set descriptions
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parent
45a8b758a7
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3f43d8a966
3 changed files with 12 additions and 3 deletions
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@ -619,7 +619,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
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epoch_num = hypernetwork.step // steps_per_epoch
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epoch_num = hypernetwork.step // steps_per_epoch
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epoch_step = hypernetwork.step % steps_per_epoch
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epoch_step = hypernetwork.step % steps_per_epoch
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pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}")
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description = f"Training hypernetwork [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}"
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pbar.set_description(description)
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shared.state.textinfo = description
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if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
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if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
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# Before saving, change name to match current checkpoint.
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# Before saving, change name to match current checkpoint.
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hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
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hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
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@ -135,7 +135,8 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
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params.process_caption_deepbooru = process_caption_deepbooru
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params.process_caption_deepbooru = process_caption_deepbooru
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params.preprocess_txt_action = preprocess_txt_action
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params.preprocess_txt_action = preprocess_txt_action
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for index, imagefile in enumerate(tqdm.tqdm(files)):
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pbar = tqdm.tqdm(files)
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for index, imagefile in enumerate(pbar):
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params.subindex = 0
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params.subindex = 0
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filename = os.path.join(src, imagefile)
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filename = os.path.join(src, imagefile)
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try:
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try:
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@ -143,6 +144,10 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
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except Exception:
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except Exception:
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continue
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continue
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description = f"Preprocessing [Image {index}/{len(files)}]"
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pbar.set_description(description)
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shared.state.textinfo = description
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params.src = filename
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params.src = filename
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existing_caption = None
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existing_caption = None
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@ -476,7 +476,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
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epoch_num = embedding.step // steps_per_epoch
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epoch_num = embedding.step // steps_per_epoch
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epoch_step = embedding.step % steps_per_epoch
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epoch_step = embedding.step % steps_per_epoch
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pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}")
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description = f"Training textual inversion [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}"
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pbar.set_description(description)
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shared.state.textinfo = description
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if embedding_dir is not None and steps_done % save_embedding_every == 0:
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if embedding_dir is not None and steps_done % save_embedding_every == 0:
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# Before saving, change name to match current checkpoint.
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# Before saving, change name to match current checkpoint.
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embedding_name_every = f'{embedding_name}-{steps_done}'
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embedding_name_every = f'{embedding_name}-{steps_done}'
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