GitLab

Logging level now defaults to info. --verbose sets it to debug.

* import kornia

* Added morphology nodexs

* Add kornia to requirements

* fix choices

* options, also move to postprocessors

* fix placing and step

* Implement Differential Diffusion

* Cleanup.

* Fix.

* Masks should be applied at full strength.

* Fix colors.

* Register the node.

* Cleaner code.

* Fix issue with getting unipc sampler.

* Adjust thresholds.

* Switch to linear thresholds.

* Only calculate nearest_idx on valid thresholds.

The effnet can be used to encode images for img2img with Stage C.

Allow configurable addition of batch number to output file name.

Put the model file in models/photomaker and use PhotoMakerLoader.

Then use PhotoMakerEncode with the keyword "photomaker" to apply the image

This is an alternative to VAE Encode for inpaint that should work with
lower denoise.

This is a different take on #2501

This is an old model.

Load the checkpoint like a regular one and use the new
SD_4XUpscale_Conditioning node.

Allow VALIDATE_INPUTS to only validate specific inputs.

To use it use the ImageOnlyCheckpointLoader to load the checkpoint and
the new Stable_Zero123 node.

* First SAG test

* need to put extra options on the model instead of patcher

* no errors and results seem not-broken

* Use @ashen-uncensored formula, which works better!!!

* Fix a crash when using weird resolutions. Remove an unnecessary UNet call

* Improve comments, optimize memory in blur routine

* SAG works with sampler_cfg_function

The img2vid model is conditioned on clip vision output only which means
there's no CLIP model which is why I added a ImageOnlyCheckpointLoader to
load it. Note that the unClipCheckpointLoader can also load it because it
also has a CLIP_VISION output.

SDV_img2vid_Conditioning is the node used to pass the right conditioning
to the img2vid model.

VideoLinearCFGGuidance applies a linearly decreasing CFG scale to each
video frame from the cfg set in the sampler node to min_cfg.

SDV_img2vid_Conditioning can be found in conditioning->video_models
ImageOnlyCheckpointLoader can be found in loaders->video_models
VideoLinearCFGGuidance can be found in sampling->video_models

They will show up if both the taesd_encoder and taesd_decoder or taesdxl
model files are present in the models/vae_approx directory.

By adding a downscale to the unet in the first timesteps this node lets
you generate images at higher resolutions with less consistency issues.

This doesn't affect how percentages behave in the frontend but breaks
things if you relied on them in the backend.

percent_to_sigma goes from 0 to 1.0 instead of 1.0 to 0 for less confusion.

Make percent 0 return an extremely large sigma and percent 1.0 return a
zero one to fix imprecision.

This allows changing the sampling parameters of the model (eps or vpred)
or set the model to use zsnr.