* Add alpha_mask parameter and apply masked loss
* Fix type hint in trim_and_resize_if_required function
* Refactor code to use keyword arguments in train_util.py
* Fix alpha mask flipping logic
* Fix alpha mask initialization
* Fix alpha_mask transformation
* Cache alpha_mask
* Update alpha_masks to be on CPU
* Set flipped_alpha_masks to Null if option disabled
* Check if alpha_mask is None
* Set alpha_mask to None if option disabled
* Add description of alpha_mask option to docs
When trying to load stored latents, if an error occurs, this change will tell you what file failed to load
Currently it will just tell you that something failed without telling you which file
* add huber loss and huber_c compute to train_util
* add reduction modes
* add huber_c retrieval from timestep getter
* move get timesteps and huber to own function
* add conditional loss to all training scripts
* add cond loss to train network
* add (scheduled) huber_loss to args
* fixup twice timesteps getting
* PHL-schedule should depend on noise scheduler's num timesteps
* *2 multiplier to huber loss cause of 1/2 a^2 conv.
The Taylor expansion of sqrt near zero gives 1/2 a^2, which differs from a^2 of the standard MSE loss. This change scales them better against one another
* add option for smooth l1 (huber / delta)
* unify huber scheduling
* add snr huber scheduler
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Co-authored-by: Kohya S <52813779+kohya-ss@users.noreply.github.com>
* support meta cached dataset
* add cache meta scripts
* random ip_noise_gamma strength
* random noise_offset strength
* use correct settings for parser
* cache path/caption/size only
* revert mess up commit
* revert mess up commit
* Update requirements.txt
* Add arguments for meta cache.
* remove pickle implementation
* Return sizes when enable cache
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Co-authored-by: Kohya S <52813779+kohya-ss@users.noreply.github.com>
