Update tab_batch.py

tab_batch.py

@@ -1,6 +1,6 @@
 import streamlit as st
 import random
-from utils import DEFAULTS, save_json, load_json
+from utils import DEFAULTS, save_json, load_json, render_smart_input
 from history_tree import HistoryTree 
 
 def create_batch_callback(original_filename, current_data, current_dir):
@@ -39,7 +39,6 @@ def render_batch_processor(data, file_path, json_files, current_dir, selected_fi
     # --- 1. RESTORED STATE INDICATOR ---
     if 'restored_indicator' in st.session_state and st.session_state.restored_indicator:
         st.info(f"📍 Editing Restored Version: **{st.session_state.restored_indicator}**")
-    # -----------------------------------
 
     batch_list = data.get("batch_data", [])
     
@@ -189,11 +188,27 @@ def render_batch_processor(data, file_path, json_files, current_dir, selected_fi
                     seq["reference image path"] = st.text_input("Ref Img", value=seq.get("reference image path", ""), key=f"{prefix}_ri2")
                     seq["flf image path"] = st.text_input("FLF Img", value=seq.get("flf image path", ""), key=f"{prefix}_flfi")
 
-            with st.expander("LoRA Settings"):
+            # --- LoRA Settings (SMART INPUTS) ---
+            with st.expander("💊 LoRA Settings"):
+                meta = st.session_state.get("comfy_meta", {})
+                lora_list = meta.get("loras", [])
+                
                 lc1, lc2 = st.columns(2)
-                for li, lk in enumerate(lora_keys):
+                
-                    with (lc1 if li % 2 == 0 else lc2):
+                with lc1:
-                        seq[lk] = st.text_input(lk.title(), value=seq.get(lk, ""), key=f"{prefix}_{lk}")
+                    st.caption("LoRA 1")
+                    seq["lora 1 high"] = render_smart_input("Model", f"{prefix}_l1h", seq.get("lora 1 high", ""), lora_list)
+                    seq["lora 1 low"] = str(st.slider("Strength", 0.0, 2.0, float(seq.get("lora 1 low", 1.0)), 0.05, key=f"{prefix}_l1l"))
+
+                with lc2:
+                    st.caption("LoRA 2")
+                    seq["lora 2 high"] = render_smart_input("Model", f"{prefix}_l2h", seq.get("lora 2 high", ""), lora_list)
+                    seq["lora 2 low"] = str(st.slider("Strength", 0.0, 2.0, float(seq.get("lora 2 low", 1.0)), 0.05, key=f"{prefix}_l2l"))
+                
+                with lc1:
+                    st.caption("LoRA 3")
+                    seq["lora 3 high"] = render_smart_input("Model", f"{prefix}_l3h", seq.get("lora 3 high", ""), lora_list)
+                    seq["lora 3 low"] = str(st.slider("Strength", 0.0, 2.0, float(seq.get("lora 3 low", 1.0)), 0.05, key=f"{prefix}_l3l"))
 
             # --- CUSTOM PARAMETERS ---
             st.markdown("---")
@@ -243,7 +258,6 @@ def render_batch_processor(data, file_path, json_files, current_dir, selected_fi
         if st.button("💾 Save & Snap", use_container_width=True):
             data["batch_data"] = batch_list
             
-            # Commit to Tree
             tree_data = data.get("history_tree", {})
             htree = HistoryTree(tree_data)
             
@@ -255,7 +269,6 @@ def render_batch_processor(data, file_path, json_files, current_dir, selected_fi
             data["history_tree"] = htree.to_dict()
             save_json(file_path, data)
             
-            # CLEAR THE INDICATOR SINCE WE MOVED FORWARD
             if 'restored_indicator' in st.session_state:
                 del st.session_state.restored_indicator