Call weighted_forward during training
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@ -640,13 +640,14 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
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with devices.autocast():
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x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
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w = batch.weight.to(devices.device, non_blocking=pin_memory)
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if tag_drop_out != 0 or shuffle_tags:
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shared.sd_model.cond_stage_model.to(devices.device)
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c = shared.sd_model.cond_stage_model(batch.cond_text).to(devices.device, non_blocking=pin_memory)
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shared.sd_model.cond_stage_model.to(devices.cpu)
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else:
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c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory)
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loss = shared.sd_model(x, c)[0] / gradient_step
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loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step
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del x
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del c
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@ -480,6 +480,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
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with devices.autocast():
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x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
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w = batch.weight.to(devices.device, non_blocking=pin_memory)
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c = shared.sd_model.cond_stage_model(batch.cond_text)
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if is_training_inpainting_model:
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@ -490,7 +491,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
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else:
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cond = c
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loss = shared.sd_model(x, cond)[0] / gradient_step
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loss = shared.sd_model.weighted_forward(x, cond, w)[0] / gradient_step
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del x
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_loss_step += loss.item()
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