42 lines
1.6 KiB
Python
42 lines
1.6 KiB
Python
import html
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import os
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import re
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import gradio as gr
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import modules.hypernetworks.hypernetwork
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from modules import devices, sd_hijack, shared
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not_available = ["hardswish", "multiheadattention"]
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keys = list(x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
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def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None):
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filename = modules.hypernetworks.hypernetwork.create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, dropout_structure)
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return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {filename}", ""
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def train_hypernetwork(*args):
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initial_hypernetwork = shared.loaded_hypernetwork
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assert not shared.cmd_opts.lowvram, 'Training models with lowvram is not possible'
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try:
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sd_hijack.undo_optimizations()
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hypernetwork, filename = modules.hypernetworks.hypernetwork.train_hypernetwork(*args)
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res = f"""
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Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.
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Hypernetwork 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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shared.loaded_hypernetwork = initial_hypernetwork
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shared.sd_model.cond_stage_model.to(devices.device)
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shared.sd_model.first_stage_model.to(devices.device)
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sd_hijack.apply_optimizations()
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