| Name | waifu2x-ncnn-py JSON |
| Version |
2.0.0
JSON |
| download |
| home_page | |
| Summary | |
| upload_time | 2023-12-30 10:38:12 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| license | BSD-3-Clause |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# waifu2x-ncnn-py
Python Binding for waifu2x-ncnn-vulkan with PyBind11
[](https://badge.fury.io/py/waifu2x-ncnn-py?123456)
[](https://github.com/Tohrusky/waifu2x-ncnn-py/actions/workflows/test_pip.yml)
[](https://github.com/Tohrusky/waifu2x-ncnn-py/actions/workflows/Release.yml)
Image Super-Resolution for Anime-style art using Deep Convolutional Neural Networks. And it supports photo. This wrapper provides an easy-to-use interface for running the pre-trained Waifu2x model.
### Current building status matrix
| System | Status | CPU (32bit) | CPU (64bit) | GPU (32bit) | GPU (64bit) |
| :-----------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------: | :----------------: | :---------: | :----------------: |
| Linux (Clang) | [](https://github.com/Tohrusky/waifu2x-ncnn-py/actions/workflows/CI-Linux-x64-Clang.yml) | — | :white_check_mark: | — | :white_check_mark: |
| Linux (GCC) | [](https://github.com/Tohrusky/waifu2x-ncnn-py/actions/workflows/CI-Linux-x64-GCC.yml) | — | :white_check_mark: | — | :white_check_mark: |
| Windows | [](https://github.com/Tohrusky/waifu2x-ncnn-py/actions/workflows/CI-Windows-x64-MSVC.yml) | — | :white_check_mark: | — | :white_check_mark: |
| MacOS | [](https://github.com/Tohrusky/waifu2x-ncnn-py/actions/workflows/CI-MacOS-Universal-Clang.yml) | — | :white_check_mark: | — | :white_check_mark: |
| MacOS (ARM) | [](https://github.com/Tohrusky/waifu2x-ncnn-py/actions/workflows/CI-MacOS-Universal-Clang.yml) | — | :white_check_mark: | — | :white_check_mark: |
# Usage
To use this package, simply install it via pip:
```sh
pip install waifu2x-ncnn-py
```
For Linux user:
```sh
apt install -y libomp5 libvulkan-dev
```
Then, import the Waifu2x class from the package:
```python
from src.waifu2x_ncnn_py import Waifu2x
```
To initialize the model:
```python
waifu2x = Waifu2x(gpuid: int = 0, tta_mode: bool = False, num_threads: int = 1, noise: int = 0, scale: int = 2, tilesize: int = 0, model: str = "models-cunet")
# model can be "models-cunet", "models-upconv_7_anime_style_art_rgb" and "models-upconv_7_photo"
# or an absolute path to the models' directory
```
Here, gpuid specifies the GPU device to use (-1 means use CPU), tta_mode enables test-time augmentation, num_threads sets the number of threads for processing, noise specifies the level of noise to apply to the image (-1 to 3), scale is the scaling factor for super-resolution (1 to 4), tilesize specifies the tile size for processing (0 or >= 32), and model specifies the name of the pre-trained model to use.
Once the model is initialized, you can use the upscale method to super-resolve your images:
### Pillow
```python
from PIL import Image
waifu2x = Waifu2x(gpuid=0, scale=2, noise=3)
with Image.open("input.jpg") as image:
image = waifu2x.process_pil(image)
image.save("output.jpg", quality=95)
```
### opencv-python
```python
import cv2
waifu2x = Waifu2x(gpuid=0, scale=2, noise=3)
image = cv2.imdecode(np.fromfile("input.jpg", dtype=np.uint8), cv2.IMREAD_COLOR)
image = waifu2x.process_cv2(image)
cv2.imencode(".jpg", image)[1].tofile("output_cv2.jpg")
```
### ffmpeg
```python
import subprocess as sp
# your ffmpeg parameters
command_out = [FFMPEG_BIN,........]
command_in = [FFMPEG_BIN,........]
pipe_out = sp.Popen(command_out, stdout=sp.PIPE, bufsize=10 ** 8)
pipe_in = sp.Popen(command_in, stdin=sp.PIPE)
waifu2x = Waifu2x(gpuid=0, scale=2, noise=3)
while True:
raw_image = pipe_out.stdout.read(src_width * src_height * 3)
if not raw_image:
break
raw_image = waifu2x.process_bytes(raw_image, src_width, src_height, 3)
pipe_in.stdin.write(raw_image)
```
# Build
[here](https://github.com/Tohrusky/waifu2x-ncnn-py/blob/main/.github/workflows/Release.yml)
_The project just only been tested in Ubuntu 18+ and Debian 9+ environments on Linux, so if the project does not work on your system, please try building it._
# References
The following references were used in the development of this project:
[nihui/waifu2x-ncnn-vulkan](https://github.com/nihui/waifu2x-ncnn-vulkan) - This project was the main inspiration for our work. It provided the core implementation of the Waifu2x algorithm using the ncnn and Vulkan libraries.
[Waifu2x](https://github.com/nagadomi/waifu2x) - Waifu2x is an Image Super-Resolution algorithm for Anime-style art using Deep Convolutional Neural Networks. And it supports photo.
[media2x/waifu2x-ncnn-vulkan-python](https://github.com/media2x/waifu2x-ncnn-vulkan-python) - This project was used as a reference for implementing the wrapper. _Special thanks_ to the original author for sharing the code.
[ncnn](https://github.com/Tencent/ncnn) - ncnn is a high-performance neural network inference framework developed by Tencent AI Lab.
# License
This project is licensed under the BSD 3-Clause - see the [LICENSE file](https://github.com/Tohrusky/realcugan-ncnn-py/blob/main/LICENSE) for details.
Raw data
{
"_id": null,
"home_page": "",
"name": "waifu2x-ncnn-py",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "",
"keywords": "",
"author": "",
"author_email": "Tohrusky <65994850+Tohrusky@users.noreply.github.com>",
"download_url": "",
"platform": null,
"description": "# waifu2x-ncnn-py\n\nPython Binding for waifu2x-ncnn-vulkan with PyBind11\n\n[](https://badge.fury.io/py/waifu2x-ncnn-py?123456)\n[](https://github.com/Tohrusky/waifu2x-ncnn-py/actions/workflows/test_pip.yml)\n[](https://github.com/Tohrusky/waifu2x-ncnn-py/actions/workflows/Release.yml)\n\n\nImage Super-Resolution for Anime-style art using Deep Convolutional Neural Networks. And it supports photo. This wrapper provides an easy-to-use interface for running the pre-trained Waifu2x model.\n\n### Current building status matrix\n\n| System | Status | CPU (32bit) | CPU (64bit) | GPU (32bit) | GPU (64bit) |\n| :-----------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------: | :----------------: | :---------: | :----------------: |\n| Linux (Clang) | [](https://github.com/Tohrusky/waifu2x-ncnn-py/actions/workflows/CI-Linux-x64-Clang.yml) | \u2014 | :white_check_mark: | \u2014 | :white_check_mark: |\n| Linux (GCC) | [](https://github.com/Tohrusky/waifu2x-ncnn-py/actions/workflows/CI-Linux-x64-GCC.yml) | \u2014 | :white_check_mark: | \u2014 | :white_check_mark: |\n| Windows | [](https://github.com/Tohrusky/waifu2x-ncnn-py/actions/workflows/CI-Windows-x64-MSVC.yml) | \u2014 | :white_check_mark: | \u2014 | :white_check_mark: |\n| MacOS | [](https://github.com/Tohrusky/waifu2x-ncnn-py/actions/workflows/CI-MacOS-Universal-Clang.yml) | \u2014 | :white_check_mark: | \u2014 | :white_check_mark: |\n| MacOS (ARM) | [](https://github.com/Tohrusky/waifu2x-ncnn-py/actions/workflows/CI-MacOS-Universal-Clang.yml) | \u2014 | :white_check_mark: | \u2014 | :white_check_mark: |\n\n# Usage\n\nTo use this package, simply install it via pip:\n\n```sh\npip install waifu2x-ncnn-py\n```\n\nFor Linux user:\n\n```sh\napt install -y libomp5 libvulkan-dev\n```\n\nThen, import the Waifu2x class from the package:\n\n```python\nfrom src.waifu2x_ncnn_py import Waifu2x\n```\n\nTo initialize the model:\n\n```python\nwaifu2x = Waifu2x(gpuid: int = 0, tta_mode: bool = False, num_threads: int = 1, noise: int = 0, scale: int = 2, tilesize: int = 0, model: str = \"models-cunet\")\n# model can be \"models-cunet\", \"models-upconv_7_anime_style_art_rgb\" and \"models-upconv_7_photo\"\n# or an absolute path to the models' directory\n```\n\nHere, gpuid specifies the GPU device to use (-1 means use CPU), tta_mode enables test-time augmentation, num_threads sets the number of threads for processing, noise specifies the level of noise to apply to the image (-1 to 3), scale is the scaling factor for super-resolution (1 to 4), tilesize specifies the tile size for processing (0 or >= 32), and model specifies the name of the pre-trained model to use.\n\nOnce the model is initialized, you can use the upscale method to super-resolve your images:\n\n### Pillow\n\n```python\nfrom PIL import Image\nwaifu2x = Waifu2x(gpuid=0, scale=2, noise=3)\nwith Image.open(\"input.jpg\") as image:\n image = waifu2x.process_pil(image)\n image.save(\"output.jpg\", quality=95)\n```\n\n### opencv-python\n\n```python\nimport cv2\nwaifu2x = Waifu2x(gpuid=0, scale=2, noise=3)\nimage = cv2.imdecode(np.fromfile(\"input.jpg\", dtype=np.uint8), cv2.IMREAD_COLOR)\nimage = waifu2x.process_cv2(image)\ncv2.imencode(\".jpg\", image)[1].tofile(\"output_cv2.jpg\")\n```\n\n### ffmpeg\n\n```python\nimport subprocess as sp\n# your ffmpeg parameters\ncommand_out = [FFMPEG_BIN,........]\ncommand_in = [FFMPEG_BIN,........]\npipe_out = sp.Popen(command_out, stdout=sp.PIPE, bufsize=10 ** 8)\npipe_in = sp.Popen(command_in, stdin=sp.PIPE)\nwaifu2x = Waifu2x(gpuid=0, scale=2, noise=3)\nwhile True:\n raw_image = pipe_out.stdout.read(src_width * src_height * 3)\n if not raw_image:\n break\n raw_image = waifu2x.process_bytes(raw_image, src_width, src_height, 3)\n pipe_in.stdin.write(raw_image)\n```\n\n# Build\n\n[here](https://github.com/Tohrusky/waifu2x-ncnn-py/blob/main/.github/workflows/Release.yml)\n\n_The project just only been tested in Ubuntu 18+ and Debian 9+ environments on Linux, so if the project does not work on your system, please try building it._\n\n# References\n\nThe following references were used in the development of this project:\n\n[nihui/waifu2x-ncnn-vulkan](https://github.com/nihui/waifu2x-ncnn-vulkan) - This project was the main inspiration for our work. It provided the core implementation of the Waifu2x algorithm using the ncnn and Vulkan libraries.\n\n[Waifu2x](https://github.com/nagadomi/waifu2x) - Waifu2x is an Image Super-Resolution algorithm for Anime-style art using Deep Convolutional Neural Networks. And it supports photo.\n\n[media2x/waifu2x-ncnn-vulkan-python](https://github.com/media2x/waifu2x-ncnn-vulkan-python) - This project was used as a reference for implementing the wrapper. _Special thanks_ to the original author for sharing the code.\n\n[ncnn](https://github.com/Tencent/ncnn) - ncnn is a high-performance neural network inference framework developed by Tencent AI Lab.\n\n# License\n\nThis project is licensed under the BSD 3-Clause - see the [LICENSE file](https://github.com/Tohrusky/realcugan-ncnn-py/blob/main/LICENSE) for details.\n",
"bugtrack_url": null,
"license": "BSD-3-Clause",
"summary": "",
"version": "2.0.0",
"project_urls": {
"Homepage": "https://github.com/Final2x/waifu2x-ncnn-py",
"Repository": "https://github.com/Final2x/waifu2x-ncnn-py"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "1999a6f8cc4fd62f4deabc357d6aded681af94278e3063a8477f213bdec5055c",
"md5": "464fca16aeb27ff9e9e2f833094d22c5",
"sha256": "18584cde42ce417df70001c0dc8c8763401b0e20ae57a2b2fef5edbb4f4a8ca9"
},
"downloads": -1,
"filename": "waifu2x_ncnn_py-2.0.0-cp310-none-macosx_11_0_universal2.whl",
"has_sig": false,
"md5_digest": "464fca16aeb27ff9e9e2f833094d22c5",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.8",
"size": 40877463,
"upload_time": "2023-12-30T10:38:12",
"upload_time_iso_8601": "2023-12-30T10:38:12.218675Z",
"url": "https://files.pythonhosted.org/packages/19/99/a6f8cc4fd62f4deabc357d6aded681af94278e3063a8477f213bdec5055c/waifu2x_ncnn_py-2.0.0-cp310-none-macosx_11_0_universal2.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "9c1e2d3143d0823dff2644c07e7eeca0414cc9112b37a1a8723393b1127516a9",
"md5": "dfd04cb24a8698095fc2256329019c51",
"sha256": "89514098703361aff0e787f2d62c2786f5c637c98ff530e68079253422baef62"
},
"downloads": -1,
"filename": "waifu2x_ncnn_py-2.0.0-cp310-none-manylinux1_x86_64.whl",
"has_sig": false,
"md5_digest": "dfd04cb24a8698095fc2256329019c51",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.8",
"size": 36027683,
"upload_time": "2023-12-30T10:38:17",
"upload_time_iso_8601": "2023-12-30T10:38:17.474839Z",
"url": "https://files.pythonhosted.org/packages/9c/1e/2d3143d0823dff2644c07e7eeca0414cc9112b37a1a8723393b1127516a9/waifu2x_ncnn_py-2.0.0-cp310-none-manylinux1_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "cc630b4ffe36152ee2826f204eb787bbf490f7aab918beb101a4d89b562c6a45",
"md5": "6d83b7fa3785b0feef5fc8b8e3e31ff1",
"sha256": "56380d72bf51604f57dd695d9632fed0eff1d6e97339390b6ba2fdab5a32f7b7"
},
"downloads": -1,
"filename": "waifu2x_ncnn_py-2.0.0-cp310-none-win_amd64.whl",
"has_sig": false,
"md5_digest": "6d83b7fa3785b0feef5fc8b8e3e31ff1",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.8",
"size": 34563442,
"upload_time": "2023-12-30T10:38:20",
"upload_time_iso_8601": "2023-12-30T10:38:20.748029Z",
"url": "https://files.pythonhosted.org/packages/cc/63/0b4ffe36152ee2826f204eb787bbf490f7aab918beb101a4d89b562c6a45/waifu2x_ncnn_py-2.0.0-cp310-none-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "e20ca519f08fef390bb5e3843e21e7cf3ffc283e6267f217b01520b56b20dcb8",
"md5": "2561dbe02552aca27a45c9a9a284c5dc",
"sha256": "2e76989552a70e62dba24f017e4ec15701dfd22e5865afbdf6b9aa355b00ffe2"
},
"downloads": -1,
"filename": "waifu2x_ncnn_py-2.0.0-cp311-none-macosx_11_0_universal2.whl",
"has_sig": false,
"md5_digest": "2561dbe02552aca27a45c9a9a284c5dc",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.8",
"size": 40879411,
"upload_time": "2023-12-30T10:38:24",
"upload_time_iso_8601": "2023-12-30T10:38:24.415570Z",
"url": "https://files.pythonhosted.org/packages/e2/0c/a519f08fef390bb5e3843e21e7cf3ffc283e6267f217b01520b56b20dcb8/waifu2x_ncnn_py-2.0.0-cp311-none-macosx_11_0_universal2.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "a2c79202b1223c4a0dd651c9b7aacd774954cd50bd6ec8e786ec3e92769910b3",
"md5": "fe9119dc22e5e1a10eaf608f52ea45bb",
"sha256": "1f991d6c066bf3f5f038fc9ece448ad23f0d91cf75b62e10870e645c29a05d7c"
},
"downloads": -1,
"filename": "waifu2x_ncnn_py-2.0.0-cp311-none-manylinux1_x86_64.whl",
"has_sig": false,
"md5_digest": "fe9119dc22e5e1a10eaf608f52ea45bb",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.8",
"size": 36030227,
"upload_time": "2023-12-30T10:38:27",
"upload_time_iso_8601": "2023-12-30T10:38:27.911860Z",
"url": "https://files.pythonhosted.org/packages/a2/c7/9202b1223c4a0dd651c9b7aacd774954cd50bd6ec8e786ec3e92769910b3/waifu2x_ncnn_py-2.0.0-cp311-none-manylinux1_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "51b63e99bd679d05152c4aceda57e374796b69511fffd2ba79ee754bb76aa09a",
"md5": "f7fd397f2b3cd54df2b7b5555a4a8d58",
"sha256": "0a00d709ed1f666dd5d0be4eae8a66589ebe991513a8409e6fa7d93efe83b4ec"
},
"downloads": -1,
"filename": "waifu2x_ncnn_py-2.0.0-cp311-none-win_amd64.whl",
"has_sig": false,
"md5_digest": "f7fd397f2b3cd54df2b7b5555a4a8d58",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.8",
"size": 34565069,
"upload_time": "2023-12-30T10:38:30",
"upload_time_iso_8601": "2023-12-30T10:38:30.902337Z",
"url": "https://files.pythonhosted.org/packages/51/b6/3e99bd679d05152c4aceda57e374796b69511fffd2ba79ee754bb76aa09a/waifu2x_ncnn_py-2.0.0-cp311-none-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "c58b474088dfeb3051a7bc865746c76c12e7ab2629cd191703990bda71574ddc",
"md5": "90bd565deb844eecea3106320eb0524a",
"sha256": "b2fcf0887fb489779aa24cc6124f34b92633108356d86e699b797b96bd530b1b"
},
"downloads": -1,
"filename": "waifu2x_ncnn_py-2.0.0-cp312-none-macosx_11_0_universal2.whl",
"has_sig": false,
"md5_digest": "90bd565deb844eecea3106320eb0524a",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.8",
"size": 40877998,
"upload_time": "2023-12-30T10:38:34",
"upload_time_iso_8601": "2023-12-30T10:38:34.730209Z",
"url": "https://files.pythonhosted.org/packages/c5/8b/474088dfeb3051a7bc865746c76c12e7ab2629cd191703990bda71574ddc/waifu2x_ncnn_py-2.0.0-cp312-none-macosx_11_0_universal2.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "7fadce2dff93a14f46d9d18d725b288d8284572bdbc498d30fbab4b55cf5a861",
"md5": "0fb224c1f3724240413ca41c816b0537",
"sha256": "70db561fb00c2dd9b184c3a52dfb2a69f4a0eebf0cc9e21aa6ab7fc4978995f2"
},
"downloads": -1,
"filename": "waifu2x_ncnn_py-2.0.0-cp312-none-manylinux1_x86_64.whl",
"has_sig": false,
"md5_digest": "0fb224c1f3724240413ca41c816b0537",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.8",
"size": 36031173,
"upload_time": "2023-12-30T10:38:38",
"upload_time_iso_8601": "2023-12-30T10:38:38.186630Z",
"url": "https://files.pythonhosted.org/packages/7f/ad/ce2dff93a14f46d9d18d725b288d8284572bdbc498d30fbab4b55cf5a861/waifu2x_ncnn_py-2.0.0-cp312-none-manylinux1_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "86a53acbd64f42abb5f46c438d2a61560c01bb22ac7f8cd80cc1ca0ff4cc187b",
"md5": "431cea66e7e98102703ed681edc3cf5c",
"sha256": "5b480bcf34e8ed82bea0fe1b9650f63735351c5ada60a09b64bde1914dc79233"
},
"downloads": -1,
"filename": "waifu2x_ncnn_py-2.0.0-cp312-none-win_amd64.whl",
"has_sig": false,
"md5_digest": "431cea66e7e98102703ed681edc3cf5c",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.8",
"size": 34564909,
"upload_time": "2023-12-30T10:38:41",
"upload_time_iso_8601": "2023-12-30T10:38:41.594192Z",
"url": "https://files.pythonhosted.org/packages/86/a5/3acbd64f42abb5f46c438d2a61560c01bb22ac7f8cd80cc1ca0ff4cc187b/waifu2x_ncnn_py-2.0.0-cp312-none-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "1b77184a325ea68aa81a0b4069c6ea13ddebb0da5c25907d4bf488e450ecee73",
"md5": "2da11f326157862fc10098569aa4c766",
"sha256": "1e377ec0734c3fa9ae1a21fa26fc33e1408ee1a1e34863060ee7d3dfb55677d6"
},
"downloads": -1,
"filename": "waifu2x_ncnn_py-2.0.0-cp38-none-macosx_10_15_x86_64.whl",
"has_sig": false,
"md5_digest": "2da11f326157862fc10098569aa4c766",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.8",
"size": 40877321,
"upload_time": "2023-12-30T10:38:45",
"upload_time_iso_8601": "2023-12-30T10:38:45.018456Z",
"url": "https://files.pythonhosted.org/packages/1b/77/184a325ea68aa81a0b4069c6ea13ddebb0da5c25907d4bf488e450ecee73/waifu2x_ncnn_py-2.0.0-cp38-none-macosx_10_15_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "6fd4144f3615441279b202408c953af30e669123925c548bf2fc0e41f0ef6c86",
"md5": "dc0399b41531e9cd931822d8a80236d6",
"sha256": "2c6f5e2a9f0cee02d938f83da2f6e4730ce3cd2d2d9553044cd3963c009ce956"
},
"downloads": -1,
"filename": "waifu2x_ncnn_py-2.0.0-cp38-none-manylinux1_x86_64.whl",
"has_sig": false,
"md5_digest": "dc0399b41531e9cd931822d8a80236d6",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.8",
"size": 36027656,
"upload_time": "2023-12-30T10:38:48",
"upload_time_iso_8601": "2023-12-30T10:38:48.463814Z",
"url": "https://files.pythonhosted.org/packages/6f/d4/144f3615441279b202408c953af30e669123925c548bf2fc0e41f0ef6c86/waifu2x_ncnn_py-2.0.0-cp38-none-manylinux1_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "0386bfa61e21b38208a44da506597fb4ce33704f5fd9aba589630b2c5f1fc0c8",
"md5": "5798c2455583be905d266433c08c2ca2",
"sha256": "7bfc21c6c09629d1cca3148b66e79384fedcfc28294fddab1eb042af471f591f"
},
"downloads": -1,
"filename": "waifu2x_ncnn_py-2.0.0-cp38-none-win_amd64.whl",
"has_sig": false,
"md5_digest": "5798c2455583be905d266433c08c2ca2",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.8",
"size": 34563522,
"upload_time": "2023-12-30T10:38:51",
"upload_time_iso_8601": "2023-12-30T10:38:51.169868Z",
"url": "https://files.pythonhosted.org/packages/03/86/bfa61e21b38208a44da506597fb4ce33704f5fd9aba589630b2c5f1fc0c8/waifu2x_ncnn_py-2.0.0-cp38-none-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "cf6b40009d21e47935982f2015908a4481ee11939cafa639eb24f6dc137165a4",
"md5": "c2285c34a916c73b49aac771394428f1",
"sha256": "7ac6f2bdb6709c7459e72b657c151d2943b95d3af5257c915089a33db8cc6cb3"
},
"downloads": -1,
"filename": "waifu2x_ncnn_py-2.0.0-cp39-none-macosx_11_0_universal2.whl",
"has_sig": false,
"md5_digest": "c2285c34a916c73b49aac771394428f1",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.8",
"size": 40877765,
"upload_time": "2023-12-30T10:38:55",
"upload_time_iso_8601": "2023-12-30T10:38:55.153951Z",
"url": "https://files.pythonhosted.org/packages/cf/6b/40009d21e47935982f2015908a4481ee11939cafa639eb24f6dc137165a4/waifu2x_ncnn_py-2.0.0-cp39-none-macosx_11_0_universal2.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "a81b0c7c43a79b3c04ea2c07121ac6efafe19e705340862aeb147937ceeae025",
"md5": "db71c2f287d985f08f9ef46e471c6b98",
"sha256": "3b08ff93ac1382386977d95f07ab099892e45e1ee734d7b462e70cd47ade28e6"
},
"downloads": -1,
"filename": "waifu2x_ncnn_py-2.0.0-cp39-none-manylinux1_x86_64.whl",
"has_sig": false,
"md5_digest": "db71c2f287d985f08f9ef46e471c6b98",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.8",
"size": 36027753,
"upload_time": "2023-12-30T10:38:58",
"upload_time_iso_8601": "2023-12-30T10:38:58.786945Z",
"url": "https://files.pythonhosted.org/packages/a8/1b/0c7c43a79b3c04ea2c07121ac6efafe19e705340862aeb147937ceeae025/waifu2x_ncnn_py-2.0.0-cp39-none-manylinux1_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "82e57f501a23ef2b54564ca6b320ff6440719ee0694b0a619b2acf28b8f09cbf",
"md5": "a02bc1287840227b46b07afcbc00a4ea",
"sha256": "b546242b14b6e2132097a431aa902df634e44efad8101c46d034f0f8779cfa5e"
},
"downloads": -1,
"filename": "waifu2x_ncnn_py-2.0.0-cp39-none-win_amd64.whl",
"has_sig": false,
"md5_digest": "a02bc1287840227b46b07afcbc00a4ea",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.8",
"size": 34563508,
"upload_time": "2023-12-30T10:39:02",
"upload_time_iso_8601": "2023-12-30T10:39:02.188643Z",
"url": "https://files.pythonhosted.org/packages/82/e5/7f501a23ef2b54564ca6b320ff6440719ee0694b0a619b2acf28b8f09cbf/waifu2x_ncnn_py-2.0.0-cp39-none-win_amd64.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-12-30 10:38:12",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "Final2x",
"github_project": "waifu2x-ncnn-py",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "waifu2x-ncnn-py"
}
