support lora inside prompt selector

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

CHANGELOG.md

@@ -2,9 +2,10 @@
 
 ## Todo
 
+- upstream `torch/rocm` slowdown
 - update `rocm/windows`
 
-## Update for 2026-01-24
+## Update for 2026-01-25
 
 - **Features**
   - **caption** tab support for Booru tagger models, thanks @CalamitousFelicitousness  
@@ -32,6 +33,7 @@
   - add video ui elem_ids, thanks @ryanmeador  
   - use base steps as-is for non sd/sdxl models  
   - ui css fixes for modernui  
+  - support lora inside prompt selector  
 
 ## Update for 2026-01-22
 

modules/lora/lora_load.py

@@ -269,7 +269,7 @@ def network_load(names, te_multipliers=None, unet_multipliers=None, dyn_dims=Non
                 continue
         if net is None:
             failed_to_load_networks.append(name)
-            shared.log.error(f'Network load: type=LoRA name="{name}" detected={network_on_disk.sd_version if network_on_disk is not None else None} failed')
+            shared.log.error(f'Network load: type=LoRA name="{name}" detected={network_on_disk.sd_version if network_on_disk is not None else None} not found')
             continue
         if hasattr(sd_model, 'embedding_db'):
             sd_model.embedding_db.load_diffusers_embedding(None, net.bundle_embeddings)

modules/styles.py

@@ -54,7 +54,11 @@ def select_from_weighted_list(inner: str) -> str:
     unweighted = []
 
     for p in parts:
-        if ':' in p and not p.startswith('(') and not p.endswith(')'):
+        is_list = (p.startswith('(') and p.endswith(')')) or \
+                  (p.startswith('[') and p.endswith(']')) or \
+                  (p.startswith('{') and p.endswith('}')) or \
+                  (p.startswith('<') and p.endswith('>'))
+        if (':' in p) and not is_list:
             name, wstr = p.split(':', 1)
             name = name.strip()
             try:
@@ -69,8 +73,7 @@ def select_from_weighted_list(inner: str) -> str:
     W = sum(weighted.values())
     U = len(unweighted)
 
-    if U == 0:
-        # Only weighted options
+    if U == 0: # only weighted options
         keys = list(weighted.keys())
         if not keys:
             return ''
@@ -79,10 +82,8 @@ def select_from_weighted_list(inner: str) -> str:
         if abs(W - 1.0) > 1e-12:
             for k in weighted:
                 weighted[k] = weighted[k] / W
-    else:
-        # Mix of weighted and unweighted
-        if W >= 1.0:
-            # Weighted probabilities consume whole mass -> normalize them, unweighted get 0
+    else: # mix of weighted and unweighted
+        if W >= 1.0: # weighted probabilities consume whole mass -> normalize them, unweighted get 0
             for k in weighted:
                 weighted[k] = weighted[k] / W
         else: