feat(cycler): add preset strength scale (#865)

py/nodes/lora_cycler.py

@@ -8,6 +8,7 @@ and tracks the cycle progress which persists across workflow save/load.
 
 import logging
 import os
+
 from ..utils.utils import get_lora_info
 
 logger = logging.getLogger(__name__)
@@ -54,6 +55,9 @@ class LoraCyclerLM:
         current_index = cycler_config.get("current_index", 1)  # 1-based
         model_strength = float(cycler_config.get("model_strength", 1.0))
         clip_strength = float(cycler_config.get("clip_strength", 1.0))
+        use_same_clip_strength = cycler_config.get("use_same_clip_strength", True)
+        use_preset_strength = cycler_config.get("use_preset_strength", False)
+        preset_strength_scale = float(cycler_config.get("preset_strength_scale", 1.0))
         sort_by = "filename"
 
         # Include "no lora" option
@@ -131,6 +135,39 @@ class LoraCyclerLM:
             else:
                 # Normalize path separators
                 lora_path = lora_path.replace("/", os.sep)
+
+                if use_preset_strength:
+                    lora_metadata = await lora_service.get_lora_metadata_by_filename(
+                        current_lora["file_name"]
+                    )
+                    if lora_metadata:
+                        recommended_strength = (
+                            lora_service.get_recommended_strength_from_lora_data(
+                                lora_metadata
+                            )
+                        )
+                        if recommended_strength is not None:
+                            model_strength = round(
+                                recommended_strength * preset_strength_scale, 2
+                            )
+
+                        if use_same_clip_strength:
+                            clip_strength = model_strength
+                        else:
+                            recommended_clip_strength = (
+                                lora_service.get_recommended_clip_strength_from_lora_data(
+                                    lora_metadata
+                                )
+                            )
+                            if recommended_clip_strength is not None:
+                                clip_strength = round(
+                                    recommended_clip_strength * preset_strength_scale, 2
+                                )
+                    elif use_same_clip_strength:
+                        clip_strength = model_strength
+                elif use_same_clip_strength:
+                    clip_strength = model_strength
+
                 lora_stack = [(lora_path, model_strength, clip_strength)]
 
         # Calculate next index (wrap to 1 if at end)

py/services/lora_service.py

@@ -1,5 +1,6 @@
-import os
 import logging
+import json
+import os
 from typing import Dict, List, Optional
 
 from .base_model_service import BaseModelService
@@ -278,6 +279,42 @@ class LoraService(BaseModelService):
 
         return None
 
+    @staticmethod
+    def get_recommended_strength_from_lora_data(lora_data: Dict) -> Optional[float]:
+        """Parse usage_tips JSON and extract recommended model strength."""
+        try:
+            usage_tips = lora_data.get("usage_tips", "")
+            if not usage_tips:
+                return None
+            tips_data = json.loads(usage_tips)
+            return tips_data.get("strength")
+        except (json.JSONDecodeError, TypeError, AttributeError):
+            return None
+
+    @staticmethod
+    def get_recommended_clip_strength_from_lora_data(
+        lora_data: Dict,
+    ) -> Optional[float]:
+        """Parse usage_tips JSON and extract recommended clip strength."""
+        try:
+            usage_tips = lora_data.get("usage_tips", "")
+            if not usage_tips:
+                return None
+            tips_data = json.loads(usage_tips)
+            return tips_data.get("clipStrength")
+        except (json.JSONDecodeError, TypeError, AttributeError):
+            return None
+
+    async def get_lora_metadata_by_filename(self, filename: str) -> Optional[Dict]:
+        """Return cached raw metadata for a LoRA matching the given filename."""
+        cache = await self.scanner.get_cached_data(force_refresh=False)
+
+        for lora in cache.raw_data if cache else []:
+            if lora.get("file_name") == filename:
+                return lora
+
+        return None
+
     def find_duplicate_hashes(self) -> Dict:
         """Find LoRAs with duplicate SHA256 hashes"""
         return self.scanner._hash_index.get_duplicate_hashes()
@@ -328,34 +365,10 @@ class LoraService(BaseModelService):
             List of LoRA dicts with randomized strengths
         """
         import random
-        import json
-
         # Use a local Random instance to avoid affecting global random state
         # This ensures each execution with a different seed produces different results
         rng = random.Random(seed)
 
-        def get_recommended_strength(lora_data: Dict) -> Optional[float]:
-            """Parse usage_tips JSON and extract recommended strength"""
-            try:
-                usage_tips = lora_data.get("usage_tips", "")
-                if not usage_tips:
-                    return None
-                tips_data = json.loads(usage_tips)
-                return tips_data.get("strength")
-            except (json.JSONDecodeError, TypeError, AttributeError):
-                return None
-
-        def get_recommended_clip_strength(lora_data: Dict) -> Optional[float]:
-            """Parse usage_tips JSON and extract recommended clip strength"""
-            try:
-                usage_tips = lora_data.get("usage_tips", "")
-                if not usage_tips:
-                    return None
-                tips_data = json.loads(usage_tips)
-                return tips_data.get("clipStrength")
-            except (json.JSONDecodeError, TypeError, AttributeError):
-                return None
-
         if locked_loras is None:
             locked_loras = []
 
@@ -403,7 +416,9 @@ class LoraService(BaseModelService):
         result_loras = []
         for lora in selected:
             if use_recommended_strength:
-                recommended_strength = get_recommended_strength(lora)
+                recommended_strength = self.get_recommended_strength_from_lora_data(
+                    lora
+                )
                 if recommended_strength is not None:
                     scale = rng.uniform(
                         recommended_strength_scale_min, recommended_strength_scale_max
@@ -421,7 +436,9 @@ class LoraService(BaseModelService):
             if use_same_clip_strength:
                 clip_str = model_str
             elif use_recommended_strength:
-                recommended_clip_strength = get_recommended_clip_strength(lora)
+                recommended_clip_strength = (
+                    self.get_recommended_clip_strength_from_lora_data(lora)
+                )
                 if recommended_clip_strength is not None:
                     scale = rng.uniform(
                         recommended_strength_scale_min, recommended_strength_scale_max