fix the issue with training on SD2.0
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e672cfb074
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311354c0bb
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@ -228,6 +228,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
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model.sd_model_checkpoint = checkpoint_file
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model.sd_model_checkpoint = checkpoint_file
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model.sd_checkpoint_info = checkpoint_info
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model.sd_checkpoint_info = checkpoint_info
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model.logvar = model.logvar.to(devices.device) # fix for training
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sd_vae.delete_base_vae()
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sd_vae.delete_base_vae()
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sd_vae.clear_loaded_vae()
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sd_vae.clear_loaded_vae()
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vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
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vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
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@ -282,7 +282,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
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return embedding, filename
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return embedding, filename
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scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
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scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
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# dataset loading may take a while, so input validations and early returns should be done before this
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# dataset loading may take a while, so input validations and early returns should be done before this
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shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
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shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
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old_parallel_processing_allowed = shared.parallel_processing_allowed
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old_parallel_processing_allowed = shared.parallel_processing_allowed
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@ -310,7 +310,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
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loss_step = 0
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loss_step = 0
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_loss_step = 0 #internal
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_loss_step = 0 #internal
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last_saved_file = "<none>"
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last_saved_file = "<none>"
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last_saved_image = "<none>"
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last_saved_image = "<none>"
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forced_filename = "<none>"
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forced_filename = "<none>"
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