GitLab

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

* Add control_filter_list to filter items after queue

* fix regex

* backwards compatibility

* formatting

* revert

* Add and fix test

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

* Refactor multiline widget into generic DOM widget

* wip webp preview

* webp support

* fix check

* fix sizing

* show image when zoomed out

* Swap webp checkto generic animated image flag

* remove duplicate

* Fix falsy check

Pytorch supports two variants of fp8:
--fp8_e4m3fn-text-enc (the one that seems to give better results)
--fp8_e5m2-text-enc

Improve diffusers model detection.

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