Call weighted_forward during training

modules/hypernetworks/hypernetwork.py

@@ -640,13 +640,14 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
                 
                 with devices.autocast():
                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
+                    w = batch.weight.to(devices.device, non_blocking=pin_memory)
                     if tag_drop_out != 0 or shuffle_tags:
                         shared.sd_model.cond_stage_model.to(devices.device)
                         c = shared.sd_model.cond_stage_model(batch.cond_text).to(devices.device, non_blocking=pin_memory)
                         shared.sd_model.cond_stage_model.to(devices.cpu)
                     else:
                         c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory)
-                    loss = shared.sd_model(x, c)[0] / gradient_step
+                    loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step
                     del x
                     del c
 

modules/textual_inversion/textual_inversion.py

@@ -480,6 +480,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
             
                 with devices.autocast():
                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
+                    w = batch.weight.to(devices.device, non_blocking=pin_memory)
                     c = shared.sd_model.cond_stage_model(batch.cond_text)
 
                     if is_training_inpainting_model:
@@ -490,7 +491,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
                     else:
                         cond = c
 
-                    loss = shared.sd_model(x, cond)[0] / gradient_step
+                    loss = shared.sd_model.weighted_forward(x, cond, w)[0] / gradient_step
                     del x
 
                     _loss_step += loss.item()