lint fixes

Signed-off-by: Vladimir Mandic <mandic00@live.com>

modules/lora/lora_convert.py

@@ -107,14 +107,14 @@ def make_unet_conversion_map() -> Dict[str, str]:
 
 class KeyConvert:
     def __init__(self):
-            self.is_sdxl = True if shared.sd_model_type == "sdxl" else False
-            self.UNET_CONVERSION_MAP = make_unet_conversion_map() if self.is_sdxl else None
-            self.LORA_PREFIX_UNET = "lora_unet_"
-            self.LORA_PREFIX_TEXT_ENCODER = "lora_te_"
-            self.OFT_PREFIX_UNET = "oft_unet_"
-            # SDXL: must starts with LORA_PREFIX_TEXT_ENCODER
-            self.LORA_PREFIX_TEXT_ENCODER1 = "lora_te1_"
-            self.LORA_PREFIX_TEXT_ENCODER2 = "lora_te2_"
+        self.is_sdxl = True if shared.sd_model_type == "sdxl" else False
+        self.UNET_CONVERSION_MAP = make_unet_conversion_map() if self.is_sdxl else None
+        self.LORA_PREFIX_UNET = "lora_unet_"
+        self.LORA_PREFIX_TEXT_ENCODER = "lora_te_"
+        self.OFT_PREFIX_UNET = "oft_unet_"
+        # SDXL: must starts with LORA_PREFIX_TEXT_ENCODER
+        self.LORA_PREFIX_TEXT_ENCODER1 = "lora_te1_"
+        self.LORA_PREFIX_TEXT_ENCODER2 = "lora_te2_"
 
     def __call__(self, key):
         if self.is_sdxl:
@@ -446,6 +446,7 @@ def _convert_sd_scripts_to_ai_toolkit(sds_sd):
                 lora_name_alpha = f"{lora_name}.alpha"
                 diffusers_name = _convert_text_encoder_lora_key(key, lora_name)
 
+                sd_lora_rank = 1
                 if lora_name.startswith(("lora_te_", "lora_te1_")):
                     down_weight = sds_sd.pop(key)
                     sd_lora_rank = down_weight.shape[0]

modules/lora/network.py

@@ -1,9 +1,11 @@
 import os
-from collections import namedtuple
 import enum
+from typing import Union
+from collections import namedtuple
 
 from modules import sd_models, hashes, shared
 
+
 NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module'])
 metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
 
@@ -105,7 +107,7 @@ def __init__(self, name, network_on_disk: NetworkOnDisk):
 
 
 class ModuleType:
-    def create_module(self, net: Network, weights: NetworkWeights) -> Network | None: # pylint: disable=W0613
+    def create_module(self, net: Network, weights: NetworkWeights) -> Union[Network, None]: # pylint: disable=W0613
         return None
 
 

modules/lora/network_norm.py

@@ -1,5 +1,6 @@
 import modules.lora.network as network
 
+
 class ModuleTypeNorm(network.ModuleType):
     def create_module(self, net: network.Network, weights: network.NetworkWeights):
         if all(x in weights.w for x in ["w_norm", "b_norm"]):

modules/lora/network_oft.py

@@ -1,7 +1,7 @@
 import torch
+from einops import rearrange
 import modules.lora.network as network
 from modules.lora.lyco_helpers import factorization
-from einops import rearrange
 
 
 class ModuleTypeOFT(network.ModuleType):
@@ -10,6 +10,7 @@ def create_module(self, net: network.Network, weights: network.NetworkWeights):
             return NetworkModuleOFT(net, weights)
         return None
 
+
 # Supports both kohya-ss' implementation of COFT  https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
 # and KohakuBlueleaf's implementation of OFT/COFT https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py
 class NetworkModuleOFT(network.NetworkModule): # pylint: disable=abstract-method

modules/lora/networks.py

@@ -3,6 +3,9 @@
 import re
 import time
 import concurrent
+import torch
+import diffusers.models.lora
+
 import modules.lora.network as network
 import modules.lora.network_lora as network_lora
 import modules.lora.network_hada as network_hada
@@ -14,8 +17,6 @@
 import modules.lora.network_glora as network_glora
 import modules.lora.network_overrides as network_overrides
 import modules.lora.lora_convert as lora_convert
-import torch
-import diffusers.models.lora
 from modules import shared, devices, sd_models, sd_models_compile, errors, scripts, files_cache, model_quant
 
 
@@ -74,7 +75,7 @@ def assign_network_names_to_compvis_modules(sd_model):
     shared.sd_model.network_layer_mapping = network_layer_mapping
 
 
-def load_diffusers(name, network_on_disk, lora_scale=shared.opts.extra_networks_default_multiplier) -> network.Network | None:
+def load_diffusers(name, network_on_disk, lora_scale=shared.opts.extra_networks_default_multiplier) -> Union[network.Network, None]:
     name = name.replace(".", "_")
     shared.log.debug(f'Load network: type=LoRA name="{name}" file="{network_on_disk.filename}" detected={network_on_disk.sd_version} method=diffusers scale={lora_scale} fuse={shared.opts.lora_fuse_diffusers}')
     if not shared.native:
@@ -103,7 +104,7 @@ def load_diffusers(name, network_on_disk, lora_scale=shared.opts.extra_networks_
     return net
 
 
-def load_network(name, network_on_disk) -> network.Network | None:
+def load_network(name, network_on_disk) -> Union[network.Network, None]:
     if not shared.sd_loaded:
         return None
 
@@ -173,6 +174,7 @@ def load_network(name, network_on_disk) -> network.Network | None:
     net.bundle_embeddings = bundle_embeddings
     return net
 
+
 def maybe_recompile_model(names, te_multipliers):
     recompile_model = False
     if shared.compiled_model_state is not None and shared.compiled_model_state.is_compiled:
@@ -186,7 +188,7 @@ def maybe_recompile_model(names, te_multipliers):
             if not recompile_model:
                 if len(loaded_networks) > 0 and debug:
                     shared.log.debug('Model Compile: Skipping LoRa loading')
-                return
+                return recompile_model
         else:
             recompile_model = True
             shared.compiled_model_state.lora_model = []
@@ -277,6 +279,7 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
     t1 = time.time()
     timer['load'] += t1 - t0
 
+
 def set_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, diffusers.models.lora.LoRACompatibleLinear, diffusers.models.lora.LoRACompatibleConv], updown, ex_bias):
     weights_backup = getattr(self, "network_weights_backup", None)
     bias_backup = getattr(self, "network_bias_backup", None)
@@ -389,6 +392,7 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
     t1 = time.time()
     timer['apply'] += t1 - t0
 
+
 def network_load():
     sd_model = getattr(shared.sd_model, "pipe", shared.sd_model)  # wrapped model compatiblility
     for component_name in ['text_encoder','text_encoder_2', 'unet', 'transformer']: