Add features for models that may need a non masked loss such as inpainiting.

extensions_built_in/sd_trainer/SDTrainer.py

@@ -1382,7 +1382,7 @@ class SDTrainer(BaseSDTrainProcess):
                         clip_images = batch.clip_image_tensor.to(self.device_torch, dtype=dtype).detach()
 
             mask_multiplier = torch.ones((noisy_latents.shape[0], 1, 1, 1), device=self.device_torch, dtype=dtype)
-            if batch.mask_tensor is not None:
+            if batch.mask_tensor is not None and self.sd.do_masked_loss:
                 with self.timer('get_mask_multiplier'):
                     # upsampling no supported for bfloat16
                     mask_multiplier = batch.mask_tensor.to(self.device_torch, dtype=torch.float16).detach()

toolkit/models/base_model.py

@@ -194,6 +194,9 @@ class BaseModel:
         # can be used on models to invalidate cache if things change.
         self.latent_space_version = None
         
+        # if a mask is passed, do the loss with the mask. May be set false for models that use a mask for other reasons.
+        self.do_masked_loss = True
+
     # properties for old arch for backwards compatibility
     @property
     def unet(self):

toolkit/stable_diffusion_model.py

@@ -232,6 +232,9 @@ class StableDiffusion:
         # can be used on models to invalidate cache if things change.
         self.latent_space_version = None
         
+        # if a mask is passed, do the loss with the mask. May be set false for models that use a mask for other reasons.
+        self.do_masked_loss = True
+        
     # properties for old arch for backwards compatibility
     @property
     def is_xl(self):