import re import torch import gradio as gr from fastapi import FastAPI import network import networks import lora # noqa:F401 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_network torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_network torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_network torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_network torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_network torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_network def before_ui(): ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora()) networks.extra_network_lora = extra_networks_lora.ExtraNetworkLora() extra_networks.register_extra_network(networks.extra_network_lora) extra_networks.register_extra_network_alias(networks.extra_network_lora, "lyco") if not hasattr(torch.nn, 'Linear_forward_before_network'): torch.nn.Linear_forward_before_network = torch.nn.Linear.forward if not hasattr(torch.nn, 'Linear_load_state_dict_before_network'): torch.nn.Linear_load_state_dict_before_network = torch.nn.Linear._load_from_state_dict if not hasattr(torch.nn, 'Conv2d_forward_before_network'): torch.nn.Conv2d_forward_before_network = torch.nn.Conv2d.forward if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_network'): torch.nn.Conv2d_load_state_dict_before_network = torch.nn.Conv2d._load_from_state_dict if not hasattr(torch.nn, 'GroupNorm_forward_before_network'): torch.nn.GroupNorm_forward_before_network = torch.nn.GroupNorm.forward if not hasattr(torch.nn, 'GroupNorm_load_state_dict_before_network'): torch.nn.GroupNorm_load_state_dict_before_network = torch.nn.GroupNorm._load_from_state_dict if not hasattr(torch.nn, 'LayerNorm_forward_before_network'): torch.nn.LayerNorm_forward_before_network = torch.nn.LayerNorm.forward if not hasattr(torch.nn, 'LayerNorm_load_state_dict_before_network'): torch.nn.LayerNorm_load_state_dict_before_network = torch.nn.LayerNorm._load_from_state_dict if not hasattr(torch.nn, 'MultiheadAttention_forward_before_network'): torch.nn.MultiheadAttention_forward_before_network = torch.nn.MultiheadAttention.forward if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_network'): torch.nn.MultiheadAttention_load_state_dict_before_network = torch.nn.MultiheadAttention._load_from_state_dict torch.nn.Linear.forward = networks.network_Linear_forward torch.nn.Linear._load_from_state_dict = networks.network_Linear_load_state_dict torch.nn.Conv2d.forward = networks.network_Conv2d_forward torch.nn.Conv2d._load_from_state_dict = networks.network_Conv2d_load_state_dict torch.nn.GroupNorm.forward = networks.network_GroupNorm_forward torch.nn.GroupNorm._load_from_state_dict = networks.network_GroupNorm_load_state_dict torch.nn.LayerNorm.forward = networks.network_LayerNorm_forward torch.nn.LayerNorm._load_from_state_dict = networks.network_LayerNorm_load_state_dict torch.nn.MultiheadAttention.forward = networks.network_MultiheadAttention_forward torch.nn.MultiheadAttention._load_from_state_dict = networks.network_MultiheadAttention_load_state_dict script_callbacks.on_model_loaded(networks.assign_network_names_to_compvis_modules) script_callbacks.on_script_unloaded(unload) script_callbacks.on_before_ui(before_ui) script_callbacks.on_infotext_pasted(networks.infotext_pasted) shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), { "sd_lora": shared.OptionInfo("None", "Add network to prompt", gr.Dropdown, lambda: {"choices": ["None", *networks.available_networks]}, refresh=networks.list_available_networks), "lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to Lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}), "lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"), "lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"), "lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}), "lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}), })) shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), { "lora_functional": shared.OptionInfo(False, "Lora/Networks: 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: network.NetworkOnDisk): return { "name": obj.name, "alias": obj.alias, "path": obj.filename, "metadata": obj.metadata, } def api_networks(_: gr.Blocks, app: FastAPI): @app.get("/sdapi/v1/loras") async def get_loras(): return [create_lora_json(obj) for obj in networks.available_networks.values()] @app.post("/sdapi/v1/refresh-loras") async def refresh_loras(): return networks.list_available_networks() script_callbacks.on_app_started(api_networks) re_lora = re.compile("