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.gitattributes CHANGED
@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ *.jpg filter=lfs diff=lfs merge=lfs -text
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+ *.jpeg filter=lfs diff=lfs merge=lfs -text
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+ *.ong filter=lfs diff=lfs merge=lfs -text
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+ *.png filter=lfs diff=lfs merge=lfs -text
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+ *.webp filter=lfs diff=lfs merge=lfs -text
.gitignore ADDED
@@ -0,0 +1,162 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ *.safetensors
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+
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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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+
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+ # C extensions
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+ *.so
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+
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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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+
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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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+
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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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+
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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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+
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+ # Translations
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+ *.mo
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+ *.pot
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+
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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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+
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+ # Flask stuff:
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+ instance/
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+ .webassets-cache
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+
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+ # Scrapy stuff:
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+ .scrapy
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+
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+ # Sphinx documentation
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+ docs/_build/
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+
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+ # PyBuilder
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+ .pybuilder/
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+ target/
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+
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+ # Jupyter Notebook
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+ .ipynb_checkpoints
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+
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+ # IPython
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+ profile_default/
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+ ipython_config.py
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+
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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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+
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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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+
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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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+
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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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+ # in version control.
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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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+ # Celery stuff
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+ celerybeat-schedule
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+ celerybeat.pid
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+
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+ # SageMath parsed files
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+ *.sage.py
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+
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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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+
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+ # Spyder project settings
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+ .spyderproject
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+ .spyproject
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+
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+ # Rope project settings
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+ .ropeproject
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+
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+ # mkdocs documentation
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+ /site
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+
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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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+
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+ # Pyre type checker
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+ .pyre/
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+
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+ # pytype static type analyzer
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+ .pytype/
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+
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+ # Cython debug symbols
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+ cython_debug/
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+
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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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app.py CHANGED
@@ -1,13 +1,429 @@
 
 
 
1
  import torch
2
- print(f"Is CUDA available: {torch.cuda.is_available()}")
3
- # True
4
- print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
5
- # Tesla T4
6
 
7
- import gradio as gr
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8
 
9
- def greet(name):
10
- return "Hello " + name + "!!"
11
 
12
- demo = gr.Interface(fn=greet, inputs="text", outputs="text")
13
- demo.launch()
 
1
+ import math
2
+ import gradio as gr
3
+ import numpy as np
4
  import torch
5
+ import safetensors.torch as sf
6
+ import db_examples
 
 
7
 
8
+ from PIL import Image
9
+ from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
10
+ from diffusers import AutoencoderKL, UNet2DConditionModel, DDIMScheduler, EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler
11
+ from diffusers.models.attention_processor import AttnProcessor2_0
12
+ from transformers import CLIPTextModel, CLIPTokenizer
13
+ from briarmbg import BriaRMBG
14
+ from enum import Enum
15
+ from torch.hub import download_url_to_file
16
+
17
+
18
+ # 'stablediffusionapi/realistic-vision-v51'
19
+ # 'runwayml/stable-diffusion-v1-5'
20
+ sd15_name = 'stablediffusionapi/realistic-vision-v51'
21
+ tokenizer = CLIPTokenizer.from_pretrained(sd15_name, subfolder="tokenizer")
22
+ text_encoder = CLIPTextModel.from_pretrained(sd15_name, subfolder="text_encoder")
23
+ vae = AutoencoderKL.from_pretrained(sd15_name, subfolder="vae")
24
+ unet = UNet2DConditionModel.from_pretrained(sd15_name, subfolder="unet")
25
+ rmbg = BriaRMBG.from_pretrained("briaai/RMBG-1.4")
26
+
27
+ # Change UNet
28
+
29
+ with torch.no_grad():
30
+ new_conv_in = torch.nn.Conv2d(8, unet.conv_in.out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding)
31
+ new_conv_in.weight.zero_()
32
+ new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)
33
+ new_conv_in.bias = unet.conv_in.bias
34
+ unet.conv_in = new_conv_in
35
+
36
+ unet_original_forward = unet.forward
37
+
38
+
39
+ def hooked_unet_forward(sample, timestep, encoder_hidden_states, **kwargs):
40
+ c_concat = kwargs['cross_attention_kwargs']['concat_conds'].to(sample)
41
+ c_concat = torch.cat([c_concat] * (sample.shape[0] // c_concat.shape[0]), dim=0)
42
+ new_sample = torch.cat([sample, c_concat], dim=1)
43
+ kwargs['cross_attention_kwargs'] = {}
44
+ return unet_original_forward(new_sample, timestep, encoder_hidden_states, **kwargs)
45
+
46
+
47
+ unet.forward = hooked_unet_forward
48
+
49
+ # Load
50
+
51
+ model_path = './models/iclight_sd15_fc.safetensors'
52
+ download_url_to_file(url='https://huggingface.co/lllyasviel/ic-light/resolve/main/iclight_sd15_fc.safetensors', dst=model_path)
53
+ sd_offset = sf.load_file(model_path)
54
+ sd_origin = unet.state_dict()
55
+ keys = sd_origin.keys()
56
+ sd_merged = {k: sd_origin[k] + sd_offset[k] for k in sd_origin.keys()}
57
+ unet.load_state_dict(sd_merged, strict=True)
58
+ del sd_offset, sd_origin, sd_merged, keys
59
+
60
+ # Device
61
+
62
+ device = torch.device('cuda')
63
+ text_encoder = text_encoder.to(device=device, dtype=torch.float16)
64
+ vae = vae.to(device=device, dtype=torch.bfloat16)
65
+ unet = unet.to(device=device, dtype=torch.float16)
66
+ rmbg = rmbg.to(device=device, dtype=torch.float32)
67
+
68
+ # SDP
69
+
70
+ unet.set_attn_processor(AttnProcessor2_0())
71
+ vae.set_attn_processor(AttnProcessor2_0())
72
+
73
+ # Samplers
74
+
75
+ ddim_scheduler = DDIMScheduler(
76
+ num_train_timesteps=1000,
77
+ beta_start=0.00085,
78
+ beta_end=0.012,
79
+ beta_schedule="scaled_linear",
80
+ clip_sample=False,
81
+ set_alpha_to_one=False,
82
+ steps_offset=1,
83
+ )
84
+
85
+ euler_a_scheduler = EulerAncestralDiscreteScheduler(
86
+ num_train_timesteps=1000,
87
+ beta_start=0.00085,
88
+ beta_end=0.012,
89
+ steps_offset=1
90
+ )
91
+
92
+ dpmpp_2m_sde_karras_scheduler = DPMSolverMultistepScheduler(
93
+ num_train_timesteps=1000,
94
+ beta_start=0.00085,
95
+ beta_end=0.012,
96
+ algorithm_type="sde-dpmsolver++",
97
+ use_karras_sigmas=True,
98
+ steps_offset=1
99
+ )
100
+
101
+ # Pipelines
102
+
103
+ t2i_pipe = StableDiffusionPipeline(
104
+ vae=vae,
105
+ text_encoder=text_encoder,
106
+ tokenizer=tokenizer,
107
+ unet=unet,
108
+ scheduler=dpmpp_2m_sde_karras_scheduler,
109
+ safety_checker=None,
110
+ requires_safety_checker=False,
111
+ feature_extractor=None,
112
+ image_encoder=None
113
+ )
114
+
115
+ i2i_pipe = StableDiffusionImg2ImgPipeline(
116
+ vae=vae,
117
+ text_encoder=text_encoder,
118
+ tokenizer=tokenizer,
119
+ unet=unet,
120
+ scheduler=dpmpp_2m_sde_karras_scheduler,
121
+ safety_checker=None,
122
+ requires_safety_checker=False,
123
+ feature_extractor=None,
124
+ image_encoder=None
125
+ )
126
+
127
+
128
+ @torch.inference_mode()
129
+ def encode_prompt_inner(txt: str):
130
+ max_length = tokenizer.model_max_length
131
+ chunk_length = tokenizer.model_max_length - 2
132
+ id_start = tokenizer.bos_token_id
133
+ id_end = tokenizer.eos_token_id
134
+ id_pad = id_end
135
+
136
+ def pad(x, p, i):
137
+ return x[:i] if len(x) >= i else x + [p] * (i - len(x))
138
+
139
+ tokens = tokenizer(txt, truncation=False, add_special_tokens=False)["input_ids"]
140
+ chunks = [[id_start] + tokens[i: i + chunk_length] + [id_end] for i in range(0, len(tokens), chunk_length)]
141
+ chunks = [pad(ck, id_pad, max_length) for ck in chunks]
142
+
143
+ token_ids = torch.tensor(chunks).to(device=device, dtype=torch.int64)
144
+ conds = text_encoder(token_ids).last_hidden_state
145
+
146
+ return conds
147
+
148
+
149
+ @torch.inference_mode()
150
+ def encode_prompt_pair(positive_prompt, negative_prompt):
151
+ c = encode_prompt_inner(positive_prompt)
152
+ uc = encode_prompt_inner(negative_prompt)
153
+
154
+ c_len = float(len(c))
155
+ uc_len = float(len(uc))
156
+ max_count = max(c_len, uc_len)
157
+ c_repeat = int(math.ceil(max_count / c_len))
158
+ uc_repeat = int(math.ceil(max_count / uc_len))
159
+ max_chunk = max(len(c), len(uc))
160
+
161
+ c = torch.cat([c] * c_repeat, dim=0)[:max_chunk]
162
+ uc = torch.cat([uc] * uc_repeat, dim=0)[:max_chunk]
163
+
164
+ c = torch.cat([p[None, ...] for p in c], dim=1)
165
+ uc = torch.cat([p[None, ...] for p in uc], dim=1)
166
+
167
+ return c, uc
168
+
169
+
170
+ @torch.inference_mode()
171
+ def pytorch2numpy(imgs, quant=True):
172
+ results = []
173
+ for x in imgs:
174
+ y = x.movedim(0, -1)
175
+
176
+ if quant:
177
+ y = y * 127.5 + 127.5
178
+ y = y.detach().float().cpu().numpy().clip(0, 255).astype(np.uint8)
179
+ else:
180
+ y = y * 0.5 + 0.5
181
+ y = y.detach().float().cpu().numpy().clip(0, 1).astype(np.float32)
182
+
183
+ results.append(y)
184
+ return results
185
+
186
+
187
+ @torch.inference_mode()
188
+ def numpy2pytorch(imgs):
189
+ h = torch.from_numpy(np.stack(imgs, axis=0)).float() / 127.0 - 1.0 # so that 127 must be strictly 0.0
190
+ h = h.movedim(-1, 1)
191
+ return h
192
+
193
+
194
+ def resize_and_center_crop(image, target_width, target_height):
195
+ pil_image = Image.fromarray(image)
196
+ original_width, original_height = pil_image.size
197
+ scale_factor = max(target_width / original_width, target_height / original_height)
198
+ resized_width = int(round(original_width * scale_factor))
199
+ resized_height = int(round(original_height * scale_factor))
200
+ resized_image = pil_image.resize((resized_width, resized_height), Image.LANCZOS)
201
+ left = (resized_width - target_width) / 2
202
+ top = (resized_height - target_height) / 2
203
+ right = (resized_width + target_width) / 2
204
+ bottom = (resized_height + target_height) / 2
205
+ cropped_image = resized_image.crop((left, top, right, bottom))
206
+ return np.array(cropped_image)
207
+
208
+
209
+ def resize_without_crop(image, target_width, target_height):
210
+ pil_image = Image.fromarray(image)
211
+ resized_image = pil_image.resize((target_width, target_height), Image.LANCZOS)
212
+ return np.array(resized_image)
213
+
214
+
215
+ @torch.inference_mode()
216
+ def run_rmbg(img, sigma=0.0):
217
+ H, W, C = img.shape
218
+ assert C == 3
219
+ k = (256.0 / float(H * W)) ** 0.5
220
+ feed = resize_without_crop(img, int(64 * round(W * k)), int(64 * round(H * k)))
221
+ feed = numpy2pytorch([feed]).to(device=device, dtype=torch.float32)
222
+ alpha = rmbg(feed)[0][0]
223
+ alpha = torch.nn.functional.interpolate(alpha, size=(H, W), mode="bilinear")
224
+ alpha = alpha.movedim(1, -1)[0]
225
+ alpha = alpha.detach().float().cpu().numpy().clip(0, 1)
226
+ result = 127 + (img.astype(np.float32) - 127 + sigma) * alpha
227
+ return result.clip(0, 255).astype(np.uint8), alpha
228
+
229
+
230
+ @torch.inference_mode()
231
+ def process(input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source):
232
+ bg_source = BGSource(bg_source)
233
+ input_bg = None
234
+
235
+ if bg_source == BGSource.NONE:
236
+ pass
237
+ elif bg_source == BGSource.LEFT:
238
+ gradient = np.linspace(255, 0, image_width)
239
+ image = np.tile(gradient, (image_height, 1))
240
+ input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
241
+ elif bg_source == BGSource.RIGHT:
242
+ gradient = np.linspace(0, 255, image_width)
243
+ image = np.tile(gradient, (image_height, 1))
244
+ input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
245
+ elif bg_source == BGSource.TOP:
246
+ gradient = np.linspace(255, 0, image_height)[:, None]
247
+ image = np.tile(gradient, (1, image_width))
248
+ input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
249
+ elif bg_source == BGSource.BOTTOM:
250
+ gradient = np.linspace(0, 255, image_height)[:, None]
251
+ image = np.tile(gradient, (1, image_width))
252
+ input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
253
+ else:
254
+ raise 'Wrong initial latent!'
255
+
256
+ rng = torch.Generator(device=device).manual_seed(int(seed))
257
+
258
+ fg = resize_and_center_crop(input_fg, image_width, image_height)
259
+
260
+ concat_conds = numpy2pytorch([fg]).to(device=vae.device, dtype=vae.dtype)
261
+ concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor
262
+
263
+ conds, unconds = encode_prompt_pair(positive_prompt=prompt + ', ' + a_prompt, negative_prompt=n_prompt)
264
+
265
+ if input_bg is None:
266
+ latents = t2i_pipe(
267
+ prompt_embeds=conds,
268
+ negative_prompt_embeds=unconds,
269
+ width=image_width,
270
+ height=image_height,
271
+ num_inference_steps=steps,
272
+ num_images_per_prompt=num_samples,
273
+ generator=rng,
274
+ output_type='latent',
275
+ guidance_scale=cfg,
276
+ cross_attention_kwargs={'concat_conds': concat_conds},
277
+ ).images.to(vae.dtype) / vae.config.scaling_factor
278
+ else:
279
+ bg = resize_and_center_crop(input_bg, image_width, image_height)
280
+ bg_latent = numpy2pytorch([bg]).to(device=vae.device, dtype=vae.dtype)
281
+ bg_latent = vae.encode(bg_latent).latent_dist.mode() * vae.config.scaling_factor
282
+ latents = i2i_pipe(
283
+ image=bg_latent,
284
+ strength=lowres_denoise,
285
+ prompt_embeds=conds,
286
+ negative_prompt_embeds=unconds,
287
+ width=image_width,
288
+ height=image_height,
289
+ num_inference_steps=int(round(steps / lowres_denoise)),
290
+ num_images_per_prompt=num_samples,
291
+ generator=rng,
292
+ output_type='latent',
293
+ guidance_scale=cfg,
294
+ cross_attention_kwargs={'concat_conds': concat_conds},
295
+ ).images.to(vae.dtype) / vae.config.scaling_factor
296
+
297
+ pixels = vae.decode(latents).sample
298
+ pixels = pytorch2numpy(pixels)
299
+ pixels = [resize_without_crop(
300
+ image=p,
301
+ target_width=int(round(image_width * highres_scale / 64.0) * 64),
302
+ target_height=int(round(image_height * highres_scale / 64.0) * 64))
303
+ for p in pixels]
304
+
305
+ pixels = numpy2pytorch(pixels).to(device=vae.device, dtype=vae.dtype)
306
+ latents = vae.encode(pixels).latent_dist.mode() * vae.config.scaling_factor
307
+ latents = latents.to(device=unet.device, dtype=unet.dtype)
308
+
309
+ image_height, image_width = latents.shape[2] * 8, latents.shape[3] * 8
310
+
311
+ fg = resize_and_center_crop(input_fg, image_width, image_height)
312
+ concat_conds = numpy2pytorch([fg]).to(device=vae.device, dtype=vae.dtype)
313
+ concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor
314
+
315
+ latents = i2i_pipe(
316
+ image=latents,
317
+ strength=highres_denoise,
318
+ prompt_embeds=conds,
319
+ negative_prompt_embeds=unconds,
320
+ width=image_width,
321
+ height=image_height,
322
+ num_inference_steps=int(round(steps / highres_denoise)),
323
+ num_images_per_prompt=num_samples,
324
+ generator=rng,
325
+ output_type='latent',
326
+ guidance_scale=cfg,
327
+ cross_attention_kwargs={'concat_conds': concat_conds},
328
+ ).images.to(vae.dtype) / vae.config.scaling_factor
329
+
330
+ pixels = vae.decode(latents).sample
331
+
332
+ return pytorch2numpy(pixels)
333
+
334
+
335
+ @torch.inference_mode()
336
+ def process_relight(input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source):
337
+ input_fg, matting = run_rmbg(input_fg)
338
+ results = process(input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source)
339
+ return input_fg, results
340
+
341
+
342
+ quick_prompts = [
343
+ 'sunshine from window',
344
+ 'neon light, city',
345
+ 'sunset over sea',
346
+ 'golden time',
347
+ 'sci-fi RGB glowing, cyberpunk',
348
+ 'natural lighting',
349
+ 'warm atmosphere, at home, bedroom',
350
+ 'magic lit',
351
+ 'evil, gothic, Yharnam',
352
+ 'light and shadow',
353
+ 'shadow from window',
354
+ 'soft studio lighting',
355
+ 'home atmosphere, cozy bedroom illumination',
356
+ 'neon, Wong Kar-wai, warm'
357
+ ]
358
+ quick_prompts = [[x] for x in quick_prompts]
359
+
360
+
361
+ quick_subjects = [
362
+ 'beautiful woman, detailed face',
363
+ 'handsome man, detailed face',
364
+ ]
365
+ quick_subjects = [[x] for x in quick_subjects]
366
+
367
+
368
+ class BGSource(Enum):
369
+ NONE = "None"
370
+ LEFT = "Left Light"
371
+ RIGHT = "Right Light"
372
+ TOP = "Top Light"
373
+ BOTTOM = "Bottom Light"
374
+
375
+
376
+ block = gr.Blocks().queue()
377
+ with block:
378
+ with gr.Row():
379
+ gr.Markdown("## IC-Light (Relighting with Foreground Condition)")
380
+ with gr.Row():
381
+ with gr.Column():
382
+ with gr.Row():
383
+ input_fg = gr.Image(source='upload', type="numpy", label="Image", height=480)
384
+ output_bg = gr.Image(type="numpy", label="Preprocessed Foreground", height=480)
385
+ prompt = gr.Textbox(label="Prompt")
386
+ bg_source = gr.Radio(choices=[e.value for e in BGSource],
387
+ value=BGSource.NONE.value,
388
+ label="Lighting Preference (Initial Latent)", type='value')
389
+ example_quick_subjects = gr.Dataset(samples=quick_subjects, label='Subject Quick List', samples_per_page=1000, components=[prompt])
390
+ example_quick_prompts = gr.Dataset(samples=quick_prompts, label='Lighting Quick List', samples_per_page=1000, components=[prompt])
391
+ relight_button = gr.Button(value="Relight")
392
+
393
+ with gr.Group():
394
+ with gr.Row():
395
+ num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
396
+ seed = gr.Number(label="Seed", value=12345, precision=0)
397
+
398
+ with gr.Row():
399
+ image_width = gr.Slider(label="Image Width", minimum=256, maximum=1024, value=512, step=64)
400
+ image_height = gr.Slider(label="Image Height", minimum=256, maximum=1024, value=640, step=64)
401
+
402
+ with gr.Accordion("Advanced options", open=False):
403
+ steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=25, step=1)
404
+ cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=2, step=0.01)
405
+ lowres_denoise = gr.Slider(label="Lowres Denoise (for initial latent)", minimum=0.1, maximum=1.0, value=0.9, step=0.01)
406
+ highres_scale = gr.Slider(label="Highres Scale", minimum=1.0, maximum=3.0, value=1.5, step=0.01)
407
+ highres_denoise = gr.Slider(label="Highres Denoise", minimum=0.1, maximum=1.0, value=0.5, step=0.01)
408
+ a_prompt = gr.Textbox(label="Added Prompt", value='best quality')
409
+ n_prompt = gr.Textbox(label="Negative Prompt", value='lowres, bad anatomy, bad hands, cropped, worst quality')
410
+ with gr.Column():
411
+ result_gallery = gr.Gallery(height=832, object_fit='contain', label='Outputs')
412
+ with gr.Row():
413
+ dummy_image_for_outputs = gr.Image(visible=False, label='Result')
414
+ gr.Examples(
415
+ fn=lambda *args: ([args[-1]], None),
416
+ examples=db_examples.foreground_conditioned_examples,
417
+ inputs=[
418
+ input_fg, prompt, bg_source, image_width, image_height, seed, dummy_image_for_outputs
419
+ ],
420
+ outputs=[result_gallery, output_bg],
421
+ run_on_click=True, examples_per_page=1024
422
+ )
423
+ ips = [input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source]
424
+ relight_button.click(fn=process_relight, inputs=ips, outputs=[output_bg, result_gallery])
425
+ example_quick_prompts.click(lambda x, y: ', '.join(y.split(', ')[:2] + [x[0]]), inputs=[example_quick_prompts, prompt], outputs=prompt, show_progress=False, queue=False)
426
+ example_quick_subjects.click(lambda x: x[0], inputs=example_quick_subjects, outputs=prompt, show_progress=False, queue=False)
427
 
 
 
428
 
429
+ block.launch(server_name='0.0.0.0')
 
briarmbg.py ADDED
@@ -0,0 +1,462 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # RMBG1.4 (diffusers implementation)
2
+ # Found on huggingface space of several projects
3
+ # Not sure which project is the source of this file
4
+
5
+ import torch
6
+ import torch.nn as nn
7
+ import torch.nn.functional as F
8
+ from huggingface_hub import PyTorchModelHubMixin
9
+
10
+
11
+ class REBNCONV(nn.Module):
12
+ def __init__(self, in_ch=3, out_ch=3, dirate=1, stride=1):
13
+ super(REBNCONV, self).__init__()
14
+
15
+ self.conv_s1 = nn.Conv2d(
16
+ in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate, stride=stride
17
+ )
18
+ self.bn_s1 = nn.BatchNorm2d(out_ch)
19
+ self.relu_s1 = nn.ReLU(inplace=True)
20
+
21
+ def forward(self, x):
22
+ hx = x
23
+ xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
24
+
25
+ return xout
26
+
27
+
28
+ def _upsample_like(src, tar):
29
+ src = F.interpolate(src, size=tar.shape[2:], mode="bilinear")
30
+ return src
31
+
32
+
33
+ ### RSU-7 ###
34
+ class RSU7(nn.Module):
35
+ def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512):
36
+ super(RSU7, self).__init__()
37
+
38
+ self.in_ch = in_ch
39
+ self.mid_ch = mid_ch
40
+ self.out_ch = out_ch
41
+
42
+ self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) ## 1 -> 1/2
43
+
44
+ self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
45
+ self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
46
+
47
+ self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
48
+ self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
49
+
50
+ self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
51
+ self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
52
+
53
+ self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
54
+ self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
55
+
56
+ self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
57
+ self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
58
+
59
+ self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1)
60
+
61
+ self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2)
62
+
63
+ self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
64
+ self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
65
+ self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
66
+ self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
67
+ self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
68
+ self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
69
+
70
+ def forward(self, x):
71
+ b, c, h, w = x.shape
72
+
73
+ hx = x
74
+ hxin = self.rebnconvin(hx)
75
+
76
+ hx1 = self.rebnconv1(hxin)
77
+ hx = self.pool1(hx1)
78
+
79
+ hx2 = self.rebnconv2(hx)
80
+ hx = self.pool2(hx2)
81
+
82
+ hx3 = self.rebnconv3(hx)
83
+ hx = self.pool3(hx3)
84
+
85
+ hx4 = self.rebnconv4(hx)
86
+ hx = self.pool4(hx4)
87
+
88
+ hx5 = self.rebnconv5(hx)
89
+ hx = self.pool5(hx5)
90
+
91
+ hx6 = self.rebnconv6(hx)
92
+
93
+ hx7 = self.rebnconv7(hx6)
94
+
95
+ hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1))
96
+ hx6dup = _upsample_like(hx6d, hx5)
97
+
98
+ hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1))
99
+ hx5dup = _upsample_like(hx5d, hx4)
100
+
101
+ hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
102
+ hx4dup = _upsample_like(hx4d, hx3)
103
+
104
+ hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
105
+ hx3dup = _upsample_like(hx3d, hx2)
106
+
107
+ hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
108
+ hx2dup = _upsample_like(hx2d, hx1)
109
+
110
+ hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
111
+
112
+ return hx1d + hxin
113
+
114
+
115
+ ### RSU-6 ###
116
+ class RSU6(nn.Module):
117
+ def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
118
+ super(RSU6, self).__init__()
119
+
120
+ self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
121
+
122
+ self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
123
+ self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
124
+
125
+ self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
126
+ self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
127
+
128
+ self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
129
+ self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
130
+
131
+ self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
132
+ self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
133
+
134
+ self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
135
+
136
+ self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2)
137
+
138
+ self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
139
+ self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
140
+ self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
141
+ self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
142
+ self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
143
+
144
+ def forward(self, x):
145
+ hx = x
146
+
147
+ hxin = self.rebnconvin(hx)
148
+
149
+ hx1 = self.rebnconv1(hxin)
150
+ hx = self.pool1(hx1)
151
+
152
+ hx2 = self.rebnconv2(hx)
153
+ hx = self.pool2(hx2)
154
+
155
+ hx3 = self.rebnconv3(hx)
156
+ hx = self.pool3(hx3)
157
+
158
+ hx4 = self.rebnconv4(hx)
159
+ hx = self.pool4(hx4)
160
+
161
+ hx5 = self.rebnconv5(hx)
162
+
163
+ hx6 = self.rebnconv6(hx5)
164
+
165
+ hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1))
166
+ hx5dup = _upsample_like(hx5d, hx4)
167
+
168
+ hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
169
+ hx4dup = _upsample_like(hx4d, hx3)
170
+
171
+ hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
172
+ hx3dup = _upsample_like(hx3d, hx2)
173
+
174
+ hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
175
+ hx2dup = _upsample_like(hx2d, hx1)
176
+
177
+ hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
178
+
179
+ return hx1d + hxin
180
+
181
+
182
+ ### RSU-5 ###
183
+ class RSU5(nn.Module):
184
+ def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
185
+ super(RSU5, self).__init__()
186
+
187
+ self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
188
+
189
+ self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
190
+ self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
191
+
192
+ self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
193
+ self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
194
+
195
+ self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
196
+ self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
197
+
198
+ self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
199
+
200
+ self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2)
201
+
202
+ self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
203
+ self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
204
+ self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
205
+ self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
206
+
207
+ def forward(self, x):
208
+ hx = x
209
+
210
+ hxin = self.rebnconvin(hx)
211
+
212
+ hx1 = self.rebnconv1(hxin)
213
+ hx = self.pool1(hx1)
214
+
215
+ hx2 = self.rebnconv2(hx)
216
+ hx = self.pool2(hx2)
217
+
218
+ hx3 = self.rebnconv3(hx)
219
+ hx = self.pool3(hx3)
220
+
221
+ hx4 = self.rebnconv4(hx)
222
+
223
+ hx5 = self.rebnconv5(hx4)
224
+
225
+ hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1))
226
+ hx4dup = _upsample_like(hx4d, hx3)
227
+
228
+ hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
229
+ hx3dup = _upsample_like(hx3d, hx2)
230
+
231
+ hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
232
+ hx2dup = _upsample_like(hx2d, hx1)
233
+
234
+ hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
235
+
236
+ return hx1d + hxin
237
+
238
+
239
+ ### RSU-4 ###
240
+ class RSU4(nn.Module):
241
+ def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
242
+ super(RSU4, self).__init__()
243
+
244
+ self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
245
+
246
+ self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
247
+ self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
248
+
249
+ self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
250
+ self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
251
+
252
+ self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
253
+
254
+ self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2)
255
+
256
+ self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
257
+ self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
258
+ self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
259
+
260
+ def forward(self, x):
261
+ hx = x
262
+
263
+ hxin = self.rebnconvin(hx)
264
+
265
+ hx1 = self.rebnconv1(hxin)
266
+ hx = self.pool1(hx1)
267
+
268
+ hx2 = self.rebnconv2(hx)
269
+ hx = self.pool2(hx2)
270
+
271
+ hx3 = self.rebnconv3(hx)
272
+
273
+ hx4 = self.rebnconv4(hx3)
274
+
275
+ hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
276
+ hx3dup = _upsample_like(hx3d, hx2)
277
+
278
+ hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
279
+ hx2dup = _upsample_like(hx2d, hx1)
280
+
281
+ hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
282
+
283
+ return hx1d + hxin
284
+
285
+
286
+ ### RSU-4F ###
287
+ class RSU4F(nn.Module):
288
+ def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
289
+ super(RSU4F, self).__init__()
290
+
291
+ self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
292
+
293
+ self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
294
+ self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2)
295
+ self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4)
296
+
297
+ self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8)
298
+
299
+ self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4)
300
+ self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2)
301
+ self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
302
+
303
+ def forward(self, x):
304
+ hx = x
305
+
306
+ hxin = self.rebnconvin(hx)
307
+
308
+ hx1 = self.rebnconv1(hxin)
309
+ hx2 = self.rebnconv2(hx1)
310
+ hx3 = self.rebnconv3(hx2)
311
+
312
+ hx4 = self.rebnconv4(hx3)
313
+
314
+ hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
315
+ hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1))
316
+ hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1))
317
+
318
+ return hx1d + hxin
319
+
320
+
321
+ class myrebnconv(nn.Module):
322
+ def __init__(
323
+ self,
324
+ in_ch=3,
325
+ out_ch=1,
326
+ kernel_size=3,
327
+ stride=1,
328
+ padding=1,
329
+ dilation=1,
330
+ groups=1,
331
+ ):
332
+ super(myrebnconv, self).__init__()
333
+
334
+ self.conv = nn.Conv2d(
335
+ in_ch,
336
+ out_ch,
337
+ kernel_size=kernel_size,
338
+ stride=stride,
339
+ padding=padding,
340
+ dilation=dilation,
341
+ groups=groups,
342
+ )
343
+ self.bn = nn.BatchNorm2d(out_ch)
344
+ self.rl = nn.ReLU(inplace=True)
345
+
346
+ def forward(self, x):
347
+ return self.rl(self.bn(self.conv(x)))
348
+
349
+
350
+ class BriaRMBG(nn.Module, PyTorchModelHubMixin):
351
+ def __init__(self, config: dict = {"in_ch": 3, "out_ch": 1}):
352
+ super(BriaRMBG, self).__init__()
353
+ in_ch = config["in_ch"]
354
+ out_ch = config["out_ch"]
355
+ self.conv_in = nn.Conv2d(in_ch, 64, 3, stride=2, padding=1)
356
+ self.pool_in = nn.MaxPool2d(2, stride=2, ceil_mode=True)
357
+
358
+ self.stage1 = RSU7(64, 32, 64)
359
+ self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
360
+
361
+ self.stage2 = RSU6(64, 32, 128)
362
+ self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
363
+
364
+ self.stage3 = RSU5(128, 64, 256)
365
+ self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
366
+
367
+ self.stage4 = RSU4(256, 128, 512)
368
+ self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
369
+
370
+ self.stage5 = RSU4F(512, 256, 512)
371
+ self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
372
+
373
+ self.stage6 = RSU4F(512, 256, 512)
374
+
375
+ # decoder
376
+ self.stage5d = RSU4F(1024, 256, 512)
377
+ self.stage4d = RSU4(1024, 128, 256)
378
+ self.stage3d = RSU5(512, 64, 128)
379
+ self.stage2d = RSU6(256, 32, 64)
380
+ self.stage1d = RSU7(128, 16, 64)
381
+
382
+ self.side1 = nn.Conv2d(64, out_ch, 3, padding=1)
383
+ self.side2 = nn.Conv2d(64, out_ch, 3, padding=1)
384
+ self.side3 = nn.Conv2d(128, out_ch, 3, padding=1)
385
+ self.side4 = nn.Conv2d(256, out_ch, 3, padding=1)
386
+ self.side5 = nn.Conv2d(512, out_ch, 3, padding=1)
387
+ self.side6 = nn.Conv2d(512, out_ch, 3, padding=1)
388
+
389
+ # self.outconv = nn.Conv2d(6*out_ch,out_ch,1)
390
+
391
+ def forward(self, x):
392
+ hx = x
393
+
394
+ hxin = self.conv_in(hx)
395
+ # hx = self.pool_in(hxin)
396
+
397
+ # stage 1
398
+ hx1 = self.stage1(hxin)
399
+ hx = self.pool12(hx1)
400
+
401
+ # stage 2
402
+ hx2 = self.stage2(hx)
403
+ hx = self.pool23(hx2)
404
+
405
+ # stage 3
406
+ hx3 = self.stage3(hx)
407
+ hx = self.pool34(hx3)
408
+
409
+ # stage 4
410
+ hx4 = self.stage4(hx)
411
+ hx = self.pool45(hx4)
412
+
413
+ # stage 5
414
+ hx5 = self.stage5(hx)
415
+ hx = self.pool56(hx5)
416
+
417
+ # stage 6
418
+ hx6 = self.stage6(hx)
419
+ hx6up = _upsample_like(hx6, hx5)
420
+
421
+ # -------------------- decoder --------------------
422
+ hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
423
+ hx5dup = _upsample_like(hx5d, hx4)
424
+
425
+ hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
426
+ hx4dup = _upsample_like(hx4d, hx3)
427
+
428
+ hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
429
+ hx3dup = _upsample_like(hx3d, hx2)
430
+
431
+ hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
432
+ hx2dup = _upsample_like(hx2d, hx1)
433
+
434
+ hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
435
+
436
+ # side output
437
+ d1 = self.side1(hx1d)
438
+ d1 = _upsample_like(d1, x)
439
+
440
+ d2 = self.side2(hx2d)
441
+ d2 = _upsample_like(d2, x)
442
+
443
+ d3 = self.side3(hx3d)
444
+ d3 = _upsample_like(d3, x)
445
+
446
+ d4 = self.side4(hx4d)
447
+ d4 = _upsample_like(d4, x)
448
+
449
+ d5 = self.side5(hx5d)
450
+ d5 = _upsample_like(d5, x)
451
+
452
+ d6 = self.side6(hx6)
453
+ d6 = _upsample_like(d6, x)
454
+
455
+ return [
456
+ F.sigmoid(d1),
457
+ F.sigmoid(d2),
458
+ F.sigmoid(d3),
459
+ F.sigmoid(d4),
460
+ F.sigmoid(d5),
461
+ F.sigmoid(d6),
462
+ ], [hx1d, hx2d, hx3d, hx4d, hx5d, hx6]
db_examples.py ADDED
@@ -0,0 +1,217 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ foreground_conditioned_examples = [
2
+ [
3
+ "imgs/i1.webp",
4
+ "beautiful woman, detailed face, sunshine, outdoor, warm atmosphere",
5
+ "Right Light",
6
+ 512,
7
+ 960,
8
+ 12345,
9
+ "imgs/o1.png",
10
+ ],
11
+ [
12
+ "imgs/i1.webp",
13
+ "beautiful woman, detailed face, sunshine, outdoor, warm atmosphere",
14
+ "Left Light",
15
+ 512,
16
+ 960,
17
+ 50,
18
+ "imgs/o2.png",
19
+ ],
20
+ [
21
+ "imgs/i3.png",
22
+ "beautiful woman, detailed face, neon, Wong Kar-wai, warm",
23
+ "Left Light",
24
+ 512,
25
+ 768,
26
+ 12345,
27
+ "imgs/o3.png",
28
+ ],
29
+ [
30
+ "imgs/i3.png",
31
+ "beautiful woman, detailed face, sunshine from window",
32
+ "Left Light",
33
+ 512,
34
+ 768,
35
+ 12345,
36
+ "imgs/o4.png",
37
+ ],
38
+ [
39
+ "imgs/i5.png",
40
+ "beautiful woman, detailed face, warm atmosphere, at home, bedroom",
41
+ "Left Light",
42
+ 512,
43
+ 768,
44
+ 123,
45
+ "imgs/o5.png",
46
+ ],
47
+ [
48
+ "imgs/i6.jpg",
49
+ "beautiful woman, detailed face, sunshine from window",
50
+ "Right Light",
51
+ 512,
52
+ 768,
53
+ 42,
54
+ "imgs/o6.png",
55
+ ],
56
+ [
57
+ "imgs/i7.jpg",
58
+ "beautiful woman, detailed face, shadow from window",
59
+ "Left Light",
60
+ 512,
61
+ 768,
62
+ 8888,
63
+ "imgs/o7.png",
64
+ ],
65
+ [
66
+ "imgs/i8.webp",
67
+ "beautiful woman, detailed face, sunset over sea",
68
+ "Right Light",
69
+ 512,
70
+ 640,
71
+ 42,
72
+ "imgs/o8.png",
73
+ ],
74
+ [
75
+ "imgs/i9.png",
76
+ "handsome boy, detailed face, neon light, city",
77
+ "Left Light",
78
+ 512,
79
+ 640,
80
+ 12345,
81
+ "imgs/o9.png",
82
+ ],
83
+ [
84
+ "imgs/i10.png",
85
+ "beautiful woman, detailed face, light and shadow",
86
+ "Left Light",
87
+ 512,
88
+ 960,
89
+ 8888,
90
+ "imgs/o10.png",
91
+ ],
92
+ [
93
+ "imgs/i11.png",
94
+ "Buddha, detailed face, sci-fi RGB glowing, cyberpunk",
95
+ "Left Light",
96
+ 512,
97
+ 768,
98
+ 8888,
99
+ "imgs/o11.png",
100
+ ],
101
+ [
102
+ "imgs/i11.png",
103
+ "Buddha, detailed face, natural lighting",
104
+ "Left Light",
105
+ 512,
106
+ 768,
107
+ 12345,
108
+ "imgs/o12.png",
109
+ ],
110
+ [
111
+ "imgs/i13.png",
112
+ "toy, detailed face, shadow from window",
113
+ "Bottom Light",
114
+ 512,
115
+ 704,
116
+ 12345,
117
+ "imgs/o13.png",
118
+ ],
119
+ [
120
+ "imgs/i14.png",
121
+ "toy, detailed face, sunset over sea",
122
+ "Right Light",
123
+ 512,
124
+ 704,
125
+ 100,
126
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