lora extension rework to include other types of networks

extensions-builtin/Lora/scripts/lora_script.py

@@ -4,18 +4,19 @@ import torch
 import gradio as gr
 from fastapi import FastAPI
 
-import lora
+import network
+import networks
 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
+    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():
@@ -23,50 +24,50 @@ def before_ui():
     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_forward_before_network'):
+    torch.nn.Linear_forward_before_network = 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, '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_lora'):
-    torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward
+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_lora'):
-    torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict
+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, 'MultiheadAttention_forward_before_lora'):
-    torch.nn.MultiheadAttention_forward_before_lora = torch.nn.MultiheadAttention.forward
+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_lora'):
-    torch.nn.MultiheadAttention_load_state_dict_before_lora = torch.nn.MultiheadAttention._load_from_state_dict
+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 = 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
+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.MultiheadAttention.forward = networks.network_MultiheadAttention_forward
+torch.nn.MultiheadAttention._load_from_state_dict = networks.network_MultiheadAttention_load_state_dict
 
-script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
+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(lora.infotext_pasted)
+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 Lora to prompt", gr.Dropdown, lambda: {"choices": ["None", *lora.available_loras]}, refresh=lora.list_available_loras),
+    "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"),
 }))
 
 
 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"),
+    "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: lora.LoraOnDisk):
+def create_lora_json(obj: network.NetworkOnDisk):
     return {
         "name": obj.name,
         "alias": obj.alias,
@@ -75,17 +76,17 @@ def create_lora_json(obj: lora.LoraOnDisk):
     }
 
 
-def api_loras(_: gr.Blocks, app: FastAPI):
+def api_networks(_: 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()]
+        return [create_lora_json(obj) for obj in networks.available_networks.values()]
 
     @app.post("/sdapi/v1/refresh-loras")
     async def refresh_loras():
-        return lora.list_available_loras()
+        return networks.list_available_networks()
 
 
-script_callbacks.on_app_started(api_loras)
+script_callbacks.on_app_started(api_networks)
 
 re_lora = re.compile("<lora:([^:]+):")
 
@@ -98,19 +99,19 @@ def infotext_pasted(infotext, d):
     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):
+    def network_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:
+        network_on_disk = networks.available_network_hash_lookup.get(shorthash)
+        if network_on_disk is None:
             return m.group(0)
 
-        return f'<lora:{lora_on_disk.get_alias()}:'
+        return f'<lora:{network_on_disk.get_alias()}:'
 
-    d["Prompt"] = re.sub(re_lora, lora_replacement, d["Prompt"])
+    d["Prompt"] = re.sub(re_lora, network_replacement, d["Prompt"])
 
 
 script_callbacks.on_infotext_pasted(infotext_pasted)