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Majesty Diffusion by @Dango233(@Dango233max) and @apolinario (@multimodalart)

Jupyter Notebook 100.00%

majesty-diffusion's Introduction

Majesty Diffusion 👑

Generate images from text with majesty

Formerly known as Princess Generator

Majesty Diffusion are implementations of text-to-image diffusion models with a royal touch 👸

Access our Majestic Guide (under construction), join our community on Discord or reach out via @multimodalart on Twitter). Also share your settings with us

Current implementations:

Latent Majesty Diffusion
V-Majesty Diffusion

Latent Majesty Diffusion v1.6

Formerly known as Latent Princess Generator

A Dango233 and apolinario (@multimodalart) Colab notebook implementing CompVis' Latent Diffusion. Contribute to our settings library on Hugging Face!

v1.2

Added Dango233 CLIP Guidance
Added Dango233 magical new step and upscaling scheduling
Added Dango233 cuts, augs and attributes scheduling
Added Dango233 mag and clamp settings
Added Dango233 linear ETA scheduling
Added Dango233 negative prompts for Latent Diffusion Guidance
Added Jack000 GLID-3 XL watermark free fine-tuned model
Added dmarx Multi-Modal-Comparators for CLIP and CLIP-like models
Added open_clip gradient checkpointing
Added crowsonkb aesthetic models
Added LAION-AI aesthetic predictor embeddings
Added Dango233 inpainting mode
Added apolinario (@multimodalart) savable settings and setting library (including colab-free-default, dango233-princesses, the-other-zippy and makaitrad shared settings. Share yours with us too with a pull request!

v1.3

- Better Upscaler (learn how to use it on our [Majestic Guide](https://multimodal.art/majesty-diffusion))

v1.4 & 1.5 & 1.6

v1.4

Added Dango233 Customised Dynamic Thresholding
Added open_clip ViT-L/14 LAION-400M trained
Fix CLOOB perceptor from MMC
Removes latent upscaler (was broken), adds RGB upscaler

v1.5

Even better defaults
Better dynamic thresholidng
Improves range scale
Adds var scale and mean scale
Adds the possibility of blurring cuts
Adds experimental compression and punishment settings
Adds PLMS support (experimental, results perceptually weird)

v1.6

Adds LAION ongo (finetuned in artworks) and erlich (finetuned for making logos) models
Adds noising and scaling during the advanced schedulign phases
Adds ViT-L conditioning downstream to the Latent Diffusion unet process
Small tweaks on dynamic thresholding
Fixes linear ETA

V-Majesty Diffusion v1.2

Formerly known as Princess Generator ver. Victoria

A Dango233 and apolinario (@multimodalart) Colab notebook implementing crowsonkb's V-Objective Diffusion, with the following changes:

Added Dango233 parallel multi-model diffusion (e.g.: run cc12m_1 and yfcc_2 at the same time - with or without lerping)
Added Dango233 cuts, augs and attributes scheduling
Added Dango233 mag and clamp settings
Added apolinario (@multimodalart) ETA scheduling
Added nshepperd v-diffusion imagenet512 and danbooru models
Added dmarx Multi-Modal-Comparators
Added crowsonkb AVA and Simulacra bot aesthetic models
Added LAION-AI aesthetic pre-calculated embeddings
Added open_clip gradient checkpointing
Added Dango233 inpainting mode
Added apolinario (@multimodalart) "internal upscaling" (upscales the output with yfcc_2 or openimages)
Added apolinario (@multimodalart) savable settings and setting library (including defaults, disco-diffusion-defaults default settings). Share yours with us too with a pull request!

TODO

Please feel free to help us in any of these tasks!

Figure out better defaults and add more settings to the settings library (contribute with a PR!)
Add all notebooks to a single pipeline where on model can be the output of the other (similar to Centipede Diffusion)
Add all notebooks to the MindsEye UI
Modularise everything
Create a command line version
Add an inpainting UI
Improve performance, both in speed and VRAM consumption
More technical issues will be listed on https://github.com/multimodalart/majesty-diffusion/issues

Acknowledgments

Some functions and methods are from various code masters - including but not limited to advadnoun, crowsonkb, nshepperd, russelldc, Dango233 and many others

majesty-diffusion's People

Contributors

Stargazers

Watchers

majesty-diffusion's Issues

Replace the Multi-Modal-Comparators module with a simple script

Hi I'm trying to run the majesty-diffusion locally, but I have some challenges on the Multi-Modal-Comparators.

The Multi-Modal-Comparators have some issues:

It has some version conflicts. The mmc depend on Pillow-7.2.0, but the minimum version for python 3.10 is Pillow 8.3.2 https://pillow.readthedocs.io/en/stable/installation.html
It's too complicated. Clip models management could be very straightforward. Progrockdiffusion project has a 200 lines scripts to manage models and it works very well. https://github.com/lowfuel/progrockdiffusion/blob/main/model_managers/clip_manager.py

Thanks.

Colab error : Basicsr module not found and then crashes

I was playing since some days with my own instance on Colab, all was working like a charm, but today, it doesn't work anymore.
Fearing I have misstyped something, I went back to the original instance hosted here, but I'm still getting the same error, that is happening when using GFPGAN upscaling:

Sampling images 1/1
Data shape for DDIM sampling is (1, 4, 32, 32), eta 1.3
Running DDIM Sampling with 119 timesteps
DDIM Sampler: 100%
119/119 [02:06<00:00, 1.01it/s]
/content/GFPGAN
Traceback (most recent call last):
  File "inference_gfpgan.py", line 9, in <module>
    from gfpgan import GFPGANer
  File "/content/GFPGAN/gfpgan/__init__.py", line 4, in <module>
    from .models import *
  File "/content/GFPGAN/gfpgan/models/__init__.py", line 10, in <module>
    _model_modules = [importlib.import_module(f'gfpgan.models.{file_name}') for file_name in model_filenames]
  File "/content/GFPGAN/gfpgan/models/__init__.py", line 10, in <listcomp>
    _model_modules = [importlib.import_module(f'gfpgan.models.{file_name}') for file_name in model_filenames]
  File "/usr/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/content/GFPGAN/gfpgan/models/gfpgan_model.py", line 6, in <module>
    from basicsr.losses.losses import r1_penalty
ModuleNotFoundError: No module named 'basicsr.losses.losses'
/content
---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
[<ipython-input-13-6ec39f84cd27>](https://localhost:8080/#) in <module>()
    147       fps = 24
    148       p = Popen(['ffmpeg', '-y', '-f', 'image2pipe', '-vcodec', 'png', '-r', str(fps), '-i', '-', '-vcodec', 'libx264', '-r', str(fps), '-pix_fmt', 'yuv420p', '-crf', '17', '-preset', 'veryslow', 'video.mp4'], stdin=PIPE)
--> 149   do_run()
    150   if generate_video:
    151       p.stdin.close()

1 frames
[<ipython-input-8-0502eee025d5>](https://localhost:8080/#) in fetch(url_or_path)
    124         fd.seek(0)
    125         return fd
--> 126     return open(url_or_path, 'rb')
    127 
    128 

FileNotFoundError: [Errno 2] No such file or directory: 'GFPGAN/results/restored_imgs/temp_1657646799.png'

EOF error at the Load Necessary Functions step

Getting an EOF error at the Load Necessary Functions step, Line 461.

Stack exchange says it's probably a pickle being overwritten but that's as far as my trouble shooting skill can take me.

Tried it in two separate colab instances, one with the Erlich model, the second with finetuned.

Thank you :)

`Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]
Loading model from: /usr/local/lib/python3.7/dist-packages/lpips/weights/v0.1/vgg.pth
---------------------------------------------------------------------------
EOFError                                  Traceback (most recent call last)
[<ipython-input-14-f555e4f938e4>](https://localhost:8080/#) in <module>()
    459 #Alstro's aesthetic model
    460 aesthetic_model_336 = torch.nn.Linear(768,1).cuda()
--> 461 aesthetic_model_336.load_state_dict(torch.load(f"{model_path}/ava_vit_l_14_336_linear.pth"))
    462 
    463 aesthetic_model_224 = torch.nn.Linear(768,1).cuda()

1 frames
[/usr/local/lib/python3.7/dist-packages/torch/serialization.py](https://localhost:8080/#) in _legacy_load(f, map_location, pickle_module, **pickle_load_args)
    775             "functionality.")
    776 
--> 777     magic_number = pickle_module.load(f, **pickle_load_args)
    778     if magic_number != MAGIC_NUMBER:
    779         raise RuntimeError("Invalid magic number; corrupt file?")

EOFError: Ran out of input`

last output is always blank

Hi,
I tried to run the notebook with sample prompts but the last saved output is always blank. Also the quality of generated images is not even better than latent diffusion with jack model. Do you think its a problem with settings or something else?
Thanks

Got this TypeError for V-Majesty Notebook

I tried to do the run with all default settings and when I wanted to get into the diffusion, I got this:

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

[<ipython-input-25-4d917cc6e118>](https://localhost:8080/#) in <module>()
    141                     gc.collect()
    142                     torch.cuda.empty_cache()
--> 143                     do_run()
    144                     #threading.Thread(target=do_run, args=()).start()
    145 

5 frames

[<ipython-input-16-ced52f73bed5>](https://localhost:8080/#) in forward(self, input)
     75         sideY, sideX = input.shape[2:4]
     76         max_size = min(sideX, sideY)
---> 77         min_size = min(sideX, sideY, self.cut_size)
     78         l_size = max(sideX, sideY)
     79         output_shape = [1,3,self.cut_size,self.cut_size]

TypeError: '<' not supported between instances of 'tuple' and 'int'

If there is any fix to this, I'd be happy to be noticed.

Deleting Mask File Path or entering "None" causes error

Easily fixed by copying the same code above it for the init_image:

if(init_mask == '' or init_mask == 'None'): init_mask = None

Memory leak

Running with how_many_batches>1 can lead to "Cuda out of memory" errors after the first image is completed. There is a memory leak somewhere.

Other notebooks solve this problem by putting the batch "for" loop around the do_run() function rather than inside the do_run() function. That way, whatever mystery local variable that causes this problem gets cleaned up.

TypeError: modify_score() argument after ** must be a mapping, not list

btw, opt.ddim_steps is not specified

AI do not understand the word "holding" in prompt "holding a sword"

prompt: A warrior wombat holding a sword and shield in a fighting stance. The wombat stands in front of the Arc de Triomphe on a day shrouded mist with the sun high in the sky. realistic anime illustration.

it should produce image like this

incompatible package versions

Hi, I'm working on a project and want a specific look that I was only able to generate with majesty confusion however it seems that six months later, the Colab doesn't work because not all of the packages were pinned to specific versions.

Could someone provide me a pip list from a working repo so I can reconfigure the Colab to work again?

Model memorizes previous run

Reported by @aiartdue77.

MinDalle support?

I was wondering if there is a way to add the MinDalle model to the list of droptdowns for additional models.

Seeding the system

Hi, great work on this system!

I am looking for a way to make the image generation reproducible.
Other notebooks/scripts have a seed value. I added some code to Latent Majesty Diffusion (v1.3) to seed torch:

seed = 1234567
torch.manual_seed(seed)

I also added this snippet to seed pseudo-random number generators in pytorch, numpy, python.random:

from pytorch_lightning import seed_everything
seed_everything(seed, workers=True)

But the images are still different in each run.

Any idea what else I need to change to make the image generation reproducible? That is, given the same seed, I want the system to produce the same image.

I am uncertain if this part is relevant:

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
xarray-einstats 0.2.2 requires numpy>=1.21, but you have numpy 1.20.3 which is incompatible.
torchtext 0.13.0 requires torch==1.12.0, but you have torch 1.10.2 which is incompatible.
datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.
Successfully installed gfpgan-1.3.2 numpy-1.20.3 realesrgan-0.2.5.0
WARNING: The following packages were previously imported in this runtime:
  [numpy]
You must restart the runtime in order to use newly installed versions.
---------------------------------------------------------------------------

But this should not happen:

OSError                                   Traceback (most recent call last)
<ipython-input-5-d9296e7aa8b5> in <module>()
     25 
     26     sys.path.append('./taming-transformers')
---> 27     from taming.models import vqgan
     28     from subprocess import Popen, PIPE
     29     try:

14 frames
/usr/lib/python3.7/ctypes/__init__.py in __init__(self, name, mode, handle, use_errno, use_last_error)
    362 
    363         if handle is None:
--> 364             self._handle = _dlopen(self._name, mode)
    365         else:
    366             self._handle = handle

OSError: /usr/local/lib/python3.7/dist-packages/torchtext/lib/libtorchtext.so: undefined symbol: _ZN3c1022getCustomClassTypeImplERKSt10type_index

Full output of the Installation cell: Click to expand!

Cloning into 'latent-diffusion'...
remote: Enumerating objects: 346, done.
remote: Counting objects: 100% (51/51), done.
remote: Compressing objects: 100% (35/35), done.
remote: Total 346 (delta 22), reused 37 (delta 16), pack-reused 295
Receiving objects: 100% (346/346), 28.43 MiB | 25.38 MiB/s, done.
Resolving deltas: 100% (135/135), done.
Cloning into 'taming-transformers'...
remote: Enumerating objects: 1335, done.
remote: Total 1335 (delta 0), reused 0 (delta 0), pack-reused 1335
Receiving objects: 100% (1335/1335), 409.77 MiB | 33.39 MiB/s, done.
Resolving deltas: 100% (278/278), done.
Cloning into 'GFPGAN'...
remote: Enumerating objects: 431, done.
remote: Counting objects: 100% (5/5), done.
remote: Compressing objects: 100% (5/5), done.
remote: Total 431 (delta 0), reused 4 (delta 0), pack-reused 426
Receiving objects: 100% (431/431), 5.36 MiB | 29.82 MiB/s, done.
Resolving deltas: 100% (214/214), done.
WARNING: 'git lfs clone' is deprecated and will not be updated
         with new flags from 'git clone'

'git clone' has been updated in upstream Git to have comparable
speeds to 'git lfs clone'.
Cloning into 'latent-majesty-diffusion-settings'...
remote: Enumerating objects: 14, done.
remote: Counting objects: 100% (14/14), done.
remote: Compressing objects: 100% (13/13), done.
remote: Total 14 (delta 4), reused 0 (delta 0), pack-reused 0
Unpacking objects: 100% (14/14), done.
WARNING: 'git lfs clone' is deprecated and will not be updated
         with new flags from 'git clone'

'git clone' has been updated in upstream Git to have comparable
speeds to 'git lfs clone'.
Cloning into 'aesthetic-predictor'...
remote: Enumerating objects: 67, done.
remote: Counting objects: 100% (67/67), done.
remote: Compressing objects: 100% (64/64), done.
remote: Total 67 (delta 21), reused 28 (delta 3), pack-reused 0
Unpacking objects: 100% (67/67), done.
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Obtaining file:///content/taming-transformers
Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from taming-transformers==0.0.1) (1.10.2)
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Installing collected packages: taming-transformers
 Running setup.py develop for taming-transformers
Successfully installed taming-transformers-0.0.1
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting transformers
 Downloading transformers-4.20.1-py3-none-any.whl (4.4 MB)
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Collecting huggingface-hub<1.0,>=0.1.0
 Downloading huggingface_hub-0.8.1-py3-none-any.whl (101 kB)
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Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)
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Collecting tokenizers!=0.11.3,<0.13,>=0.11.1
 Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)
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Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)
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Installing collected packages: tokenizers, huggingface-hub, transformers
Successfully installed huggingface-hub-0.8.1 tokenizers-0.12.1 transformers-4.20.1
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting dotmap
 Downloading dotmap-1.3.30-py3-none-any.whl (11 kB)
Installing collected packages: dotmap
Successfully installed dotmap-1.3.30
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting resize-right
 Downloading resize_right-0.0.2-py3-none-any.whl (8.9 kB)
Installing collected packages: resize-right
Successfully installed resize-right-0.0.2
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting piq
 Downloading piq-0.7.0-py3-none-any.whl (139 kB)
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Requirement already satisfied: torchvision!=0.9.*,>=0.6.1 in /usr/local/lib/python3.7/dist-packages (from piq) (0.11.3)
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Installing collected packages: piq
Successfully installed piq-0.7.0
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting lpips
 Downloading lpips-0.1.4-py3-none-any.whl (53 kB)
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Requirement already satisfied: numpy>=1.14.3 in /usr/local/lib/python3.7/dist-packages (from lpips) (1.21.6)
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Installing collected packages: lpips
Successfully installed lpips-0.1.4
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Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tb-nightly->basicsr) (3.2.0)
Building wheels for collected packages: basicsr
 Building wheel for basicsr (setup.py) ... done
 Created wheel for basicsr: filename=basicsr-1.4.0-py3-none-any.whl size=213769 sha256=cec57b47cf1c18b06ff5f0b8ea6e01a7995a61320330851d539f09fc01a90352
 Stored in directory: /root/.cache/pip/wheels/ab/0f/9e/e62b449971d38bbefbb54fe838167b91341f7e18b34270525f
Successfully built basicsr
Installing collected packages: yapf, tb-nightly, addict, basicsr
Successfully installed addict-2.4.0 basicsr-1.4.0 tb-nightly-2.10.0a20220712 yapf-0.32.0
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting facexlib
 Downloading facexlib-0.2.4-py3-none-any.whl (59 kB)
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Collecting filterpy
 Downloading filterpy-1.4.5.zip (177 kB)
    |████████████████████████████████| 177 kB 29.0 MB/s 
Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from facexlib) (0.51.2)
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Building wheels for collected packages: filterpy
 Building wheel for filterpy (setup.py) ... done
 Created wheel for filterpy: filename=filterpy-1.4.5-py3-none-any.whl size=110474 sha256=208ebc749b8f47447004807d1cd1f609c84f3ccabb646ef0fae220405e4eb7a0
 Stored in directory: /root/.cache/pip/wheels/ce/e0/ee/a2b3c5caab3418c1ccd8c4de573d4cbe13315d7e8b0a55fbc2
Successfully built filterpy
Installing collected packages: filterpy, facexlib
Successfully installed facexlib-0.2.4 filterpy-1.4.5
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting realesrgan
 Downloading realesrgan-0.2.5.0-py3-none-any.whl (25 kB)
Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from realesrgan) (7.1.2)
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Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from realesrgan) (4.64.0)
Collecting gfpgan>=0.2.1
 Downloading gfpgan-1.3.2-py3-none-any.whl (47 kB)
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Collecting numpy
 Downloading numpy-1.20.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.3 MB)
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Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tb-nightly->basicsr>=1.3.3.11->realesrgan) (0.4.8)
Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tb-nightly->basicsr>=1.3.3.11->realesrgan) (3.2.0)
Installing collected packages: numpy, gfpgan, realesrgan
 Attempting uninstall: numpy
   Found existing installation: numpy 1.21.6
   Uninstalling numpy-1.21.6:
     Successfully uninstalled numpy-1.21.6
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
xarray-einstats 0.2.2 requires numpy>=1.21, but you have numpy 1.20.3 which is incompatible.
torchtext 0.13.0 requires torch==1.12.0, but you have torch 1.10.2 which is incompatible.
datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.
Successfully installed gfpgan-1.3.2 numpy-1.20.3 realesrgan-0.2.5.0
WARNING: The following packages were previously imported in this runtime:
 [numpy]
You must restart the runtime in order to use newly installed versions.
---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
[<ipython-input-4-d9296e7aa8b5>](https://localhost:8080/#) in <module>()
    25 
    26     sys.path.append('./taming-transformers')
---> 27     from taming.models import vqgan
    28     from subprocess import Popen, PIPE
    29     try:

14 frames
[/usr/lib/python3.7/ctypes/__init__.py](https://localhost:8080/#) in __init__(self, name, mode, handle, use_errno, use_last_error)
   362 
   363         if handle is None:
--> 364             self._handle = _dlopen(self._name, mode)
   365         else:
   366             self._handle = handle

OSError: /usr/local/lib/python3.7/dist-packages/torchtext/lib/libtorchtext.so: undefined symbol: _ZN3c1022getCustomClassTypeImplERKSt10type_index

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