127 lines
5.5 KiB
Python
127 lines
5.5 KiB
Python
import re
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import torch
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import gradio as gr
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from fastapi import FastAPI
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import network
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import networks
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import lora # noqa:F401
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import extra_networks_lora
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import ui_extra_networks_lora
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from modules import script_callbacks, ui_extra_networks, extra_networks, shared
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def unload():
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torch.nn.Linear.forward = torch.nn.Linear_forward_before_network
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torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_network
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torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_network
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torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_network
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torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_network
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torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_network
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def before_ui():
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ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
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extra_network = extra_networks_lora.ExtraNetworkLora()
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extra_networks.register_extra_network(extra_network)
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extra_networks.register_extra_network_alias(extra_network, "lyco")
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if not hasattr(torch.nn, 'Linear_forward_before_network'):
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torch.nn.Linear_forward_before_network = torch.nn.Linear.forward
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if not hasattr(torch.nn, 'Linear_load_state_dict_before_network'):
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torch.nn.Linear_load_state_dict_before_network = torch.nn.Linear._load_from_state_dict
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if not hasattr(torch.nn, 'Conv2d_forward_before_network'):
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torch.nn.Conv2d_forward_before_network = torch.nn.Conv2d.forward
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if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_network'):
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torch.nn.Conv2d_load_state_dict_before_network = torch.nn.Conv2d._load_from_state_dict
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if not hasattr(torch.nn, 'MultiheadAttention_forward_before_network'):
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torch.nn.MultiheadAttention_forward_before_network = torch.nn.MultiheadAttention.forward
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if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_network'):
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torch.nn.MultiheadAttention_load_state_dict_before_network = torch.nn.MultiheadAttention._load_from_state_dict
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torch.nn.Linear.forward = networks.network_Linear_forward
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torch.nn.Linear._load_from_state_dict = networks.network_Linear_load_state_dict
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torch.nn.Conv2d.forward = networks.network_Conv2d_forward
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torch.nn.Conv2d._load_from_state_dict = networks.network_Conv2d_load_state_dict
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torch.nn.MultiheadAttention.forward = networks.network_MultiheadAttention_forward
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torch.nn.MultiheadAttention._load_from_state_dict = networks.network_MultiheadAttention_load_state_dict
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script_callbacks.on_model_loaded(networks.assign_network_names_to_compvis_modules)
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script_callbacks.on_script_unloaded(unload)
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script_callbacks.on_before_ui(before_ui)
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script_callbacks.on_infotext_pasted(networks.infotext_pasted)
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shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
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"sd_lora": shared.OptionInfo("None", "Add network to prompt", gr.Dropdown, lambda: {"choices": ["None", *networks.available_networks]}, refresh=networks.list_available_networks),
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"lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to Lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}),
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"lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"),
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"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"),
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"lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}),
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"lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}),
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}))
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shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), {
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"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"),
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}))
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def create_lora_json(obj: network.NetworkOnDisk):
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return {
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"name": obj.name,
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"alias": obj.alias,
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"path": obj.filename,
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"metadata": obj.metadata,
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}
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def api_networks(_: gr.Blocks, app: FastAPI):
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@app.get("/sdapi/v1/loras")
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async def get_loras():
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return [create_lora_json(obj) for obj in networks.available_networks.values()]
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@app.post("/sdapi/v1/refresh-loras")
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async def refresh_loras():
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return networks.list_available_networks()
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script_callbacks.on_app_started(api_networks)
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re_lora = re.compile("<lora:([^:]+):")
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def infotext_pasted(infotext, d):
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hashes = d.get("Lora hashes")
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if not hashes:
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return
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hashes = [x.strip().split(':', 1) for x in hashes.split(",")]
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hashes = {x[0].strip().replace(",", ""): x[1].strip() for x in hashes}
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def network_replacement(m):
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alias = m.group(1)
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shorthash = hashes.get(alias)
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if shorthash is None:
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return m.group(0)
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network_on_disk = networks.available_network_hash_lookup.get(shorthash)
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if network_on_disk is None:
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return m.group(0)
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return f'<lora:{network_on_disk.get_alias()}:'
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d["Prompt"] = re.sub(re_lora, network_replacement, d["Prompt"])
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script_callbacks.on_infotext_pasted(infotext_pasted)
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shared.opts.onchange("lora_in_memory_limit", networks.purge_networks_from_memory)
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