feat: only filter lora of selected performance instead of all performance LoRAs

modules/async_worker.py

@@ -462,8 +462,10 @@ def worker():
 
             progressbar(async_task, 2, 'Loading models ...')
 
-            loras, prompt = parse_lora_references_from_prompt(prompt, loras, modules.config.default_max_lora_number)
+            lora_filenames = modules.util.remove_performance_lora(modules.config.lora_filenames, performance_selection)
+            loras, prompt = parse_lora_references_from_prompt(prompt, loras, modules.config.default_max_lora_number, lora_filenames=lora_filenames)
             loras += performance_loras
+
             pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name,
                                         loras=loras, base_model_additional_loras=base_model_additional_loras,
                                         use_synthetic_refiner=use_synthetic_refiner, vae_name=vae_name)

modules/util.py

@@ -398,10 +398,15 @@ def get_enabled_loras(loras: list, remove_none=True) -> list:
 
 
 def parse_lora_references_from_prompt(prompt: str, loras: List[Tuple[AnyStr, float]], loras_limit: int = 5,
-                                      skip_file_check=False, prompt_cleanup=True, deduplicate_loras=True) -> tuple[List[Tuple[AnyStr, float]], str]:
+                                      skip_file_check=False, prompt_cleanup=True, deduplicate_loras=True,
+                                      lora_filenames=None) -> tuple[List[Tuple[AnyStr, float]], str]:
+    if lora_filenames is None:
+        lora_filenames = []
+
     found_loras = []
     prompt_without_loras = ''
     cleaned_prompt = ''
+
     for token in prompt.split(','):
         matches = LORAS_PROMPT_PATTERN.findall(token)
 
@@ -411,7 +416,7 @@ def parse_lora_references_from_prompt(prompt: str, loras: List[Tuple[AnyStr, flo
         for match in matches:
             lora_name = match[1] + '.safetensors'
             if not skip_file_check:
-                lora_name = get_filname_by_stem(match[1], modules.config.lora_filenames_no_special)
+                lora_name = get_filname_by_stem(match[1], lora_filenames)
             if lora_name is not None:
                 found_loras.append((lora_name, float(match[2])))
             token = token.replace(match[0], '')

tests/test_utils.py

@@ -1,5 +1,7 @@
+import os
 import unittest
 
+import modules.flags
 from modules import util
 
 
@@ -77,5 +79,59 @@ class TestUtils(unittest.TestCase):
         for test in test_cases:
             prompt, loras, loras_limit, skip_file_check = test["input"]
             expected = test["output"]
-            actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit=loras_limit, skip_file_check=skip_file_check)
+            actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit=loras_limit,
+                                                            skip_file_check=skip_file_check)
+            self.assertEqual(expected, actual)
+
+    def test_can_parse_tokens_and_strip_performance_lora(self):
+        lora_filenames = [
+            'hey-lora.safetensors',
+            modules.flags.PerformanceLoRA.EXTREME_SPEED.value,
+            modules.flags.PerformanceLoRA.LIGHTNING.value,
+            os.path.join('subfolder', modules.flags.PerformanceLoRA.HYPER_SD.value)
+        ]
+
+        test_cases = [
+            {
+                "input": ("some prompt, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.QUALITY),
+                "output": (
+                    [('hey-lora.safetensors', 0.4)],
+                    'some prompt'
+                ),
+            },
+            {
+                "input": ("some prompt, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.SPEED),
+                "output": (
+                    [('hey-lora.safetensors', 0.4)],
+                    'some prompt'
+                ),
+            },
+            {
+                "input": ("some prompt, <lora:sdxl_lcm_lora:1>, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.EXTREME_SPEED),
+                "output": (
+                    [('hey-lora.safetensors', 0.4)],
+                    'some prompt'
+                ),
+            },
+            {
+                "input": ("some prompt, <lora:sdxl_lightning_4step_lora:1>, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.LIGHTNING),
+                "output": (
+                    [('hey-lora.safetensors', 0.4)],
+                    'some prompt'
+                ),
+            },
+            {
+                "input": ("some prompt, <lora:sdxl_hyper_sd_4step_lora:1>, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.HYPER_SD),
+                "output": (
+                    [('hey-lora.safetensors', 0.4)],
+                    'some prompt'
+                ),
+            }
+        ]
+
+        for test in test_cases:
+            prompt, loras, loras_limit, skip_file_check, performance = test["input"]
+            lora_filenames = modules.util.remove_performance_lora(lora_filenames, performance)
+            expected = test["output"]
+            actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit=loras_limit, lora_filenames=lora_filenames)
             self.assertEqual(expected, actual)