bcmi / libcom Goto Github PK

Image composition toolbox: everything you want to know about image composition or object insertion

License: Apache License 2.0

Python 98.10% Shell 0.02% C++ 0.90% C 0.14% Objective-C 0.05% Cuda 0.78%

foreground-object-search image-blending image-composition image-harmonization inharmonious-region-localization object-placement painterly-image-harmonization shadow-generation cross-domain-image-composition generative-image-composition

libcom's Introduction

libcom: everything about image composition

Introduction

libcom (the library of image composition) is an image composition toolbox. The goal of image composition is inserting one foreground into a background image to get a realistic composite image, by addressing the inconsistencies (appearance, geometry, and semantic inconsistency) between foreground and background. Generally speaking, image composition could be used to combine the visual elements from different images.

libcom covers a diversity of related tasks in the field of image composition, including image blending, standard/painterly image harmonization, shadow generation, object placement, generative composition, quality evaluation, etc. For each task, we integrate one or two selected methods considering both efficiency and effectiveness. The selected methods will be continuously updated upon the emergence of better methods.

The ultimate goal of this library is solving all the problems related to image composition with simple import libcom.

Main Functions

get_composite_image generates composite images using naive copy-and-paste followed by image blending.
OPAScoreModel evaluates the rationality of foreground object placement in a composite image.
FOPAHeatMapModel can predict the rationality scores for all locations/scales given a background-foreground pair, and output the composite image with optimal location/scale.
color_transfer adjusts the foreground color to match the background using traditional color transfer method.
ImageHarmonizationModel adjusts the foreground illumination to be compatible the background given photorealistic background and photorealistic foreground.
PainterlyHarmonizationModel adjusts the foreground style to be compatible with the background given artistic background and photorealistic foreground.
HarmonyScoreModel evaluates the harmony level between foreground and background in a composite image.
InharmoniousLocalizationModel localizes the inharmonious region in a synthetic image.
FOSScoreModel evaluates the compatibility between foreground and background in a composite image in terms of geometry and semantics.
ControlComModel is a generative image composition model, which unifies image blending and image harmonization in one diffusion model.
ShadowGenerationModel generates plausible shadow for the inserted object in a composite image.

For the detailed method descriptions, code examples, visualization results, and performance comments, please refer to our [documents].

Requirements

The main branch is built on the Linux system with Python 3.8 and PyTorch>=1.10.1. For other dependencies, please refer to [conda_env] and [runtime_dependencies].

Get Started

Please refer to [Installation] for installation instructions and [documents] for user guidance.

Contributors

Institution: Brain-like Computing and Machine Intelligence (BCMI) Lab.
Project Initiator & Team Manager: Li Niu.
Architect & Lead Developer: Bo Zhang.
Documentation Manager: Jiacheng Sui.
Module Developers: Jiacheng Sui, Binjie Gao, Lingxiao Lu, Xinhao Tao, Junyan Cao, Junqi You.

License

This project is released under the Apache 2.0 license.

Bibtex

If you use our toolbox, please cite our survey paper using the following BibTeX [arxiv]:

@article{niu2021making,
  title={Making images real again: A comprehensive survey on deep image composition},
  author={Niu, Li and Cong, Wenyan and Liu, Liu and Hong, Yan and Zhang, Bo and Liang, Jing and Zhang, Liqing},
  journal={arXiv preprint arXiv:2106.14490},
  year={2021}
}

libcom's People

Contributors

Stargazers

Watchers

Forkers

sunpro108 zhanglqxyz cian0 dzp62442 yimilirui udonda rfan-debug stupidcucumber wzds2015 beyourself312 dain5832

libcom's Issues

Couldn't get it installed on Mac

I couldn't get it installed on Mac M2.
Any chance you can make a HuggingFace Space so that we can use it?

ControlCom Model other two tasks 'viewsynthesis', 'composition'

Greate Work! But I notice that the ControlCom has the other two tasks can be performed which is named as 'viewsynthesis', 'composition'. But they can't be performed in this libcom project even if I have changed the indicator map as (1,1) or (1,0).

How could i do image harmonization like demo_image_composition?

Hi , i want to use image harmonization by source in https://bcmi.sjtu.edu.cn/home/niuli/demo_image_composition/; Could you give me some advice? what files should i see? Thank you very much .

Ask about trilinear on Windows

我在windows系统上的trilinear_cpp目录下使用完sh setup.sh命令，在pycharm中运行代码仍有缺少trilinear库的报错，请问是windows系统无法使用该库的原因吗

阴影生成跑不通，出现TypeError: unhashable type: 'dict'的错误

处理后的图像，细节部分处理不行，如何改善？

cpu device support

I'm on mac and getting error during harmonization process: Only GPU are supported

Are you planning to support cpu as a device type?

创建env 失败是什么原因？

conda env create -f libcom.yaml 环境创建失败

Ask about usage of this project

Could I ask whether Windows system can use this project as a tool for image composition?

pip install libcom error

Collecting libcom
  Using cached libcom-0.0.1.post9.tar.gz (354 kB)
  Preparing metadata (setup.py) ... done
Building wheels for collected packages: libcom
  error: subprocess-exited-with-error
  
  × python setup.py bdist_wheel did not run successfully.
  │ exit code: 1
  ╰─> See above for output.
  
  note: This error originates from a subprocess, and is likely not a problem with pip.
  Building wheel for libcom (setup.py) ... error
  ERROR: Failed building wheel for libcom
  Running setup.py clean for libcom
Failed to build libcom
ERROR: Could not build wheels for libcom, which is required to install pyproject.toml-based projects

最后一个test_painterly_image...这个程序runtimeError，在哪里更改部分参数呢

c1xx: fatal error C1083: 无法打开源文件: “libcom/image_harmonization/source/trilinear_cpp/src/trilinear.cpp”

win11:
python:3.10.11

creating build\temp.win-amd64-cpython-310\Release
creating build\temp.win-amd64-cpython-310\Release\libcom
creating build\temp.win-amd64-cpython-310\Release\libcom\image_harmonization
creating build\temp.win-amd64-cpython-310\Release\libcom\image_harmonization\source
creating build\temp.win-amd64-cpython-310\Release\libcom\image_harmonization\source\trilinear_cpp
creating build\temp.win-amd64-cpython-310\Release\libcom\image_harmonization\source\trilinear_cpp\src
"C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Tools\MSVC\14.29.30133\bin\HostX86\x64\cl.exe" /c /nologo /O2 /W3 /GL /DNDEBUG /MD -Ilibcom/image_harmonization/source/trilinear_cpp/src -ID:\Python310\lib\site-packages\torch\include -ID:\Python310\lib\site-packages\torch\include\torch\csrc\api\include -ID:\Python310\lib\site-packages\torch\include\TH -ID:\Python310\lib\site-packages\torch\include\THC -ID:\Python310\include -ID:\Python310\Include "-IC:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Tools\MSVC\14.29.30133\include" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.19041.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.19041.0\shared" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.19041.0\um" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.19041.0\winrt" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.19041.0\cppwinrt" /EHsc /Tplibcom/image_harmonization/source/trilinear_cpp/src/trilinear.cpp /Fobuild\temp.win-amd64-cpython-310\Release\libcom/image_harmonization/source/trilinear_cpp/src/trilinear.obj /MD /wd4819 /wd4251 /wd4244 /wd4267 /wd4275 /wd4018 /wd4190 /wd4624 /wd4067 /wd4068 /EHsc -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=trilinear -D_GLIBCXX_USE_CXX11_ABI=0 /std:c++17
trilinear.cpp
c1xx: fatal error C1083: 无法打开源文件: “libcom/image_harmonization/source/trilinear_cpp/src/trilinear.cpp”: No such file or directory
error: command 'C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Tools\MSVC\14.29.30133\bin\HostX86\x64\cl.exe' failed with exit code 2

在管理cmd中执行如下命令：pip install libcom，报上面的错误

如何训练自己的图片

请问，如何训练自己的图片，进行前景图的提取，以及到后面的图像融合还有色彩协调呢？

ModuleNotFoundError error for trilinear package

On importing ImageHarmonizationModel, I get the ModuleNotFoundError error for trilinear package. Also I am not able to pip install the trilinear package. It says the following:
pip install trilinear
ERROR: Could not find a version that satisfies the requirement trilinear
ERROR: No matching distribution found for trilinear

from libcom import ImageHarmonizationModel
---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[10], line 1
----> 1 from libcom import ImageHarmonizationModel

File ~/anoubhav/image_editing/libcom/libcom/__init__.py:6
      4 from .harmony_score import *
      5 from .inharmonious_region_localization import *
----> 6 from .image_harmonization import *
      7 from .painterly_image_harmonization import *
      8 from .fopa_heat_map import *

File ~/anoubhav/image_editing/libcom/libcom/image_harmonization/__init__.py:2
      1 # change to your lib name
----> 2 from .image_harmonization import ImageHarmonizationModel
      4 __all__ = ['ImageHarmonizationModel']

File ~/anoubhav/image_editing/libcom/libcom/image_harmonization/image_harmonization.py:8
      6 import os
      7 import torchvision.transforms as transforms
----> 8 from .source.pct_net import *
      9 from .source.cdt_net import *
     11 cur_dir   = os.path.dirname(os.path.abspath(__file__))

File ~/anoubhav/image_editing/libcom/libcom/image_harmonization/source/pct_net.py:6
      3 import torch.nn.functional as F
      4 from functools import partial
----> 6 from .functions import PCT, ViT_Harmonizer
      9 class PCTNet(nn.Module):
     11     def __init__(
     12         self,
     13         backbone_type='ViT', 
   (...)
     16         clamp=True, color_space = 'RGB', use_attn = False
     17     ):

File ~/anoubhav/image_editing/libcom/libcom/image_harmonization/source/functions.py:11
      9 from functools import partial
     10 import os
---> 11 import trilinear
     16 class Bottleneck(torch.nn.Module):
     17     expansion = 1 

ModuleNotFoundError: No module named 'trilinear'

CudaCheckError when applying CDTNet for image harmonization

Hi,

Firstly, thank you for your great work. I'm trying different model types for image harmonization. Successfully tested PCTNet, but encountered the following error when trying to adopt CDTNet:

(Libcom) root@LUCAS-DEV-a17a0b:~/lky/TSR/libcom/demo# python compose.py 
cudaCheckError() failed : no kernel image is available for execution on the device

I built the environment on Linux with python = 3.8.5 and torch = 1.10.1, also checked that cuda is available.

(Libcom) root@LUCAS-DEV-a17a0b:~/lky/TSR/libcom/demo# pip list | grep torch
open-clip-torch           2.7.0
pytorch-lightning         1.9.0
torch                     1.10.1
torchaudio                0.10.1
torchmetrics              1.3.0.post0
torchvision               0.11.2

(Libcom) root@LUCAS-DEV-a17a0b:~/lky/TSR/libcom/demo# nvidia-smi
Thu Feb 29 15:36:10 2024       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.129.06   Driver Version: 470.129.06   CUDA Version: 11.4     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA A100-SXM...  On   | 00000000:65:01.0 Off |                    0 |
| N/A   57C    P0   410W / 400W |  18859MiB / 81251MiB |     78%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
|   1  NVIDIA A100-SXM...  On   | 00000000:67:01.0 Off |                    0 |
| N/A   46C    P0    88W / 400W |  27981MiB / 81251MiB |     53%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+

是否适用于小目标前景图的合成？

如题，对于像素小于30*30的小目标是否具有较好的效果呢

Failed to produce desired output

Hi!
I tried to use image harmonization, but it seems like the model produces undesired output.
Could you tell me how can I improve the result?

Here's the code I executed.
`# image harmonization

PCTNet = ImageHarmonizationModel(device=3, model_type='CDTNet') # CDTNet
comp_img1 = os.getcwd() + '/libcom/tests/source/composite/Comp1.png'
comp_mask1 = os.getcwd() + '/libcom/tests/source/composite_mask/Comp_mask1.png'
PCT_result1 = PCTNet(comp_img1, comp_mask1)
cv2.imwrite(os.getcwd()+'/libcom/temp.png', PCT_result1)`

And this is the result I got.

error

请问这个怎么解决呢？

training

how can we finetune/train the models ?

Getting out of memory

I am following the documentation of Libcom, and I have tested the example code. My RAM is running out of memory. I am using Linux and have 16GB of RAM with a 10GB swap area. All of my RAM is being consumed along with the swap. Additionally, I have a 16GB GPU, and I am running my model on the GPU using the .to("cuda") command in PyTorch. However, my RAM usage continues to increase. I want to know what the minimum requirements are to run the models. Do I have insufficient resources, or am I doing something wrong? I am strictly following the documentation.

I have completed the installation as instructed in the documents and ran the example of "ControlComModel", but it didn't work.
https://libcom.readthedocs.io/en/latest/api.html

'Segmentation fault (core dumped)' in image_harmonization

Hi Author,

I ran your code "python test_image_harmonization.py", it always triggered a bug as below:

begin testing image_harmonization...
Segmentation fault (core dumped)

I found out there was something wrong with the 'CDTNet.pth'. No matter downloaded from the code automatically or from the modelscope.cn, the model just didn't work.

Btw, my Env:
OS - Ubuntu 20.04
GPU - Nvidia 4090
cuda - 11.3.r11.3
python - 3.8.5

Please help or fix the bug.

pip install problem

      copying libcom/controllable_composition/source/ControlCom/configs/controlcom.yaml -> build/lib.linux-x86_64-cpython-311/libcom/controllable_composition/source/ControlCom/configs
      copying libcom/fos_score/source/config/config_rfosd.yaml -> build/lib.linux-x86_64-cpython-311/libcom/fos_score/source/config
      copying libcom/fos_score/source/config/config.sfosd.yaml -> build/lib.linux-x86_64-cpython-311/libcom/fos_score/source/config
      copying libcom/shadow_generation/source/cldm_v15.yaml -> build/lib.linux-x86_64-cpython-311/libcom/shadow_generation/source
      copying libcom/painterly_image_harmonization/source/PHDiffusion/stable_diffusion.yaml -> build/lib.linux-x86_64-cpython-311/libcom/painterly_image_harmonization/source/PHDiffusion
      running build_ext
      building 'trilinear' extension
      creating /tmp/pip-install-jbn64orc/libcom_468d5c874bb849d9a5be66d430575b81/build/temp.linux-x86_64-cpython-311
      creating /tmp/pip-install-jbn64orc/libcom_468d5c874bb849d9a5be66d430575b81/build/temp.linux-x86_64-cpython-311/libcom
      creating /tmp/pip-install-jbn64orc/libcom_468d5c874bb849d9a5be66d430575b81/build/temp.linux-x86_64-cpython-311/libcom/image_harmonization
      creating /tmp/pip-install-jbn64orc/libcom_468d5c874bb849d9a5be66d430575b81/build/temp.linux-x86_64-cpython-311/libcom/image_harmonization/source
      creating /tmp/pip-install-jbn64orc/libcom_468d5c874bb849d9a5be66d430575b81/build/temp.linux-x86_64-cpython-311/libcom/image_harmonization/source/trilinear_cpp
      creating /tmp/pip-install-jbn64orc/libcom_468d5c874bb849d9a5be66d430575b81/build/temp.linux-x86_64-cpython-311/libcom/image_harmonization/source/trilinear_cpp/src
      Emitting ninja build file /tmp/pip-install-jbn64orc/libcom_468d5c874bb849d9a5be66d430575b81/build/temp.linux-x86_64-cpython-311/build.ninja...
      Compiling objects...
      Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
      ninja: error: '/tmp/pip-install-jbn64orc/libcom_468d5c874bb849d9a5be66d430575b81/libcom/image_harmonization/source/trilinear_cpp/src/trilinear.cpp', needed by '/tmp/pip-install-jbn64orc/libcom_468d5c874bb849d9a5be66d430575b81/build/temp.linux-x86_64-cpython-311/libcom/image_harmonization/source/trilinear_cpp/src/trilinear.o', missing and no known rule to make it
      Traceback (most recent call last):
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/torch/utils/cpp_extension.py", line 2100, in _run_ninja_build
          subprocess.run(
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/subprocess.py", line 571, in run
          raise CalledProcessError(retcode, process.args,
      subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.
      
      The above exception was the direct cause of the following exception:
      
      Traceback (most recent call last):
        File "<string>", line 2, in <module>
        File "<pip-setuptools-caller>", line 34, in <module>
        File "/tmp/pip-install-jbn64orc/libcom_468d5c874bb849d9a5be66d430575b81/setup.py", line 109, in <module>
          setup(
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/__init__.py", line 103, in setup
          return distutils.core.setup(**attrs)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/_distutils/core.py", line 185, in setup
          return run_commands(dist)
                 ^^^^^^^^^^^^^^^^^^
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/_distutils/core.py", line 201, in run_commands
          dist.run_commands()
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/_distutils/dist.py", line 969, in run_commands
          self.run_command(cmd)
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/dist.py", line 989, in run_command
          super().run_command(command)
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/_distutils/dist.py", line 988, in run_command
          cmd_obj.run()
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/wheel/bdist_wheel.py", line 364, in run
          self.run_command("build")
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/_distutils/cmd.py", line 318, in run_command
          self.distribution.run_command(command)
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/dist.py", line 989, in run_command
          super().run_command(command)
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/_distutils/dist.py", line 988, in run_command
          cmd_obj.run()
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/_distutils/command/build.py", line 131, in run
          self.run_command(cmd_name)
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/_distutils/cmd.py", line 318, in run_command
          self.distribution.run_command(command)
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/dist.py", line 989, in run_command
          super().run_command(command)
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/_distutils/dist.py", line 988, in run_command
          cmd_obj.run()
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/command/build_ext.py", line 88, in run
          _build_ext.run(self)
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/_distutils/command/build_ext.py", line 345, in run
          self.build_extensions()
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/torch/utils/cpp_extension.py", line 873, in build_extensions
          build_ext.build_extensions(self)
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/_distutils/command/build_ext.py", line 467, in build_extensions
          self._build_extensions_serial()
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/_distutils/command/build_ext.py", line 493, in _build_extensions_serial
          self.build_extension(ext)
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/command/build_ext.py", line 249, in build_extension
          _build_ext.build_extension(self, ext)
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/setuptools/_distutils/command/build_ext.py", line 548, in build_extension
          objects = self.compiler.compile(
                    ^^^^^^^^^^^^^^^^^^^^^^
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/torch/utils/cpp_extension.py", line 686, in unix_wrap_ninja_compile
          _write_ninja_file_and_compile_objects(
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/torch/utils/cpp_extension.py", line 1774, in _write_ninja_file_and_compile_objects
          _run_ninja_build(
        File "/home/hangyi/anaconda3/envs/inpaint/lib/python3.11/site-packages/torch/utils/cpp_extension.py", line 2116, in _run_ninja_build
          raise RuntimeError(message) from e
      RuntimeError: Error compiling objects for extension
      [end of output]
  
  note: This error originates from a subprocess, and is likely not a problem with pip.
  ERROR: Failed building wheel for libcom
  Running setup.py clean for libcom
Failed to build libcom
ERROR: Could not build wheels for libcom, which is required to install pyproject.toml-based projects

when I use python 3.11 to install libcom (pip install libcom), the error is like above,
because I have some packages have to use python > 3.11, there are some conflicts with libcom,
I think other people have similar problems, and I have check the libcom.yaml,

since I mainly use the ShadowGenerationModel, so is it okay to update the python version from 3.8 to 3.11, or which packages conflict with python 3.11, thanks a lot.

Plan to release other two tasks 'viewsynthesis', 'composition' for ControlCom Model

Hi, is there a plan or estimated timeline to stablize and then release other two tasks 'view synthesis' and 'composition' for ControlCom model? Thanks!

modelscope.hub.errors.NotExistError

Traceback (most recent call last):
File "tests/test_fopa_heat_map.py", line 19, in
net = FOPAHeatMapModel(device=0)
File "/home/largedata/qianlei/parking_slot_detection/libcom/libcom/fopa_heat_map/fopa_heat_map.py", line 68, in init
download_pretrained_model(sopa_weight)
File "/home/largedata/qianlei/parking_slot_detection/libcom/libcom/utils/model_download.py", line 18, in download_pretrained_model
download_file_from_network(model_name, save_dir)
File "/home/largedata/qianlei/parking_slot_detection/libcom/libcom/utils/model_download.py", line 50, in download_file_from_network
file_path = model_file_download(model_id=ms_repo,
File "/home/qianlei/miniconda3/envs/Libcom/lib/python3.8/site-packages/modelscope/hub/file_download.py", line 136, in model_file_download
raise NotExistError('The file path: %s not exist in: %s' %
modelscope.hub.errors.NotExistError: The file path: SOPA.pth.tar not exist in: bcmizb/Libcom_pretrained_models

Hello, failed to download the model. I tried to open the download link, but it couldn't be opened.

Pip Installation Error: opencv_python==4.1.2.30: No Matching Distribution Found

when running: !pip install -r libcom/requirements/runtime.txt

Collecting albumentations==1.3.0 (from -r libcom/requirements/runtime.txt (line 1))
Using cached albumentations-1.3.0-py3-none-any.whl.metadata (34 kB)
Collecting einops==0.3.0 (from -r libcom/requirements/runtime.txt (line 2))
Using cached einops-0.3.0-py2.py3-none-any.whl.metadata (10 kB)
Collecting huggingface_hub==0.13.4 (from -r libcom/requirements/runtime.txt (line 3))
Using cached huggingface_hub-0.13.4-py3-none-any.whl.metadata (7.5 kB)
Collecting imageio==2.9.0 (from -r libcom/requirements/runtime.txt (line 4))
Using cached imageio-2.9.0-py3-none-any.whl.metadata (2.6 kB)
Collecting mmdet==3.2.0 (from -r libcom/requirements/runtime.txt (line 5))
Using cached mmdet-3.2.0-py3-none-any.whl.metadata (32 kB)
Collecting mmpose==1.2.0 (from -r libcom/requirements/runtime.txt (line 6))
Using cached mmpose-1.2.0-py2.py3-none-any.whl.metadata (29 kB)
Collecting modelscope==1.9.3 (from -r libcom/requirements/runtime.txt (line 7))
Using cached modelscope-1.9.3-py3-none-any.whl.metadata (33 kB)
Collecting omegaconf==2.3.0 (from -r libcom/requirements/runtime.txt (line 8))
Using cached omegaconf-2.3.0-py3-none-any.whl.metadata (3.9 kB)
ERROR: Ignored the following yanked versions: 3.4.11.39
ERROR: Could not find a version that satisfies the requirement opencv_python==4.1.2.30 (from versions: 3.4.0.14, 3.4.10.37, 3.4.11.41, 3.4.11.43, 3.4.11.45, 3.4.13.47, 3.4.15.55, 3.4.16.57, 3.4.16.59, 3.4.17.61, 3.4.17.63, 3.4.18.65, 4.3.0.38, 4.4.0.40, 4.4.0.42, 4.4.0.44, 4.4.0.46, 4.5.1.48, 4.5.3.56, 4.5.4.58, 4.5.4.60, 4.5.5.62, 4.5.5.64, 4.6.0.66, 4.7.0.68, 4.7.0.72, 4.8.0.74, 4.8.0.76, 4.8.1.78, 4.9.0.80)
ERROR: No matching distribution found for opencv_python==4.1.2.30

Training for specific use cases

Hi, thank you for the great work. I want to create shadows for objects on a white background. I tried using a pre-trained model, but the results weren't great. Can I train a model specifically for this use case? If yes - what is your recommendations?
Thanks.

您好，controllable_composition生成的图像分辨率是512*512，看起来比较模糊，请问有办法生成分辨率更大或更清晰的图片吗？

Pretrained model AutoDownload error

Multiple automatic downloads are often done with broken symlinks, forcing me to manually fix them for all the tests. Secondly, shared pre-trained models like Vit are often broken again and again when the Zip file function is envoked.

bcmi / libcom Goto Github PK

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