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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 lora |
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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_lora |
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torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora |
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torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora |
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torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lora |
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torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_lora |
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torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_lora |
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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_networks.register_extra_network(extra_networks_lora.ExtraNetworkLora()) |
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if not hasattr(torch.nn, 'Linear_forward_before_lora'): |
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torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward |
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if not hasattr(torch.nn, 'Linear_load_state_dict_before_lora'): |
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torch.nn.Linear_load_state_dict_before_lora = torch.nn.Linear._load_from_state_dict |
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if not hasattr(torch.nn, 'Conv2d_forward_before_lora'): |
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torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward |
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if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lora'): |
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torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict |
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if not hasattr(torch.nn, 'MultiheadAttention_forward_before_lora'): |
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torch.nn.MultiheadAttention_forward_before_lora = torch.nn.MultiheadAttention.forward |
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if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_lora'): |
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torch.nn.MultiheadAttention_load_state_dict_before_lora = torch.nn.MultiheadAttention._load_from_state_dict |
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torch.nn.Linear.forward = lora.lora_Linear_forward |
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torch.nn.Linear._load_from_state_dict = lora.lora_Linear_load_state_dict |
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torch.nn.Conv2d.forward = lora.lora_Conv2d_forward |
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torch.nn.Conv2d._load_from_state_dict = lora.lora_Conv2d_load_state_dict |
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torch.nn.MultiheadAttention.forward = lora.lora_MultiheadAttention_forward |
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torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention_load_state_dict |
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script_callbacks.on_model_loaded(lora.assign_lora_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(lora.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 Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras), |
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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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})) |
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shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), { |
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"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"), |
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})) |
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def create_lora_json(obj: lora.LoraOnDisk): |
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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_loras(_: 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 lora.available_loras.values()] |
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script_callbacks.on_app_started(api_loras) |
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