Infopost | 2022.08.03
|
![]() |
Add layer applied to a single-channel image. |
![]() |
Add in RGB. |
![]() |
Add in HSV gives you a hue shift. |
![]() |
Adding the Cb and Cr channels gives this color space even more of a hue shift. |
![]() |
Subtraction layer applied to a single-channel image. |
![]() |
Subtraction in RGB. |
![]() |
Subtraction in HSV. |
![]() |
Subtracting YCbCr is pretty deformative. |
![]() |
Multiply, I guess, makes things darker by the amount of darkness being multiplied (0.0-1.0 values). |
![]() |
RGB multiply looks similar. |
![]() |
In HSV, the multiplication is applied less to brightness and more to saturation. |
![]() |
Likewise YCbCr shifts green. |
![]() |
Average in luminance is pretty straightforward. |
![]() |
Average in RGB also makes sense. |
![]() |
Average in HSV sometimes sees a hue shift. |
![]() |
Average YCbCr works like RGB. |
![]() |
Max in monochrome selects the brighter pixel. |
![]() |
Same in RGB. |
![]() |
It's not as straightforward in HSV where hue and saturation impact which pixel value is used. |
![]() |
Max for YCbCr likewise biases toward purple (red and blue) pixels. |
![]() |
Minimum, of course, selects the darker pixels. |
![]() |
Same with RGB. |
![]() |
In HSV, minimum looks for dark, desaturated pixels with hues happening to be near zero. |
![]() |
YCbCr looks for dark, greenish pixels. |
![]() |
In monochrome images you can see the dark details disappear as pooling selects the brightest pixels. |
![]() |
RGB behaves similar to luminance. |
![]() |
HSV makes the occasional weird selection based on hue and saturation. |
![]() |
Much like with maximum and minimum from the previous section, maxpooling on YCbCr biases toward the purplest pixel. |
![]() |
The jaggies (square artifacts) are less obvious in average pooling. |
![]() |
Edges in RGB look more like antialiasing, flat areas look blurred. |
![]() |
HSV again shows some occasional hue shift. |
![]() |
Like with averaging two images, average pooling a single YCbCr image looks just like RGB. |
![]() |
The ReLu looks pretty close to identical. I may not understand the layer, but expected that each output would be fully connected to the inputs. Hmm. |
![]() |
Sigmoid looks like it inverts the input. |
![]() |
Softplus isn't too fond of the dark parts of the panda. |
![]() |
Tanh seems to have more or less just darkened the input. |
Model: "model" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) [(None, 32, 32, 1)] 0 __________________________________________________________________________________________________ flatten (Flatten) (None, 1024) 0 input_1[0][0] __________________________________________________________________________________________________ dense (Dense) (None, 1024) 1049600 flatten[0][0] __________________________________________________________________________________________________ input_2 (InputLayer) [(None, 32, 32, 1)] 0 __________________________________________________________________________________________________ reshape (Reshape) (None, 32, 32, 1) 0 dense[0][0] ================================================================================================== Total params: 1,049,600 Trainable params: 1,049,600 Non-trainable params: 0 __________________________________________________________________________________________________
![]() |
One conv2d layer, kernel size 3, linear activation. |
![]() |
One conv2d layer, kernel size 3, ReLu activation. |
![]() |
One conv2d layer, kernel size 3, sigmoid activation. |
![]() |
One conv2d layer, kernel size 3, softplus activation. |
![]() |
One conv2d layer, kernel size 3, tanh activation. |
![]() |
One conv2d layer, kernel size 7, ReLu activation. |
![]() |
One conv2d layer, kernel size 7, sigmoid activation. |
![]() |
One conv2d layer, kernel size 7, softplus activation. |
![]() |
One conv2d layer, kernel size 7, tanh activation. |
![]() |
Two conv2d layers, kernel size 3, ReLu activation for both. |
![]() |
Two conv2d layers, kernel size 3, ReLu activation and tanh activation. |
![]() |
Two conv2d layers, kernel size 3, tanh activation then ReLu activation. |
![]() |
Two conv2d layers, kernel size 3, tanh activation for both. |
![]() |
Conv2dTranspose, kernel size 2, strides 2, ReLu activation. |
![]() |
Conv2dTranspose, kernel size 2, strides 2, sigmoid activation. |
Conv2dTranspose, kernel size 2, strides 4, tanh activation. |
Conv2dTranspose, kernel size 4, strides 2, ReLu activation. |
Conv2dTranspose, kernel size 4, strides 2, tanh activation. |
Conv2dTranspose, kernel size 8, strides 2, ReLu activation. |
Conv2dTranspose, kernel size 8, strides 2, tanh activation. |
![]() |
2022.11.19
PromptsExperimenting with prompts in Stable Diffusion. |
![]() |
2022.11.14
Stable Diffusion setupGetting Stable Diffusion up and running on Ubuntu 22.04 with an RTX3080 Ti. |
![]() |
2022.06.23
Dall-eExperimenting with Dall-e text inputs, moving a sandbox example offline, and troubleshooting jax/cuda. |