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nope's Issues

VAE model

Hi,

I am trying to run the script for training.
I looks as if you are using a huggingface model for VAE, using diffusers.AutoencoderKL

But I am not able to access the stable-diffusion-v1-5 model using AutoencoderKL.from_config
("error no config json is present")

I tried changing from autoencoder to StableDiffusionPipeline in diffusers, and using trained weights from runwayml/stable-diffusion-v1-5 , but this class StableDiffusionPipeline do not have an encode method.

Would be glad if you can help fix this issue.

Thanks,
Gokul

test_shapeNet.py missing

Hi can you pls add test_shapeNet.py so that we can test it. Weight provided in other issue is sufficient for running this inference?

env setup fails on Ubuntu 22.04

(base) mona@ada:~/nope$ conda env create -f environment.yml
Retrieving notices: ...working... done
Collecting package metadata (repodata.json): / WARNING conda.models.version:get_matcher(556): Using .* with relational operator is superfluous and deprecated and will be removed in a future version of conda. Your spec was 1.6.0.*, but conda is ignoring the .* and treating it as 1.6.0
WARNING conda.models.version:get_matcher(556): Using .* with relational operator is superfluous and deprecated and will be removed in a future version of conda. Your spec was 1.8.0.*, but conda is ignoring the .* and treating it as 1.8.0
WARNING conda.models.version:get_matcher(556): Using .* with relational operator is superfluous and deprecated and will be removed in a future version of conda. Your spec was 1.7.1.*, but conda is ignoring the .* and treating it as 1.7.1
WARNING conda.models.version:get_matcher(556): Using .* with relational operator is superfluous and deprecated and will be removed in a future version of conda. Your spec was 1.9.0.*, but conda is ignoring the .* and treating it as 1.9.0
done
Solving environment: done


==> WARNING: A newer version of conda exists. <==
  current version: 23.7.4
  latest version: 23.10.0

Please update conda by running

    $ conda update -n base -c defaults conda

Or to minimize the number of packages updated during conda update use

     conda install conda=23.10.0



Downloading and Extracting Packages
                                                                                                                                                             
Preparing transaction: done                                                                                                                                  
Verifying transaction: done                                                                                                                                  
Executing transaction: \ By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreement (EULA): https://docs.nvidia.com/cuda/eula/index.html                                                                                                        
                                                                                                                                                             
done                                                                                                                                                         
Installing pip dependencies: / Ran pip subprocess with arguments:                                                                                            
['/home/mona/anaconda3/envs/nope/bin/python', '-m', 'pip', 'install', '-U', '-r', '/home/mona/nope/condaenv.2tcc2q38.requirements.txt', '--exists-action=b'] 
Pip subprocess output:                                                                                                                                       
Collecting absl-py==1.3.0                                                                                                                                    
  Downloading absl_py-1.3.0-py3-none-any.whl (124 kB)                                                                                                        
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 124.6/124.6 kB 4.4 MB/s eta 0:00:00                                                                            
Collecting accelerate==0.14.0                                                                                                                                
  Downloading accelerate-0.14.0-py3-none-any.whl (175 kB)                                                                                                    
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 176.0/176.0 kB 9.5 MB/s eta 0:00:00                                                                            
Collecting addict==2.4.0                                                                                                                                     
  Using cached addict-2.4.0-py3-none-any.whl (3.8 kB)                                                                                                        
Collecting aiohttp==3.8.3                                                                                                                                    
  Downloading aiohttp-3.8.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)                                                              
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.0/1.0 MB 11.4 MB/s eta 0:00:00                                                                               
Collecting aiosignal==1.3.1                                                                                                                                  
  Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB)                                                                                                      
Collecting alembic==1.8.1                                                                                                                                    
  Downloading alembic-1.8.1-py3-none-any.whl (209 kB)                                                                                                        
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 209.8/209.8 kB 16.8 MB/s eta 0:00:00                                                                           
Collecting antlr4-python3-runtime==4.9.3                                                                                                                     
  Downloading antlr4-python3-runtime-4.9.3.tar.gz (117 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 117.0/117.0 kB 36.1 MB/s eta 0:00:00
  Preparing metadata (setup.py): started
  Preparing metadata (setup.py): finished with status 'done'
Collecting asttokens==2.1.0
  Downloading asttokens-2.1.0-py2.py3-none-any.whl (26 kB)
Collecting astunparse==1.6.3
  Downloading astunparse-1.6.3-py2.py3-none-any.whl (12 kB)
Collecting async-timeout==4.0.2
  Downloading async_timeout-4.0.2-py3-none-any.whl (5.8 kB)
Collecting attrs==22.1.0
  Downloading attrs-22.1.0-py2.py3-none-any.whl (58 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 58.8/58.8 kB 18.9 MB/s eta 0:00:00
Collecting autopage==0.5.1
  Downloading autopage-0.5.1-py3-none-any.whl (29 kB)
Collecting backcall==0.2.0
  Using cached backcall-0.2.0-py2.py3-none-any.whl (11 kB)
Collecting blenderproc==2.5.0
  Downloading blenderproc-2.5.0-py3-none-any.whl (501 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 501.4/501.4 kB 17.7 MB/s eta 0:00:00
Collecting blobfile==2.0.0
  Downloading blobfile-2.0.0-py3-none-any.whl (73 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 73.3/73.3 kB 21.6 MB/s eta 0:00:00
Collecting boto3==1.26.70
  Downloading boto3-1.26.70-py3-none-any.whl (132 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 132.7/132.7 kB 9.2 MB/s eta 0:00:00
Collecting botocore==1.29.70
  Downloading botocore-1.29.70-py3-none-any.whl (10.4 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 10.4/10.4 MB 34.9 MB/s eta 0:00:00
Collecting cachetools==5.2.0
  Downloading cachetools-5.2.0-py3-none-any.whl (9.3 kB)
Collecting cfgv==3.3.1
  Downloading cfgv-3.3.1-py2.py3-none-any.whl (7.3 kB)
Collecting click==8.1.3
  Downloading click-8.1.3-py3-none-any.whl (96 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 96.6/96.6 kB 29.4 MB/s eta 0:00:00
Collecting cliff==4.1.0
  Downloading cliff-4.1.0-py3-none-any.whl (81 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 81.0/81.0 kB 31.5 MB/s eta 0:00:00

Pip subprocess error:
ERROR: Could not find a version that satisfies the requirement clip==1.0 (from versions: 0.0.1, 0.1.0, 0.2.0)
ERROR: No matching distribution found for clip==1.0

failed

CondaEnvException: Pip failed

(base) mona@ada:~$ uname -a
Linux ada 6.2.0-36-generic #37~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Mon Oct  9 15:34:04 UTC 2 x86_64 x86_64 x86_64 GNU/Linux
(base) mona@ada:~$ lsb_release -a
LSB Version:	core-11.1.0ubuntu4-noarch:security-11.1.0ubuntu4-noarch
Distributor ID:	Ubuntu
Description:	Ubuntu 22.04.3 LTS
Release:	22.04
Codename:	jammy

(base) mona@ada:~$ conda --version
conda 23.7.4

The test script

Hi,
Would you be able to share the test_script if its implemented?
I was following the README for inference, but the actual test script itself is not present in the repository.

More references as input

Hi, really interesting work!
Can I give more reference images for every category?
Best regards,
Paolo.

Evalution on real-world objects or datasets

Hi,

Thanks for your interesting work!

I noticed that the evaluation is primarily conducted on synthetic objects. Thus I'd like to ask whether you have tried the proposed method on real-world objects or datasets, for example, on some BOP benchmark datasets.

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