refactor: only use LoRA activate on handover to async worker, extract…

… method

modules/async_worker.py

@@ -46,8 +46,8 @@ def worker():
     from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion, apply_arrays
     from modules.private_logger import log
     from extras.expansion import safe_str
-    from modules.util import remove_empty_str, HWC3, resize_image, \
-        get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix
+    from modules.util import remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil, \
+        get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix, get_enabled_loras
     from modules.upscaler import perform_upscale
     from modules.flags import Performance
     from modules.meta_parser import get_metadata_parser, MetadataScheme
@@ -124,14 +124,6 @@ def build_image_wall(async_task):
         async_task.results = async_task.results + [wall]
         return
 
-    def apply_enabled_loras(loras):
-        enabled_loras = []
-        for lora_enabled, lora_model, lora_weight in loras:
-            if lora_enabled:
-                enabled_loras.append([lora_model, lora_weight])
-
-        return enabled_loras
-
     @torch.no_grad()
     @torch.inference_mode()
     def handler(async_task):
@@ -155,7 +147,7 @@ def handler(async_task):
         base_model_name = args.pop()
         refiner_model_name = args.pop()
         refiner_switch = args.pop()
-        loras = apply_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop()), ] for _ in range(modules.config.default_max_lora_number)])
+        loras = get_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop())] for _ in range(modules.config.default_max_lora_number)])
         input_image_checkbox = args.pop()
         current_tab = args.pop()
         uov_method = args.pop()

modules/core.py

@@ -73,10 +73,7 @@ def refresh_loras(self, loras):
 
         loras_to_load = []
 
-        for enabled, filename, weight in loras:
-            if not enabled:
-                continue
-
+        for filename, weight in loras:
             if filename == 'None':
                 continue
 

modules/default_pipeline.py

@@ -11,7 +11,7 @@
 
 from ldm_patched.modules.model_base import SDXL, SDXLRefiner
 from modules.sample_hijack import clip_separate
-from modules.util import get_file_from_folder_list
+from modules.util import get_file_from_folder_list, get_enabled_loras
 
 
 model_base = core.StableDiffusionModel()
@@ -254,7 +254,7 @@ def refresh_everything(refiner_model_name, base_model_name, loras,
 refresh_everything(
     refiner_model_name=modules.config.default_refiner_model_name,
     base_model_name=modules.config.default_base_model_name,
-    loras=modules.config.default_loras
+    loras=get_enabled_loras(modules.config.default_loras)
 )
 
 

modules/util.py

@@ -360,3 +360,7 @@ def makedirs_with_log(path):
         os.makedirs(path, exist_ok=True)
     except OSError as error:
         print(f'Directory {path} could not be created, reason: {error}')
+
+
+def get_enabled_loras(loras: list) -> list:
+    return [[lora[1], lora[2]] for lora in loras if lora[0]]

presets/lightning.json

@@ -4,22 +4,27 @@
     "default_refiner_switch": 0.5,
     "default_loras": [
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ]