Catppuccin Wallpapers
I was in need of wallpapers conforming to the catppuccin colorscheme. So, I wrote a simple script to iteratively generate a set of png's, that I can randomly assign as my desktop background using feh.
Using a small configuration file (see the tux config), I define a few properties. Most crucially, the xpath of the element to change and an associated color palette.
All png's are hosted in the assets branch. For convenient viewing, markdown docs are auto-generated as well containing the associated images.
Walls
Click on an image below to see all of it's pre-generated pngs.
 |
 |
 |
 |
You can find .tar.gz's to download of all the pre-generated png's in the releases.
Setup
Dependencies
git clone [email protected]:daylinmorgan/catppuccin-wallpapers.git
Usage
Generate a new directory under src. Within that, add a base.svg and config.toml.
Find the relevant xpath from your svg to change. Define the style attribute. For now only a change in color is supported. However if there are other properties i.e. stroke width or opacity. You should include these in the style string with along with {color_value} which will be taken from the palette and populated.
style = "fill:{color_value};stroke-width:10;"
The script will determine all possible color combinations for each palette and svg element.
Currently the generate_pngs.py script will only make a simple modification to the underlying svg xml. You can look at src/tux for an example config.toml.
Correctly setting the xpath may take some experimentation.
./generate-pngs.py src/cool-wallpaper
Then run just generate-assets update-assets.
373 Dec 26, 2022
68 Oct 19, 2022
100 Dec 18, 2022
196 Dec 17, 2022
23 Dec 9, 2022
17 Aug 23, 2021
98 Jan 7, 2023
67 Dec 13, 2022
![A repository that shares tuning results of trained models generated by TensorFlow / Keras. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. TensorFlow Lite. OpenVINO. CoreML. TensorFlow.js. TF-TRT. MediaPipe. ONNX. [.tflite,.h5,.pb,saved_model,tfjs,tftrt,mlmodel,.xml/.bin, .onnx]](https://user-images.githubusercontent.com/33194443/104581604-2592cb00-56a2-11eb-9610-5eaa0afb6e1f.png)
2.4k Jan 5, 2023
17 Dec 15, 2022
4 Dec 8, 2022
425 Dec 22, 2022
1 Oct 31, 2021
1 Nov 1, 2021
3 May 1, 2022
20 Sep 28, 2022
149 Dec 29, 2022
2 Dec 24, 2021
1 Dec 19, 2021
1 Nov 1, 2021
48 Nov 21, 2022
13 Aug 17, 2022
6 Apr 6, 2022
4 Jul 9, 2022
2 Nov 6, 2021
11 Sep 6, 2022
1 Dec 22, 2021
828 Dec 28, 2022
4.1k Jan 2, 2023