Translate texts in manga/images.
中文说明 | Change Log
Join us on discord https://discord.gg/Ak8APNy4vb
Some manga/images will never be translated, therefore this project is born.
- Image/Manga Translator
Please note that the samples may not always be updated, they may not represent the current main branch version.
| Original | Translated |
|---|---|
(Source @09ra_19ra) |
(Mask) |
(Source @VERTIGRIS_ART) |
--detector ctd
(Mask)
|
(Source @hiduki_yayoi) |
--translator none
(Mask)
|
(Source @rikak) |
(Mask) |
Official Demo (by zyddnys): https://touhou.ai/imgtrans/
Browser Userscript (by QiroNT): https://greasyfork.org/scripts/437569
- Note this may not work sometimes due to stupid google gcp kept restarting my instance. In that case you can wait for me to restart the service, which may take up to 24 hrs.
- Note this online demo is using the current main branch version.
Successor to MMDOCR-HighPerformance.
This is a hobby project, you are welcome to contribute!
Currently this only a simple demo, many imperfections exist, we need your support to make this project better!
Primarily designed for translating Japanese text, but also supports Chinese, English and Korean.
Supports inpainting, text rendering and colorization.
# First, you need to have Python(>=3.8) installed on your system
# The latest version often does not work with some pytorch libraries yet
$ python --version
Python 3.10.6
# Clone this repo
$ git clone https://github.com/zyddnys/manga-image-translator.git
# Create venv
$ python -m venv venv
# Activate venv
$ source venv/bin/activate
# For --use-gpu option go to https://pytorch.org/ and follow
# pytorch installation instructions. Add `--upgrade --force-reinstall`
# to the pip command to overwrite the currently installed pytorch version.
# Install the dependencies
$ pip install -r requirements.txtThe models will be downloaded into ./models at runtime.
Before you start the pip install, first install Microsoft C++ Build Tools (Download, Instructions) as some pip dependencies will not compile without it. (See #114).
To use cuda on windows install the correct pytorch version as instructed on https://pytorch.org/.
Requirements:
- Docker (version 19.03+ required for CUDA / GPU acceleration)
- Docker Compose (Optional if you want to use files in the
demo/docfolder) - Nvidia Container Runtime (Optional if you want to use CUDA)
This project has docker support under zyddnys/manga-image-translator:main image.
This docker image contains all required dependencies / models for the project.
It should be noted that this image is fairly large (~ 15GB).
The web server can be hosted using (For CPU)
docker run -p 5003:5003 -v result:/app/result --ipc=host --rm zyddnys/manga-image-translator:main -l ENG --manga2eng -v --mode web --host=0.0.0.0 --port=5003or
docker-compose -f demo/doc/docker-compose-web-with-cpu.yml updepending on which you prefer. The web server should start on port 5003
and images should become in the /result folder.
To use docker with the CLI (I.e in batch mode)
docker run -v <targetFolder>:/app/<targetFolder> -v <targetFolder>-translated:/app/<targetFolder>-translated --ipc=host --rm zyddnys/manga-image-translator:main --mode=batch -i=/app/<targetFolder> <cli flags>Note: In the event you need to reference files on your host machine
you will need to mount the associated files as volumes into the /app folder inside the container.
Paths for the CLI will need to be the internal docker path /app/... instead of the paths on your host machine
Some translation services require API keys to function to set these pass them as env vars into the docker container. For example:
docker run --env="DEEPL_AUTH_KEY=xxx" --ipc=host --rm zyddnys/manga-image-translator:main <cli flags>To use with a supported GPU please first read the initial
Dockersection. There are some special dependencies you will need to use
To run the container with the following flags set:
docker run ... --gpus=all ... zyddnys/manga-image-translator:main ... --use-gpuOr (For the web server + GPU)
docker-compose -f demo/doc/docker-compose-web-with-gpu.yml upTo build the docker image locally you can run (You will require make on your machine)
make build-imageThen to test the built image run
make run-web-server# use `--use-gpu` for speedup if you have a compatible NVIDIA GPU.
# use `--target-lang <language_code>` to specify a target language.
# use `--inpainter=none` to disable inpainting.
# use `--translator=none` if you only want to use inpainting (blank bubbles)
# replace <path> with the path to the image folder or file.
$ python -m manga_translator -v --translator=google -l ENG -i <path>
# results can be found under `<path_to_image_folder>-translated`.# saves singular image into /result folder for demonstration purposes
# use `--mode demo` to enable demo translation.
# replace <path> with the path to the image file.
$ python -m manga_translator --mode demo -v --translator=google -l ENG -i <path>
# result can be found in `result/`.# use `--mode web` to start a web server.
$ python -m manga_translator -v --mode web --use-gpu
# the demo will be serving on http://127.0.0.1:5003# use `--mode web` to start a web server.
$ python -m manga_translator -v --mode api --use-gpu
# the demo will be serving on http://127.0.0.1:5003GUI implementation: BallonsTranslator
Detector:
- ENG: ??
- JPN: ??
- CHS: ??
- KOR: ??
- Using
--detector ctdcan increase the amount of text lines detected
OCR:
- ENG: ??
- JPN: ??
- CHS: ??
- KOR: 48px
Translator:
- JPN -> ENG: Sugoi
- CHS -> ENG: ??
- CHS -> JPN: ??
- JPN -> CHS: ??
- ENG -> JPN: ??
- ENG -> CHS: ??
Inpainter: ??
Colorizer: mc2
- Small resolutions can sometimes trip up the detector, which is not so good at picking up irregular text sizes. To
circumvent this you can use an upscaler by specifying
--upscale-ratio 2or any other value - If the text being rendered is too small to read specify
--font-size-minimum 30for instance or use the--manga2engrenderer that will try to adapt to detected textbubbles - Specify a font with
--font-path fonts/anime_ace_3.ttffor example
-h, --help show this help message and exit
-m, --mode {demo,batch,web,web_client,ws,api}
Run demo in single image demo mode (demo), batch
translation mode (batch), web service mode (web)
-i, --input INPUT [INPUT ...] Path to an image file if using demo mode, or path to an
image folder if using batch mode
-o, --dest DEST Path to the destination folder for translated images in
batch mode
-l, --target-lang {CHS,CHT,CSY,NLD,ENG,FRA,DEU,HUN,ITA,JPN,KOR,PLK,PTB,ROM,RUS,ESP,TRK,UKR,VIN,ARA,CNR,SRP,HRV,THA,IND,FIL}
Destination language
-v, --verbose Print debug info and save intermediate images in result
folder
-f, --format {png,webp,jpg,xcf,psd,pdf} Output format of the translation.
--attempts ATTEMPTS Retry attempts on encountered error. -1 means infinite
times.
--ignore-errors Skip image on encountered error.
--overwrite Overwrite already translated images in batch mode.
--skip-no-text Skip image without text (Will not be saved).
--model-dir MODEL_DIR Model directory (by default ./models in project root)
--use-gpu Turn on/off gpu
--use-gpu-limited Turn on/off gpu (excluding offline translator)
--detector {default,ctd,craft,none} Text detector used for creating a text mask from an
image, DO NOT use craft for manga, it's not designed
for it
--ocr {32px,48px,48px_ctc,mocr} Optical character recognition (OCR) model to use
--use-mocr-merge Use bbox merge when Manga OCR inference.
--inpainter {default,lama_large,lama_mpe,sd,none,original}
Inpainting model to use
--upscaler {waifu2x,esrgan,4xultrasharp} Upscaler to use. --upscale-ratio has to be set for it
to take effect
--upscale-ratio UPSCALE_RATIO Image upscale ratio applied before detection. Can
improve text detection.
--colorizer {mc2} Colorization model to use.
--translator {google,youdao,baidu,deepl,papago,caiyun,gpt3,gpt3.5,gpt4,none,original,offline,nllb,nllb_big,sugoi,jparacrawl,jparacrawl_big,m2m100,m2m100_big,sakura}
Language translator to use
--translator-chain TRANSLATOR_CHAIN Output of one translator goes in another. Example:
--translator-chain "google:JPN;sugoi:ENG".
--selective-translation SELECTIVE_TRANSLATION
Select a translator based on detected language in
image. Note the first translation service acts as
default if the language isn't defined. Example:
--translator-chain "google:JPN;sugoi:ENG".
--revert-upscaling Downscales the previously upscaled image after
translation back to original size (Use with --upscale-
ratio).
--detection-size DETECTION_SIZE Size of image used for detection
--det-rotate Rotate the image for detection. Might improve
detection.
--det-auto-rotate Rotate the image for detection to prefer vertical
textlines. Might improve detection.
--det-invert Invert the image colors for detection. Might improve
detection.
--det-gamma-correct Applies gamma correction for detection. Might improve
detection.
--unclip-ratio UNCLIP_RATIO How much to extend text skeleton to form bounding box
--box-threshold BOX_THRESHOLD Threshold for bbox generation
--text-threshold TEXT_THRESHOLD Threshold for text detection
--min-text-length MIN_TEXT_LENGTH Minimum text length of a text region
--no-text-lang-skip Dont skip text that is seemingly already in the target
language.
--inpainting-size INPAINTING_SIZE Size of image used for inpainting (too large will
result in OOM)
--inpainting-precision {fp32,fp16,bf16} Inpainting precision for lama, use bf16 while you can.
--colorization-size COLORIZATION_SIZE Size of image used for colorization. Set to -1 to use
full image size
--denoise-sigma DENOISE_SIGMA Used by colorizer and affects color strength, range
from 0 to 255 (default 30). -1 turns it off.
--mask-dilation-offset MASK_DILATION_OFFSET By how much to extend the text mask to remove left-over
text pixels of the original image.
--font-size FONT_SIZE Use fixed font size for rendering
--font-size-offset FONT_SIZE_OFFSET Offset font size by a given amount, positive number
increase font size and vice versa
--font-size-minimum FONT_SIZE_MINIMUM Minimum output font size. Default is
image_sides_sum/200
--font-color FONT_COLOR Overwrite the text fg/bg color detected by the OCR
model. Use hex string without the "#" such as FFFFFF
for a white foreground or FFFFFF:000000 to also have a
black background around the text.
--line-spacing LINE_SPACING Line spacing is font_size * this value. Default is 0.01
for horizontal text and 0.2 for vertical.
--force-horizontal Force text to be rendered horizontally
--force-vertical Force text to be rendered vertically
--align-left Align rendered text left
--align-center Align rendered text centered
--align-right Align rendered text right
--uppercase Change text to uppercase
--lowercase Change text to lowercase
--no-hyphenation If renderer should be splitting up words using a hyphen
character (-)
--manga2eng Render english text translated from manga with some
additional typesetting. Ignores some other argument
options
--gpt-config GPT_CONFIG Path to GPT config file, more info in README
--use-mtpe Turn on/off machine translation post editing (MTPE) on
the command line (works only on linux right now)
--save-text Save extracted text and translations into a text file.
--save-text-file SAVE_TEXT_FILE Like --save-text but with a specified file path.
--filter-text FILTER_TEXT Filter regions by their text with a regex. Example
usage: --text-filter ".*badtext.*"
--pre-dict FILe_PATH Path to the pre-translation dictionary file. One entry per line,
Comments can be added with `#` and `//`.
usage: //Example
dog cat #Example
abc def
abc
--post-dict FILE_PATH Path to the post-translation dictionary file. Same as above.
--skip-lang Skip translation if source image is one of the provide languages,
use comma to separate multiple languages. Example: JPN,ENG
--prep-manual Prepare for manual typesetting by outputting blank,
inpainted images, plus copies of the original for
reference
--font-path FONT_PATH Path to font file
--gimp-font GIMP_FONT Font family to use for gimp rendering.
--host HOST Used by web module to decide which host to attach to
--port PORT Used by web module to decide which port to attach to
--nonce NONCE Used by web module as secret for securing internal web
server communication
--ws-url WS_URL Server URL for WebSocket mode
--save-quality SAVE_QUALITY Quality of saved JPEG image, range from 0 to 100 with
100 being best
--ignore-bubble IGNORE_BUBBLE The threshold for ignoring text in non bubble areas,
with valid values ranging from 1 to 50, does not ignore
others. Recommendation 5 to 10. If it is too low,
normal bubble areas may be ignored, and if it is too
large, non bubble areas may be considered normal
bubbles
Used by the --target-lang or -l argument.
CHS: Chinese (Simplified)
CHT: Chinese (Traditional)
CSY: Czech
NLD: Dutch
ENG: English
FRA: French
DEU: German
HUN: Hungarian
ITA: Italian
JPN: Japanese
KOR: Korean
PLK: Polish
PTB: Portuguese (Brazil)
ROM: Romanian
RUS: Russian
ESP: Spanish
TRK: Turkish
UKR: Ukrainian
VIN: Vietnames
ARA: Arabic
SRP: Serbian
HRV: Croatian
THA: Thai
IND: Indonesian
FIL: Filipino (Tagalog)| Name | API Key | Offline | Note |
|---|---|---|---|
| Disabled temporarily | |||
| youdao | ✔️ | Requires YOUDAO_APP_KEY and YOUDAO_SECRET_KEY |
|
| baidu | ✔️ | Requires BAIDU_APP_ID and BAIDU_SECRET_KEY |
|
| deepl | ✔️ | Requires DEEPL_AUTH_KEY |
|
| caiyun | ✔️ | Requires CAIYUN_TOKEN |
|
| gpt3 | ✔️ | Implements text-davinci-003. Requires OPENAI_API_KEY |
|
| gpt3.5 | ✔️ | Implements gpt-3.5-turbo. Requires OPENAI_API_KEY |
|
| gpt4 | ✔️ | Implements gpt-4. Requires OPENAI_API_KEY |
|
| papago | |||
| sakura | Requires SAKURA_API_BASE |
||
| offline | ✔️ | Chooses most suitable offline translator for language | |
| sugoi | ✔️ | Sugoi V4.0 Models | |
| m2m100 | ✔️ | Supports every language | |
| m2m100_big | ✔️ | ||
| none | ✔️ | Translate to empty texts | |
| original | ✔️ | Keep original texts |
- API Key: Whether the translator requires an API key to be set as environment variable. For this you can create a .env file in the project root directory containing your api keys like so:
OPENAI_API_KEY=sk-xxxxxxx...
DEEPL_AUTH_KEY=xxxxxxxx...-
Offline: Whether the translator can be used offline.
-
Sugoi is created by mingshiba, please support him in https://www.patreon.com/mingshiba
Used by the --gpt-config argument.
# The prompt being feed into GPT before the text to translate.
# Use {to_lang} to indicate where the target language name should be inserted.
# Note: ChatGPT models don't use this prompt.
prompt_template: >
Please help me to translate the following text from a manga to {to_lang}
(if it's already in {to_lang} or looks like gibberish you have to output it as it is instead):\n
# What sampling temperature to use, between 0 and 2.
# Higher values like 0.8 will make the output more random,
# while lower values like 0.2 will make it more focused and deterministic.
temperature: 0.5
# An alternative to sampling with temperature, called nucleus sampling,
# where the model considers the results of the tokens with top_p probability mass.
# So 0.1 means only the tokens comprising the top 10% probability mass are considered.
top_p: 1
# The prompt being feed into ChatGPT before the text to translate.
# Use {to_lang} to indicate where the target language name should be inserted.
# Tokens used in this example: 57+
chat_system_template: >
You are a professional translation engine,
please translate the story into a colloquial,
elegant and fluent content,
without referencing machine translations.
You must only translate the story, never interpret it.
If there is any issue in the text, output it as is.
Translate to {to_lang}.
# Samples being feed into ChatGPT to show an example conversation.
# In a [prompt, response] format, keyed by the target language name.
#
# Generally, samples should include some examples of translation preferences, and ideally
# some names of characters it's likely to encounter.
#
# If you'd like to disable this feature, just set this to an empty list.
chat_sample:
Simplified Chinese: # Tokens used in this example: 88 + 84
- <|1|>恥ずかしい… 目立ちたくない… 私が消えたい…
<|2|>きみ… 大丈夫⁉
<|3|>なんだこいつ 空気読めて ないのか…？
- <|1|>好尴尬…我不想引人注目…我想消失…
<|2|>你…没事吧⁉
<|3|>这家伙怎么看不懂气氛的…？
# Overwrite configs for a specific model.
# For now the list is: gpt3, gpt35, gpt4
gpt35:
temperature: 0.3When setting output format to {xcf, psd, pdf} Gimp will be used to generate the file.
On Windows this assumes Gimp 2.x to be installed to C:\Users\<Username>\AppData\Local\Programs\Gimp 2.
The resulting .xcf file contains the original image as the lowest layer and it has the inpainting as a separate layer.
The translated textboxes have their own layers with the original text as the layer name for easy access.
Limitations:
- Gimp will turn text layers to regular images when saving
.psdfiles. - Rotated text isn't handled well in Gimp. When editing a rotated textbox it'll also show a popup that it was modified by an outside program.
- Font family is controlled separately, with the
--gimp-fontargument.
API V2
# use `--mode api` to start a web server.
$ python -m manga_translator -v --mode api --use-gpu
# the api will be serving on http://127.0.0.1:5003Api is accepting json(post) and multipart.
Api endpoints are /colorize_translate, /inpaint_translate, /translate, /get_text.
Valid arguments for the api are:
// These are taken from args.py. For more info see README.md
detector: String
ocr: String
inpainter: String
upscaler: String
translator: String
target_language: String
upscale_ratio: Integer
translator_chain: String
selective_translation: String
attempts: Integer
detection_size: Integer // 1024 => 'S', 1536 => 'M', 2048 => 'L', 2560 => 'X'
text_threshold: Float
box_threshold: Float
unclip_ratio: Float
inpainting_size: Integer
det_rotate: Bool
det_auto_rotate: Bool
det_invert: Bool
det_gamma_correct: Bool
min_text_length: Integer
colorization_size: Integer
denoise_sigma: Integer
mask_dilation_offset: Integer
ignore_bubble: Integer
gpt_config: String
filter_text: String
overlay_type: String
// These are api specific args
direction: String // {'auto', 'h', 'v'}
base64Images: String //Image in base64 format
image: Multipart // image upload from multipart
url: String // an url string
Manual translation replaces machine translation with human translators. Basic manual translation demo can be found at http://127.0.0.1:5003/manual when using web mode.
API
Two modes of translation service are provided by the demo: synchronous mode and asynchronous mode.
In synchronous mode your HTTP POST request will finish once the translation task is finished.
In asynchronous mode your HTTP POST request will respond with a task_id immediately, you can use this task_id to
poll for translation task state.
- POST a form request with form data
file:<content-of-image>to http://127.0.0.1:5003/run - Wait for response
- Use the resultant
task_idto find translation result inresult/directory, e.g. using Nginx to exposeresult/
- POST a form request with form data
file:<content-of-image>to http://127.0.0.1:5003/submit - Acquire translation
task_id - Poll for translation task state by posting JSON
{"taskid": <task-id>}to http://127.0.0.1:5003/task-state - Translation is finished when the resultant state is either
finished,errororerror-lang - Find translation result in
result/directory, e.g. using Nginx to exposeresult/
POST a form request with form data file:<content-of-image> to http://127.0.0.1:5003/manual-translate
and wait for response.
You will obtain a JSON response like this:
{
"task_id": "12c779c9431f954971cae720eb104499",
"status": "pending",
"trans_result": [
{
"s": "☆上司来ちゃった……",
"t": ""
}
]
}Fill in translated texts:
{
"task_id": "12c779c9431f954971cae720eb104499",
"status": "pending",
"trans_result": [
{
"s": "☆上司来ちゃった……",
"t": "☆Boss is here..."
}
]
}Post translated JSON to http://127.0.0.1:5003/post-manual-result and wait for response.
Then you can find the translation result in result/ directory, e.g. using Nginx to expose result/.
A list of what needs to be done next, you're welcome to contribute.
- Use diffusion model based inpainting to achieve near perfect result, but this could be much slower.
IMPORTANT!!!HELP NEEDED!!! The current text rendering engine is barely usable, we need your help to improve text rendering!- Text rendering area is determined by detected text lines, not speech bubbles.
This works for images without speech bubbles, but making it impossible to decide where to put translated English text. I have no idea how to solve this. - Ryota et al. proposed using multimodal machine translation, maybe we can add ViT features for building custom NMT models.
- Make this project works for video(rewrite code in C++ and use GPU/other hardware NN accelerator).
Used for detecting hard subtitles in videos, generating ass file and remove them completely. Mask refinement based using non deep learning algorithms, I am currently testing out CRF based algorithm.Angled text region merge is not currently supported- Create pip repository
GPU server is not cheap, please consider to donate to us.
-
Ko-fi: https://ko-fi.com/voilelabs
-
Patreon: https://www.patreon.com/voilelabs
-
爱发电: https://afdian.net/@voilelabs
