43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
import html
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import gradio as gr
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import modules.textual_inversion.textual_inversion
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import modules.textual_inversion.preprocess
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from modules import sd_hijack, shared
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def create_embedding(name, initialization_text, nvpt):
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filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, init_text=initialization_text)
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sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
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return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""
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def preprocess(*args):
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modules.textual_inversion.preprocess.preprocess(*args)
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return "Preprocessing finished.", ""
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def train_embedding(*args):
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assert not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram, 'Training models with lowvram or medvram is not possible'
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try:
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sd_hijack.undo_optimizations()
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embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
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res = f"""
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Training {'interrupted' if shared.state.interrupted else 'finished'} at {embedding.step} steps.
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Embedding saved to {html.escape(filename)}
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"""
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return res, ""
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except Exception:
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raise
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finally:
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sd_hijack.apply_optimizations()
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