GitLab

calc_cond_batch can take an arbitrary amount of cond inputs.

Added a calc_cond_uncond_batch wrapper with a warning so custom nodes
won't break.

* 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.

Use ModelSamplingContinuousEDM with edm_playground_v2.5 selected.

This causes some small changes to the uni pc inpainting behavior but it
seems to improve results slightly.

self.scheduler -> scheduler_name
Co-authored-by: Lt.Dr.Data <lt.dr.data@gmail.com>

Moved all the sag related code to comfy_extras/nodes_sag.py

* 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

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 should make it much easier to write custom nodes with kdiffusion type
samplers.

This was broken because of recent changes so I fixed it and moved it from
the experiments repo.

Use the "lcm" sampler to sample them, you also have to use the
ModelSamplingDiscrete node to set them as lcm models to use them properly.

Converting Nan to zero is a bad idea because it makes it hard to tell when
something went wrong.

This should make things that use sampler_cfg_function behave like before.

Added an input argument for those that want the denoised output.

This means you can calculate the x0 prediction of the model by doing:
(input - cond) for example.