| | 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):
|
| | @app.get("/sdapi/v1/loras")
|
| | async def get_loras():
|
| | return [create_lora_json(obj) for obj in lora.available_loras.values()]
|
| |
|
| | @app.post("/sdapi/v1/refresh-loras")
|
| | 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)
|
| |
|
