ComfyUI-TeaNodes

ComfyUI-TeaNodes

Adds a few new nodes:

Image Equalization CLAHE (This node can't be any more Kornia)

• When images have their histogram smoothly distributed, I'd say it gives ControlNet preprocessor an easier time.
• Works great when image BG is removed.

Image Size Approximation

• Works based on pixel count that retains Image ratio.
• This algorithm is really dumbly written, but it works.
• Great for stabilizing the speed of image-to-image generation.

Image Resize

• Takes size tuple from Size Approximation Node.
• Seriously, image size always has 2 numbers, why can't it fit through a single wire?
• It also defaults to a node socket instead of having to convert it into input from a widget.

Image Scale

• Simply multiplies image size by a factor.
• 20240425: Now scales smoothly when multiplied by non-power-of-2 factors - use LANCZOS option now
• Have an easier time saving in-process scrap images at half or quarter resolution.

Crop To

• Crop an image to the same size of the reference image
• Hate some of those nodes that complains about bad tensor dimensions for whatever reasons? Fear no longer.

KorniaGamma

• This works like an easy/complex/idk brightness/contrast/level adjustment node
• Image processing power must be utilized for better results.

Random & Non-Random Color Fill

• Fill based on reference image size
• Randomness defined by original color and a variance in hue, saturation and value
• Hopefully the color input field works for most color string formats.

Random Lora & Model

<<<<<<< HEAD

• Uses regex pattern to filter down files within models/checkpoints and models/loras and then randomly choose one to load.
• every parameter allows the chosen model to ran certain number of times.
• pause parameter allows the chosen model to run indefinitely.
• skip parameter and changes in pattern parameter will trigger a random selection ASAP, skip doesn't stop loop counting, but changes in pattern will.
• Models are pooled, which can lead to maxing out the RAM usage, Python seem to be able to handle this on its own without issue.
• If a model has been used before in the same session, the node won't need to access the file from SSD/HDD again, it's already in the RAM.
• RESULT outputs the working state of the node, use Preview As Text or Show Text node to understand time until next randomization, which model is being used currently, previously and how many times, or which ones hasn't been used yet from the found files.
• If the regex pattern went wrong and there are no models found, a random model will be selected from all available under the respective checkpoints or loras folder. =======
• Uses regex pattern to filter down files within models/checkpoints, models/loras directory and then randomly choose one to load.
• every=# parameter allows the chosen model to run only # number of times until randomly selecting another.
• pause=True allows the current running model to run indefinitely.
• skip=True or any changes in pattern will trigger a random selection ASAP, skip doesn't stop loop counting, but changes in pattern will.
• Models are pooled, which can lead to maxing out the RAM usage, Python seem to be able to handle this on its own without issue.
  • If a model has been used before in the same session, the node won't need to access the file from SSD/HDD again, it's already in the RAM.
• RESULT outputs the working state of the node, use Preview As Text or Show Text node to understand time until next randomization, which model is being used currently, previously and how many times, or which ones hasn't been used yet from the found files.
• If the regex pattern went wrong or if there are no models found, a random model will be selected from all available under the respective checkpoints or loras folder.

0f38763 (Update README.md)