Spaces:
No application file
No application file
| import re | |
| 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", *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"]}), | |
| "lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"), | |
| })) | |
| 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): | |
| async def get_loras(): | |
| return [create_lora_json(obj) for obj in lora.available_loras.values()] | |
| async def refresh_loras(): | |
| return lora.list_available_loras() | |
| script_callbacks.on_app_started(api_loras) | |
| re_lora = re.compile("<lora:([^:]+):") | |
| def infotext_pasted(infotext, d): | |
| hashes = d.get("Lora hashes") | |
| if not hashes: | |
| return | |
| hashes = [x.strip().split(':', 1) for x in hashes.split(",")] | |
| hashes = {x[0].strip().replace(",", ""): x[1].strip() for x in hashes} | |
| def lora_replacement(m): | |
| alias = m.group(1) | |
| shorthash = hashes.get(alias) | |
| if shorthash is None: | |
| return m.group(0) | |
| lora_on_disk = lora.available_lora_hash_lookup.get(shorthash) | |
| if lora_on_disk is None: | |
| return m.group(0) | |
| return f'<lora:{lora_on_disk.get_alias()}:' | |
| d["Prompt"] = re.sub(re_lora, lora_replacement, d["Prompt"]) | |
| script_callbacks.on_infotext_pasted(infotext_pasted) | |