From 9b18068999ff53dcd73ec349979b94be7c2c3ec6 Mon Sep 17 00:00:00 2001
From: Gnome Ann <>
Date: Wed, 3 Nov 2021 17:53:38 -0400
Subject: [PATCH] Shallow copy story chunks when generating

---
 aiserver.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/aiserver.py b/aiserver.py
index d9e3e2b1..e0a3a466 100644
--- a/aiserver.py
+++ b/aiserver.py
@@ -1223,7 +1223,7 @@ def calcsubmitbudgetheader(txt, **kwargs):
 
     return winfo, mem, anotetxt, found_entries
 
-def calcsubmitbudget(actionlen, winfo, mem, anotetxt):
+def calcsubmitbudget(actionlen, winfo, mem, anotetxt, actions=vars.actions):
     anotetkns    = []  # Placeholder for Author's Note tokens
     lnanote      = 0   # Placeholder for Author's Note length
 
@@ -1262,8 +1262,8 @@ def calcsubmitbudget(actionlen, winfo, mem, anotetxt):
         
         # Get most recent action tokens up to our budget
         n = 0
-        for key in reversed(vars.actions):
-            chunk = vars.actions[key]
+        for key in reversed(actions):
+            chunk = actions[key]
             
             if(budget <= 0):
                 break
@@ -1432,6 +1432,10 @@ def generate(txt, min, max):
         model.kai_scanner_head_length = gen_in.shape[-1]
         model.kai_scanner_excluded_world_info = set()
 
+        actions = vars.actions
+        if(vars.dynamicscan):
+            actions = actions.copy()
+
         with torch.no_grad():
             already_generated = 0
             numseqs = vars.numseqs if not vars.dynamicscan else 1
@@ -1458,7 +1462,7 @@ def generate(txt, min, max):
                 txt = tokenizer.decode(genout[0, -already_generated:])
                 winfo, mem, anotetxt, _found_entries = calcsubmitbudgetheader(txt, force_use_txt=True)
                 found_entries |= _found_entries
-                txt, _, _ = calcsubmitbudget(len(vars.actions), winfo, mem, anotetxt)
+                txt, _, _ = calcsubmitbudget(len(actions), winfo, mem, anotetxt, actions=actions)
                 encoded = tokenizer.encode(txt, return_tensors="pt", truncation=True).long().to(genout.device)
                 genout = torch.cat(
                     (