| import html |
|
|
| import gradio as gr |
| import modules.hypernetworks.hypernetwork |
| from modules import devices, sd_hijack, shared |
|
|
| not_available = ["hardswish", "multiheadattention"] |
| keys = [x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict if x not in not_available] |
|
|
|
|
| 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): |
| filename = modules.hypernetworks.hypernetwork.create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, dropout_structure) |
|
|
| return gr.Dropdown.update(choices=sorted(shared.hypernetworks)), f"Created: {filename}", "" |
|
|
|
|
| def train_hypernetwork(*args): |
| shared.loaded_hypernetworks = [] |
|
|
| assert not shared.cmd_opts.lowvram, 'Training models with lowvram is not possible' |
|
|
| try: |
| sd_hijack.undo_optimizations() |
|
|
| hypernetwork, filename = modules.hypernetworks.hypernetwork.train_hypernetwork(*args) |
|
|
| res = f""" |
| Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps. |
| Hypernetwork saved to {html.escape(filename)} |
| """ |
| return res, "" |
| except Exception: |
| raise |
| finally: |
| shared.sd_model.cond_stage_model.to(devices.device) |
| shared.sd_model.first_stage_model.to(devices.device) |
| sd_hijack.apply_optimizations() |
|
|
|
|
