* Update node_helpers.py to use generic pillow wrapper to resolve multiple meta-data related issues.
replaced open_image function with a generic pillow function that takes Pil functions as a dependency injection and applies the ImageFile.LOAD_TRUNCATED_IMAGES try except fix to them.
This provides an extensible function to handle related errors that can wrap offending functions when discovered without the need to repeat code.
* Update a few Pil functions to use node_helpers.pillow wrapper
Update a Pil function calls in a few locations to use the generic node_helpers.pillow wrapper that takes the function as a dependency injection and uses the try except method with ImageFIle.LOAD_TRUNCATED_IMAGES solution
* Corrected comment in issue #s fixed.
* Update node_helpers.py to remove import of Image from PIL
import of Image is no longer required as functions are Injected
I was going to completely remove this function because it is unmaintainable
but I think this is the best compromise.
The clip skip and v_prediction parts of the configs should still work but
not the fp16 vs fp32.
* Fix issue with how PIL loads small PNG files nodes.py
Added flag to prevent ValueError: Decompressed Data Too Large
when loading PNG images with large meta data such as large embedded color profiles
* Update LoadImage node to fix error when loading PNG's in nodes.py
Fixed Value Error: Decompressed Data Too Large thrown by PIL when attempting to opening PNG files with large embedded ICC colorspaces by setting the follow flag to true when loading png images: ImageFile.LOAD_TRUNCATED_IMAGES = True
* Update node_helpers.py to include open_image helper function
open_image includes try except to catch Pillow Value Errors that occur when large ICC profiles are embedded in images.
* Update LoadImage node to use open_image helper function inplace of Image.open
open_image helper function in node_helpers.py fixes a Pillow error when attempting to open images with large embedded ICC profiles by adding an exception handler to load the image with truncated meta data if regular loading is not possible.
* Add TLS Support
* Add to readme
* Add guidance for windows users on generating certificates
* Add guidance for windows users on generating certificates
* Fix typo
This sampler is an LCM sampler that upscales the latent during sampling.
It can be used to generate at a higher resolution with an LCM model very
quickly.
To try it use it with a basic 5 step LCM workflow with scale_ratio 1.5 or
2.0
