Bring mangas to life!
License: MIT License
Just found a mind-blowing AI upscaler - https://huggingface.co/spaces/gokaygokay/AuraSR-v2.
It has even a pytorch version - https://github.com/lucidrains/gigagan-pytorch
Examples.
https://senkuro.com/manga/re-monster/chapters/119429828913939988/pages/4
Original image:
Colorized image without upscaling:
Upscaled with RealESRGAN_x4plus_anime_6B:
Upscaled with AuraSR-v2:
I have a lot of downloaded comic collections saved as zip, is this project available for local comics?
and，need CUDA GPU？
I'm currently getting issues trying to run the Kaggle api i think it may be to do with the first line and kaggle says::
fatal: unable to access 'https://github.com/BinitDOX/Manga-Colorizer.git/': Could not resolve host: github.com add Code add Markdown
and the other is:::
FileNotFoundError Traceback (most recent call last)
Cell In[44], line 2
1 import os
----> 2 os.chdir("/kaggle/working/Manga-Colorizer/Backend")
FileNotFoundError: [Errno 2] No such file or directory: '/kaggle/working/Manga-Colorizer/Backend'
I am not too knowledgeable into this type of stuff so I do not know how to fix it so any help or ideas would be great
There are images that are perceived by MC as colored, but in appearance they are black and white.
E.g.
https://mangadex.org/chapter/4927a00e-8748-4435-bc8c-0d71694759e0/5
When I use the minimum value of the "Color Tolerance" parameter of 250, at which such images begin to be processed by MC, there are color images that also begin to be processed.
https://mangadex.org/chapter/4927a00e-8748-4435-bc8c-0d71694759e0/21
At the same time, if I use inference.py from https://github.com/xiaogdgenuine/Manga-Colorization-FJ to colorize the same images, but downloaded locally, such thing doesn't occur.
Right away I want to thank you for this wonderful software that allows to colorize manga on the fly.
I've known about the source app for a while now - https://github.com/qweasdd/manga-colorization-v2/, and I'm familiar with its
limitation where processing images wider than 576px results in incorrect coloring.
Is it possible to colorize the image at a size most suitable for the program, i.e. x=576px, and only after this, scale it to a larger size?
Let me explain with an example.
https://mangadex.org/chapter/d72a3eb8-2b20-4c39-a46f-d282eb05e64f/3
Original image (1125 x 1600 Pixels)
How does the image look after processing if the program processed it at dimensions of x = 576px, as originally intended (576 x 820 Pixels)?
This is what the image looks like when processed at sizes exceeding 576px in width (960 x 1367 pixels).
when I run the kaggle notebook it says
RuntimeError Traceback (most recent call last)
Cell In[16], line 10
7 import ssl
8 import threading
---> 10 from colorizator import MangaColorizator
11 from inference import colorize_images as batch_colorize_images
File /kaggle/working/Manga-Colorizer/Backend/colorizator.py:1
----> 1 import torch
2 from torchvision.transforms import ToTensor
3 import numpy as np
File /opt/conda/lib/python3.10/site-packages/torch/init.py:229
227 if USE_GLOBAL_DEPS:
228 _load_global_deps()
--> 229 from torch._C import * # noqa: F403
231 # Appease the type checker; ordinarily this binding is inserted by the
232 # torch._C module initialization code in C
233 if TYPE_CHECKING:
RuntimeError: THPDtypeType.tp_dict == nullptr INTERNAL ASSERT FAILED at "/usr/local/src/pytorch/torch/csrc/Dtype.cpp":135, please report a bug to PyTorch.
any idea how to fix it please?
I installed MC-revamp through the following steps:
python -m venv venv.\venv\Scripts\activatepip install -r requirements.txtapp-steam.pyBut it still uses system Python, not that in the setuped virtual environment.
Although, all other projects works in their own venv.
Sometimes it would be nice to easily show the original image. Maybe the coloring made some text hard to read or you are just curious to see how it looked before coloring.
This could be done in a number of ways. The first few I can think of are:
I want to be sure the regular reading experience is not cluttered with any new visible buttons in the page. Even if they only show up on hover, they will often be flickering into view. I was thinking of a right-click menu item to make this happen, but maybe nobody would ever find it and in some browsers that menu is already crowded. Maybe something in the settings popup could enable showing originals.
It will take a little thought about how to implement since we currently discard the original image data and src when it gets colored.
There is also a lot of variety in how pages handle images. Some natively keep only a few img elements and create and destroy them as you read, others have one for every image in the chapter but clear the image data of ones that have scrolled by to save memory. Saving all original image data will increase by double (or more) the memory a page takes up while reading, so we may not want to enable it by default.
A related feature would be allowing the user to force coloring of an image that was not originally colored because there is already some color in it. This may end up needing its own separate issue for discussion and implementation, but it may help to think about both as we figure out what to do.
it keeps saying ''Error!" on https://chapmanganelo.com/ and i don't know server url
SSL Certificate outdated
New instructions for installing the backend on your own computer (self-hosting):
Clone the repository or download as .zip and extract.
Download the generator weights from https://drive.google.com/file/d/1qmxUEKADkEM4iYLp1fpPLLKnfZ6tcF-t/view?usp=sharing and move it into the Backend/networks folder.
In the Backend folder, open a command prompt, and run:
pip install -r requirements.txt
See the instructions in the comments in Backend/requirements.txt to install PyTorch, then continue below.
Run the following two commands in the Backend folder to generate your own self-signed key (needed for using https). You can safely just press Enter to accept the default value for all questions:
openssl req -nodes -newkey rsa:2048 -keyout server.key -out server.csr
openssl x509 -req -days 730 -in server.csr -signkey server.key -out server.crt
Now you have a choice to make between the original batch backend and the new streaming backend.
The original backend works with Manganelo and colors a chapter as a batch. It is used by Frontend-Chrome and Frontend-Firefox. If you are going to use this and you do not have a GPU, edit app.py and set self.gpu = False (~Line 75).
Launch the original backend with the command: python app.py
The new backend works with more sites and streams one image at a time between the front and back ends. It is used by Frontend-Chrome-AnySite, Frontend-Firefox-AnySite, and Frontend-Safari. Launch the streaming backend with the command: python app-stream.py
Whichever backend you started should run on localhost (https://127.0.0.1:5000/) and Private IP (https://x.x.x.x:5000/)
You will need to enter the Private IP into whichever frontend you use. You should be able to use a frontend on the same computer or on any device on the same local network.
After setting this up for the first time, you will only need to do the last step of opening a command prompt in the Backend folder and using the python command to start the backend each time you want to use it. Make sure your frontend gets the right Private IP each time! Many home routers will give your computer a slightly different private IP address each time it restarts.
When I try to run the command "python app.py" it gives me this:
Sometimes the error mentioned in the title occurs, after which the server stops processing images and keeps giving this error.
If the server is manually restarted, it starts working correctly, continuing to process images.
Is it possible to make it restart automatically when such an error occurs?
127.0.0.1 - - [29/Jul/2024 13:14:13] "POST /colorize-image-data HTTP/1.1" 200 -
127.0.0.1 - - [29/Jul/2024 13:14:55] "OPTIONS /colorize-image-data HTTP/1.1" 200 -
[+] [D2FZ7T3G] Requested image: 684f4f37-e95d-44e2-88c1-730889db4114, Width: 1125, Height: 1600
[+] [D2FZ7T3G] Colorize: True, Upscale: True(x4), Denoise: True
[*] [D2FZ7T3G] Denoising image...
[!] [D2FZ7T3G] Error: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
127.0.0.1 - - [29/Jul/2024 13:14:56] "POST /colorize-image-data HTTP/1.1" 200 -
[+] [4NF2I4X9] Requested image: c2dce404-1793-4c99-8dd4-e1cb2adb02cd, Width: 1125, Height: 1600
[+] [4NF2I4X9] Colorize: True, Upscale: True(x4), Denoise: True
[*] [4NF2I4X9] Denoising image...
[!] [4NF2I4X9] Error: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
127.0.0.1 - - [29/Jul/2024 13:14:56] "POST /colorize-image-data HTTP/1.1" 200 -
(venv) h:\Manga-Colorizer-revamp>app-stream.py
H:\Manga-Colorizer-revamp\colorizator.py:22: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
state_dict = torch.load(config.colorizer_path, map_location=self.device)
H:\Manga-Colorizer-revamp\denoising\denoiser.py:42: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
state_dict = torch.load(weights_path, map_location=torch.device('cpu'))
H:\Manga-Colorizer-revamp\upscalator.py:19: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
model = torch.load(config.upscaler_path, map_location=self.device)
C:\Python312\Lib\site-packages\torch\serialization.py:1189: SourceChangeWarning: source code of class 'torch.nn.modules.conv.Conv2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Python312\Lib\site-packages\torch\serialization.py:1189: SourceChangeWarning: source code of class 'torch.nn.modules.container.Sequential' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Python312\Lib\site-packages\torch\serialization.py:1189: SourceChangeWarning: source code of class 'torch.nn.modules.activation.LeakyReLU' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
* Serving Flask app 'app-stream'
* Debug mode: off
WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
* Running on all addresses (0.0.0.0)
* Running on https://127.0.0.1:5000
* Running on https://192.168.0.3:5000
Press CTRL+C to quit
127.0.0.1 - - [29/Jul/2024 13:15:53] "OPTIONS /colorize-image-data HTTP/1.1" 200 -
[+] [7YLY6313] Requested image: 4c1ed87e-1587-43cf-8e83-45794973117c, Width: 1013, Height: 1440
[+] [7YLY6313] Colorize: True, Upscale: True(x4), Denoise: True
[*] [7YLY6313] Denoising image...
H:\Manga-Colorizer-revamp\denoising\functions.py:40: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\torch\csrc\tensor\python_tensor.cpp:80.)
downsampledfeatures = torch.cuda.FloatTensor(N, Cout, Hout, Wout).fill_(0)
[+] [7YLY6313] Denoised image [1440, 1013, 4]->[1200, 844, 3] in 0.52 seconds.
[*] [7YLY6313] Colorizing image...
[+] [7YLY6313] Colorized image [1200, 844, 3]->[819, 576, 3] in 0.37 seconds.
[*] [7YLY6313] Upscaling image...
[+] [7YLY6313] Upscaled image (x4) [819, 576, 3]->[3276, 2304, 3] in 0.54 seconds.
127.0.0.1 - - [29/Jul/2024 13:15:57] "POST /colorize-image-data HTTP/1.1" 200 -
[+] [CY1F6KS6] Requested image: 5556783d-16ab-4d7f-b9eb-dcc15427a8ee, Width: 1125, Height: 1600
[+] [CY1F6KS6] Colorize: True, Upscale: True(x4), Denoise: True
[*] [CY1F6KS6] Denoising image...
[+] [CY1F6KS6] Denoised image [1600, 1125, 4]->[1200, 843, 3] in 0.23 seconds.
[*] [CY1F6KS6] Colorizing image...
[+] [CY1F6KS6] Colorized image [1200, 843, 3]->[820, 576, 3] in 0.29 seconds.
[*] [CY1F6KS6] Upscaling image...
[+] [CY1F6KS6] Upscaled image (x4) [820, 576, 3]->[3280, 2304, 3] in 0.55 seconds.
127.0.0.1 - - [29/Jul/2024 13:16:01] "POST /colorize-image-data HTTP/1.1" 200 -
127.0.0.1 - - [29/Jul/2024 13:16:20] "OPTIONS /colorize-image-data HTTP/1.1" 200 -
[+] [3RV2N4KH] Requested image: f5bde9bf-315d-45ff-8ecd-f069b24be781, Width: 1125, Height: 1600
[+] [3RV2N4KH] Colorize: True, Upscale: True(x4), Denoise: True
[*] [3RV2N4KH] Denoising image...
[+] [3RV2N4KH] Denoised image [1600, 1125, 4]->[1200, 843, 3] in 0.39 seconds.
[*] [3RV2N4KH] Colorizing image...
[+] [3RV2N4KH] Colorized image [1200, 843, 3]->[820, 576, 3] in 0.19 seconds.
[*] [3RV2N4KH] Upscaling image...
[+] [3RV2N4KH] Upscaled image (x4) [820, 576, 3]->[3280, 2304, 3] in 0.59 seconds.
127.0.0.1 - - [29/Jul/2024 13:16:23] "POST /colorize-image-data HTTP/1.1" 200 -
Anyone to contact you, I need help setting this up as I'm having major issues.
Discord: travis#6256
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