feat: optimize performance lora filtering in metadata (#3048)

* feat: add remove_performance_lora method

* feat: use class PerformanceLoRA instead of strings in config

* refactor: cleanup flags, use __member__ to check if enums contains key

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

* fix: disable intermediate results for all restricted performances

too fast for Gradio, which becomes a bottleneck

* refactor: rename parse_json to to_json, rename parse_string to to_string

* feat: use speed steps as default instead of hardcoded 30

* feat: add method to_steps to Performance

* refactor: remove method ordinal_suffix, not needed anymore

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

both metadata and history log

* feat: do not filter LoRAs in metadata parser but rather in metadata load action

modules/flags.py

@@ -48,7 +48,8 @@ SAMPLERS = KSAMPLER | SAMPLER_EXTRA
 
 KSAMPLER_NAMES = list(KSAMPLER.keys())
 
-SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo", "align_your_steps", "tcd"]
+SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo",
+                   "align_your_steps", "tcd"]
 SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys())
 
 sampler_list = SAMPLER_NAMES
@@ -91,6 +92,7 @@ sdxl_aspect_ratios = [
     '1664*576', '1728*576'
 ]
 
+
 class MetadataScheme(Enum):
     FOOOCUS = 'fooocus'
     A1111 = 'a1111'
@@ -115,6 +117,14 @@ class OutputFormat(Enum):
         return list(map(lambda c: c.value, cls))
 
 
+class PerformanceLoRA(Enum):
+    QUALITY = None
+    SPEED = None
+    EXTREME_SPEED = 'sdxl_lcm_lora.safetensors'
+    LIGHTNING = 'sdxl_lightning_4step_lora.safetensors'
+    HYPER_SD = 'sdxl_hyper_sd_4step_lora.safetensors'
+
+
 class Steps(IntEnum):
     QUALITY = 60
     SPEED = 30
@@ -142,6 +152,10 @@ class Performance(Enum):
     def list(cls) -> list:
         return list(map(lambda c: c.value, cls))
 
+    @classmethod
+    def by_steps(cls, steps: int | str):
+        return cls[Steps(int(steps)).name]
+
     @classmethod
     def has_restricted_features(cls, x) -> bool:
         if isinstance(x, Performance):
@@ -149,7 +163,10 @@ class Performance(Enum):
         return x in [cls.EXTREME_SPEED.value, cls.LIGHTNING.value, cls.HYPER_SD.value]
 
     def steps(self) -> int | None:
-        return Steps[self.name].value if Steps[self.name] else None
+        return Steps[self.name].value if self.name in Steps.__members__ else None
 
     def steps_uov(self) -> int | None:
-        return StepsUOV[self.name].value if Steps[self.name] else None
+        return StepsUOV[self.name].value if self.name in StepsUOV.__members__ else None
+
+    def lora_filename(self) -> str | None:
+        return PerformanceLoRA[self.name].value if self.name in PerformanceLoRA.__members__ else None