import torch import gradio as gr from fastapi import FastAPI import lora import extra_networks_lora import ui_extra_networks_lora from modules import script_callbacks, ui_extra_networks, extra_networks, shared def unload(): torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lora torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_lora torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_lora def before_ui(): ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora()) extra_networks.register_extra_network(extra_networks_lora.ExtraNetworkLora()) if not hasattr(torch.nn, 'Linear_forward_before_lora'): torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward if not hasattr(torch.nn, 'Linear_load_state_dict_before_lora'): torch.nn.Linear_load_state_dict_before_lora = torch.nn.Linear._load_from_state_dict if not hasattr(torch.nn, 'Conv2d_forward_before_lora'): torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lora'): torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict if not hasattr(torch.nn, 'MultiheadAttention_forward_before_lora'): torch.nn.MultiheadAttention_forward_before_lora = torch.nn.MultiheadAttention.forward if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_lora'): torch.nn.MultiheadAttention_load_state_dict_before_lora = torch.nn.MultiheadAttention._load_from_state_dict torch.nn.Linear.forward = lora.lora_Linear_forward torch.nn.Linear._load_from_state_dict = lora.lora_Linear_load_state_dict torch.nn.Conv2d.forward = lora.lora_Conv2d_forward torch.nn.Conv2d._load_from_state_dict = lora.lora_Conv2d_load_state_dict torch.nn.MultiheadAttention.forward = lora.lora_MultiheadAttention_forward torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention_load_state_dict script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules) script_callbacks.on_script_unloaded(unload) script_callbacks.on_before_ui(before_ui) script_callbacks.on_infotext_pasted(lora.infotext_pasted) shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), { "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras), "lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}), })) shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), { "lora_functional": shared.OptionInfo(False, "Lora: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"), })) def create_lora_json(obj: lora.LoraOnDisk): return { "name": obj.name, "alias": obj.alias, "path": obj.filename, "metadata": obj.metadata, } def api_loras(_: gr.Blocks, app: FastAPI): @app.get("/sdapi/v1/loras") async def get_loras(): return [create_lora_json(obj) for obj in lora.available_loras.values()] script_callbacks.on_app_started(api_loras)