65 lines
2.3 KiB
Python
65 lines
2.3 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.textual_inversion.preprocess
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import modules.textual_inversion.textual_inversion
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from modules import devices, sd_hijack, shared
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from modules.hypernetworks import hypernetwork
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not_available = ["hardswish", "multiheadattention"]
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keys = ["linear"] + list(x for x in 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):
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# Remove illegal characters from name.
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name = "".join( x for x in name if (x.isalnum() or x in "._- "))
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fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
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if not overwrite_old:
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assert not os.path.exists(fn), f"file {fn} already exists"
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if type(layer_structure) == str:
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layer_structure = [float(x.strip()) for x in layer_structure.split(",")]
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hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(
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name=name,
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enable_sizes=[int(x) for x in enable_sizes],
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layer_structure=layer_structure,
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activation_func=activation_func,
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weight_init=weight_init,
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add_layer_norm=add_layer_norm,
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use_dropout=use_dropout,
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)
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hypernet.save(fn)
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shared.reload_hypernetworks()
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return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", ""
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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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