,
|
512 |
+
detect_resolution=preprocess_resolution,
|
513 |
+
)
|
514 |
+
if 'anime' in preprocessor_name:
|
515 |
+
self.load_controlnet_weight('lineart_anime')
|
516 |
+
else:
|
517 |
+
self.load_controlnet_weight('lineart')
|
518 |
+
results = self.run_pipe(
|
519 |
+
prompt=self.get_prompt(prompt, additional_prompt),
|
520 |
+
negative_prompt=negative_prompt,
|
521 |
+
control_image=control_image,
|
522 |
+
num_images=num_images,
|
523 |
+
num_steps=num_steps,
|
524 |
+
guidance_scale=guidance_scale,
|
525 |
+
seed=seed,
|
526 |
+
)
|
527 |
+
return [control_image] + results
|
528 |
+
|
529 |
+
@torch.inference_mode()
|
530 |
+
def process_shuffle(
|
531 |
+
self,
|
532 |
+
image: np.ndarray,
|
533 |
+
prompt: str,
|
534 |
+
additional_prompt: str,
|
535 |
+
negative_prompt: str,
|
536 |
+
num_images: int,
|
537 |
+
image_resolution: int,
|
538 |
+
num_steps: int,
|
539 |
+
guidance_scale: float,
|
540 |
+
seed: int,
|
541 |
+
preprocessor_name: str,
|
542 |
+
) -> list[PIL.Image.Image]:
|
543 |
+
if preprocessor_name == 'None':
|
544 |
+
image = HWC3(image)
|
545 |
+
image = resize_image(image, resolution=image_resolution)
|
546 |
+
control_image = PIL.Image.fromarray(image)
|
547 |
+
else:
|
548 |
+
self.preprocessor.load(preprocessor_name)
|
549 |
+
control_image = self.preprocessor(
|
550 |
+
image=image,
|
551 |
+
image_resolution=image_resolution,
|
552 |
+
)
|
553 |
+
self.load_controlnet_weight('shuffle')
|
554 |
+
results = self.run_pipe(
|
555 |
+
prompt=self.get_prompt(prompt, additional_prompt),
|
556 |
+
negative_prompt=negative_prompt,
|
557 |
+
control_image=control_image,
|
558 |
+
num_images=num_images,
|
559 |
+
num_steps=num_steps,
|
560 |
+
guidance_scale=guidance_scale,
|
561 |
+
seed=seed,
|
562 |
+
)
|
563 |
+
return [control_image] + results
|
564 |
+
|
565 |
+
@torch.inference_mode()
|
566 |
+
def process_ip2p(
|
567 |
+
self,
|
568 |
+
image: np.ndarray,
|
569 |
+
prompt: str,
|
570 |
+
additional_prompt: str,
|
571 |
+
negative_prompt: str,
|
572 |
+
num_images: int,
|
573 |
+
image_resolution: int,
|
574 |
+
num_steps: int,
|
575 |
+
guidance_scale: float,
|
576 |
+
seed: int,
|
577 |
+
) -> list[PIL.Image.Image]:
|
578 |
+
image = HWC3(image)
|
579 |
+
image = resize_image(image, resolution=image_resolution)
|
580 |
+
control_image = PIL.Image.fromarray(image)
|
581 |
+
self.load_controlnet_weight('ip2p')
|
582 |
+
results = self.run_pipe(
|
583 |
+
prompt=self.get_prompt(prompt, additional_prompt),
|
584 |
+
negative_prompt=negative_prompt,
|
585 |
+
control_image=control_image,
|
586 |
+
num_images=num_images,
|
587 |
+
num_steps=num_steps,
|
588 |
+
guidance_scale=guidance_scale,
|
589 |
+
seed=seed,
|
590 |
+
)
|
591 |
+
return [control_image] + results
|
notebooks/notebook.ipynb
ADDED
@@ -0,0 +1,69 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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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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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"cells": [
|
3 |
+
{
|
4 |
+
"cell_type": "code",
|
5 |
+
"execution_count": null,
|
6 |
+
"metadata": {
|
7 |
+
"id": "8CnkIPtjn8Dc"
|
8 |
+
},
|
9 |
+
"outputs": [],
|
10 |
+
"source": [
|
11 |
+
"!git clone --recursive https://huggingface.co/spaces/hysts/ControlNet-v1-1"
|
12 |
+
]
|
13 |
+
},
|
14 |
+
{
|
15 |
+
"cell_type": "code",
|
16 |
+
"execution_count": null,
|
17 |
+
"metadata": {
|
18 |
+
"id": "IZlaYNTWoFPK"
|
19 |
+
},
|
20 |
+
"outputs": [],
|
21 |
+
"source": [
|
22 |
+
"%cd ControlNet-v1-1"
|
23 |
+
]
|
24 |
+
},
|
25 |
+
{
|
26 |
+
"cell_type": "code",
|
27 |
+
"execution_count": null,
|
28 |
+
"metadata": {
|
29 |
+
"id": "P_fzYrLvoIcI"
|
30 |
+
},
|
31 |
+
"outputs": [],
|
32 |
+
"source": [
|
33 |
+
"!pip install -q -r requirements.txt"
|
34 |
+
]
|
35 |
+
},
|
36 |
+
{
|
37 |
+
"cell_type": "code",
|
38 |
+
"execution_count": null,
|
39 |
+
"metadata": {
|
40 |
+
"id": "GOfGng5Woktd"
|
41 |
+
},
|
42 |
+
"outputs": [],
|
43 |
+
"source": [
|
44 |
+
"import app"
|
45 |
+
]
|
46 |
+
},
|
47 |
+
{
|
48 |
+
"cell_type": "code",
|
49 |
+
"execution_count": null,
|
50 |
+
"metadata": {
|
51 |
+
"id": "7Cued230ol7T"
|
52 |
+
},
|
53 |
+
"outputs": [],
|
54 |
+
"source": []
|
55 |
+
}
|
56 |
+
],
|
57 |
+
"metadata": {
|
58 |
+
"accelerator": "GPU",
|
59 |
+
"colab": {
|
60 |
+
"provenance": []
|
61 |
+
},
|
62 |
+
"gpuClass": "standard",
|
63 |
+
"language_info": {
|
64 |
+
"name": "python"
|
65 |
+
}
|
66 |
+
},
|
67 |
+
"nbformat": 4,
|
68 |
+
"nbformat_minor": 0
|
69 |
+
}
|
preprocessor.py
ADDED
@@ -0,0 +1,77 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import gc
|
2 |
+
|
3 |
+
import numpy as np
|
4 |
+
import PIL.Image
|
5 |
+
import torch
|
6 |
+
from controlnet_aux import (CannyDetector, ContentShuffleDetector, HEDdetector,
|
7 |
+
LineartAnimeDetector, LineartDetector,
|
8 |
+
MidasDetector, MLSDdetector, NormalBaeDetector,
|
9 |
+
OpenposeDetector, PidiNetDetector)
|
10 |
+
from controlnet_aux.util import HWC3
|
11 |
+
|
12 |
+
from cv_utils import resize_image
|
13 |
+
from depth_estimator import DepthEstimator
|
14 |
+
from image_segmentor import ImageSegmentor
|
15 |
+
|
16 |
+
|
17 |
+
class Preprocessor:
|
18 |
+
MODEL_ID = 'lllyasviel/Annotators'
|
19 |
+
|
20 |
+
def __init__(self):
|
21 |
+
self.model = None
|
22 |
+
self.name = ''
|
23 |
+
|
24 |
+
def load(self, name: str) -> None:
|
25 |
+
if name == self.name:
|
26 |
+
return
|
27 |
+
if name == 'HED':
|
28 |
+
self.model = HEDdetector.from_pretrained(self.MODEL_ID)
|
29 |
+
elif name == 'Midas':
|
30 |
+
self.model = MidasDetector.from_pretrained(self.MODEL_ID)
|
31 |
+
elif name == 'MLSD':
|
32 |
+
self.model = MLSDdetector.from_pretrained(self.MODEL_ID)
|
33 |
+
elif name == 'Openpose':
|
34 |
+
self.model = OpenposeDetector.from_pretrained(self.MODEL_ID)
|
35 |
+
elif name == 'PidiNet':
|
36 |
+
self.model = PidiNetDetector.from_pretrained(self.MODEL_ID)
|
37 |
+
elif name == 'NormalBae':
|
38 |
+
self.model = NormalBaeDetector.from_pretrained(self.MODEL_ID)
|
39 |
+
elif name == 'Lineart':
|
40 |
+
self.model = LineartDetector.from_pretrained(self.MODEL_ID)
|
41 |
+
elif name == 'LineartAnime':
|
42 |
+
self.model = LineartAnimeDetector.from_pretrained(self.MODEL_ID)
|
43 |
+
elif name == 'Canny':
|
44 |
+
self.model = CannyDetector()
|
45 |
+
elif name == 'ContentShuffle':
|
46 |
+
self.model = ContentShuffleDetector()
|
47 |
+
elif name == 'DPT':
|
48 |
+
self.model = DepthEstimator()
|
49 |
+
elif name == 'UPerNet':
|
50 |
+
self.model = ImageSegmentor()
|
51 |
+
else:
|
52 |
+
raise ValueError
|
53 |
+
torch.cuda.empty_cache()
|
54 |
+
gc.collect()
|
55 |
+
self.name = name
|
56 |
+
|
57 |
+
def __call__(self, image: PIL.Image.Image, **kwargs) -> PIL.Image.Image:
|
58 |
+
if self.name == 'Canny':
|
59 |
+
if 'detect_resolution' in kwargs:
|
60 |
+
detect_resolution = kwargs.pop('detect_resolution')
|
61 |
+
image = np.array(image)
|
62 |
+
image = HWC3(image)
|
63 |
+
image = resize_image(image, resolution=detect_resolution)
|
64 |
+
image = self.model(image, **kwargs)
|
65 |
+
return PIL.Image.fromarray(image)
|
66 |
+
elif self.name == 'Midas':
|
67 |
+
detect_resolution = kwargs.pop('detect_resolution', 512)
|
68 |
+
image_resolution = kwargs.pop('image_resolution', 512)
|
69 |
+
image = np.array(image)
|
70 |
+
image = HWC3(image)
|
71 |
+
image = resize_image(image, resolution=detect_resolution)
|
72 |
+
image = self.model(image, **kwargs)
|
73 |
+
image = HWC3(image)
|
74 |
+
image = resize_image(image, resolution=image_resolution)
|
75 |
+
return PIL.Image.fromarray(image)
|
76 |
+
else:
|
77 |
+
return self.model(image, **kwargs)
|
requirements.txt
ADDED
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
accelerate==0.20.3
|
2 |
+
controlnet_aux==0.0.5
|
3 |
+
diffusers==0.17.1
|
4 |
+
einops==0.6.1
|
5 |
+
gradio==3.34.0
|
6 |
+
huggingface-hub==0.14.1
|
7 |
+
opencv-python-headless==4.7.0.72
|
8 |
+
safetensors==0.3.1
|
9 |
+
torch==2.0.1
|
10 |
+
torchvision==0.15.2
|
11 |
+
transformers==4.30.2
|
12 |
+
xformers==0.0.20
|
style.css
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
h1 {
|
2 |
+
text-align: center;
|
3 |
+
}
|
utils.py
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import random
|
2 |
+
|
3 |
+
|
4 |
+
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
|
5 |
+
if randomize_seed:
|
6 |
+
seed = random.randint(0, 1000000)
|
7 |
+
return seed
|