diff --git a/aiserver.py b/aiserver.py
index a4f12997..584f81ae 100644
--- a/aiserver.py
+++ b/aiserver.py
@@ -119,6 +119,7 @@ class vars:
     lua_running = False  # Whether or not Lua is running (i.e. wasn't stopped due to an error)
     lua_edited  = set()  # Set of chunk numbers that were edited from a Lua generation modifier
     lua_deleted = set()  # Set of chunk numbers that were deleted from a Lua generation modifier
+    generated_tkns = 0   # If using a backend that supports Lua generation modifiers, how many tokens have already been generated, otherwise 0
     spfilename  = ""     # Filename of soft prompt to load, or an empty string if not using a soft prompt
     userscripts = []     # List of userscripts to load
     last_userscripts = []  # List of previous userscript filenames from the previous time userscripts were send via usstatitems
@@ -796,7 +797,10 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
                 scores: torch.FloatTensor,
                 **kwargs,
             ) -> bool:
-                if(vars.lua_koboldbridge.generated_cols >= vars.genamt):
+                vars.generated_tkns += 1
+                if(vars.lua_koboldbridge.generated_cols and vars.generated_tkns != vars.lua_koboldbridge.generated_cols):
+                    raise RuntimeError(f"Inconsistency detected between KoboldAI Python and Lua backends ({vars.generated_tkns} != {vars.lua_koboldbridge.generated_cols})")
+                if(vars.generated_tkns >= vars.genamt):
                     self.regeneration_required = False
                     self.halt = False
                     return True
@@ -808,7 +812,7 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
                 vars.lua_koboldbridge.regeneration_required = False
 
                 for i in range(vars.numseqs):
-                    vars.lua_koboldbridge.generated[i+1][vars.lua_koboldbridge.generated_cols] = input_ids[i, -1].item()
+                    vars.lua_koboldbridge.generated[i+1][vars.generated_tkns] = int(input_ids[i, -1].item())
 
                 if(not vars.dynamicscan):
                     return self.regeneration_required or self.halt
@@ -1145,7 +1149,7 @@ def lua_compute_context(submission, entries, folders):
             i += 1
     winfo, mem, anotetxt, _ = calcsubmitbudgetheader(submission, allowed_entries=allowed_entries, allowed_folders=allowed_folders, force_use_txt=True)
     txt, _, _ = calcsubmitbudget(len(actions), winfo, mem, anotetxt, actions)
-    return txt
+    return tokenizer.decode(txt)
 
 #==================================================================#
 #  Get property of a world info entry given its UID and property name
@@ -2241,38 +2245,53 @@ def calcsubmitbudgetheader(txt, **kwargs):
 
     return winfo, mem, anotetxt, found_entries
 
-def calcsubmitbudget(actionlen, winfo, mem, anotetxt, actions):
+def calcsubmitbudget(actionlen, winfo, mem, anotetxt, actions, submission=None, budget_deduction=0):
     forceanote   = False # In case we don't have enough actions to hit A.N. depth
     anoteadded   = False # In case our budget runs out before we hit A.N. depth
     anotetkns    = []  # Placeholder for Author's Note tokens
     lnanote      = 0   # Placeholder for Author's Note length
 
-    # Calculate token budget
-    prompttkns = tokenizer.encode(vars.comregex_ai.sub('', vars.prompt), max_length=1+int(vars.max_length), truncation=True)
-    lnprompt   = len(prompttkns)
-    
-    memtokens = tokenizer.encode(mem, max_length=1+int(vars.max_length), truncation=True)
-    lnmem     = len(memtokens)
-    
-    witokens  = tokenizer.encode(winfo, max_length=1+int(vars.max_length), truncation=True)
-    lnwi      = len(witokens)
-    
-    if(anotetxt != ""):
-        anotetkns = tokenizer.encode(anotetxt, max_length=1+int(vars.max_length), truncation=True)
-        lnanote   = len(anotetkns)
-    
     lnsp = vars.sp.shape[0] if vars.sp is not None else 0
-    
+
+    # Calculate token budget
+    prompttkns = tokenizer.encode(vars.comregex_ai.sub('', vars.prompt), max_length=int(2e9), truncation=True)
+    lnprompt   = len(prompttkns)
+
+    memtokens = tokenizer.encode(mem, max_length=int(2e9), truncation=True)
+    lnmem     = len(memtokens)
+    if(lnmem > vars.max_length - lnsp - vars.genamt - budget_deduction):
+        raise OverflowError("The memory in your story is too long. Please either write a shorter memory text or increase the Max Tokens setting. If you are using a soft prompt, additionally consider using a smaller soft prompt.")
+
+    witokens  = tokenizer.encode(winfo, max_length=int(2e9), truncation=True)
+    lnwi      = len(witokens)
+    if(lnmem + lnwi > vars.max_length - lnsp - vars.genamt - budget_deduction):
+        raise OverflowError("The current active world info keys take up too many tokens. Please either write shorter world info, decrease World Info Depth or increase the Max Tokens setting. If you are using a soft prompt, additionally consider using a smaller soft prompt.")
+
+    if(anotetxt != ""):
+        anotetkns = tokenizer.encode(anotetxt, max_length=int(2e9), truncation=True)
+        lnanote   = len(anotetkns)
+        if(lnmem + lnwi + lnanote > vars.max_length - lnsp - vars.genamt - budget_deduction):
+            raise OverflowError("The author's note in your story is too long. Please either write a shorter author's note or increase the Max Tokens setting. If you are using a soft prompt, additionally consider using a smaller soft prompt.")
+
     if(vars.useprompt):
-        budget = vars.max_length - lnsp - lnprompt - lnmem - lnanote - lnwi - vars.genamt
+        budget = vars.max_length - lnsp - lnprompt - lnmem - lnanote - lnwi - vars.genamt - budget_deduction
     else:
-        budget = vars.max_length - lnsp - lnmem - lnanote - lnwi - vars.genamt
+        budget = vars.max_length - lnsp - lnmem - lnanote - lnwi - vars.genamt - budget_deduction
+
+    lnsubmission = len(tokenizer.encode(vars.comregex_ai.sub('', submission), max_length=int(2e9), truncation=True)) if submission is not None else 0
+    maybe_lnprompt = lnprompt if vars.useprompt and actionlen > 0 else 0
+
+    if(lnmem + lnwi + lnanote + maybe_lnprompt + lnsubmission > vars.max_length - lnsp - vars.genamt - budget_deduction):
+        raise OverflowError("Your submission is too long. Please either write a shorter submission or increase the Max Tokens setting. If you are using a soft prompt, additionally consider using a smaller soft prompt. If you are using the Always Add Prompt setting, turning it off may help.")
+
+    assert budget >= 0
 
     if(actionlen == 0):
         # First/Prompt action
-        subtxt = vars.memory + winfo + anotetxt + vars.comregex_ai.sub('', vars.prompt)
-        lnsub  = lnsp + lnmem + lnwi + lnprompt + lnanote
-        return subtxt, lnsub+1, lnsub+vars.genamt
+        tokens = memtokens + witokens + anotetkns + prompttkns
+        assert len(tokens) <= vars.max_length - lnsp - vars.genamt - budget_deduction
+        ln = len(tokens) + lnsp
+        return tokens, ln+1, ln+vars.genamt
     else:
         tokens     = []
         
@@ -2285,9 +2304,10 @@ def calcsubmitbudget(actionlen, winfo, mem, anotetxt, actions):
         for key in reversed(actions):
             chunk = vars.comregex_ai.sub('', actions[key])
             
+            assert budget >= 0
             if(budget <= 0):
                 break
-            acttkns = tokenizer.encode(chunk, max_length=int(vars.max_length), truncation=True)
+            acttkns = tokenizer.encode(chunk, max_length=int(2e9), truncation=True)
             tknlen = len(acttkns)
             if(tknlen < budget):
                 tokens = acttkns + tokens
@@ -2312,7 +2332,7 @@ def calcsubmitbudget(actionlen, winfo, mem, anotetxt, actions):
                 prompttkns = prompttkns[-budget:]
             else:
                 prompttkns = []
-                
+
         # Did we get to add the A.N.? If not, do it here
         if(anotetxt != ""):
             if((not anoteadded) or forceanote):
@@ -2322,10 +2342,11 @@ def calcsubmitbudget(actionlen, winfo, mem, anotetxt, actions):
         else:
             # Prepend Memory, WI, and Prompt before action tokens
             tokens = memtokens + witokens + prompttkns + tokens
-        
+
         # Send completed bundle to generator
+        assert len(tokens) <= vars.max_length - lnsp - vars.genamt - budget_deduction
         ln = len(tokens) + lnsp
-        return tokenizer.decode(tokens), ln+1, ln+vars.genamt
+        return tokens, ln+1, ln+vars.genamt
 
 #==================================================================#
 # Take submitted text and build the text to be given to generator
@@ -2340,23 +2361,23 @@ def calcsubmit(txt):
 
     # For all transformers models
     if(vars.model != "InferKit"):
-        subtxt, min, max = calcsubmitbudget(actionlen, winfo, mem, anotetxt, vars.actions)
+        subtxt, min, max = calcsubmitbudget(actionlen, winfo, mem, anotetxt, vars.actions, submission=txt)
         if(actionlen == 0):
             if(not vars.model in ["Colab", "OAI", "TPUMeshTransformerGPTJ"]):
                 generate(subtxt, min, max, found_entries=found_entries)
             elif(vars.model == "Colab"):
-                sendtocolab(subtxt, min, max)
+                sendtocolab(tokenizer.decode(subtxt), min, max)
             elif(vars.model == "OAI"):
-                oairequest(subtxt, min, max)
+                oairequest(tokenizer.decode(subtxt), min, max)
             elif(vars.model == "TPUMeshTransformerGPTJ"):
                 tpumtjgenerate(subtxt, min, max, found_entries=found_entries)
         else:
             if(not vars.model in ["Colab", "OAI", "TPUMeshTransformerGPTJ"]):
                 generate(subtxt, min, max, found_entries=found_entries)
             elif(vars.model == "Colab"):
-                sendtocolab(subtxt, min, max)
+                sendtocolab(tokenizer.decode(subtxt), min, max)
             elif(vars.model == "OAI"):
-                oairequest(subtxt, min, max)
+                oairequest(tokenizer.decode(subtxt), min, max)
             elif(vars.model == "TPUMeshTransformerGPTJ"):
                 tpumtjgenerate(subtxt, min, max, found_entries=found_entries)
                     
@@ -2421,13 +2442,14 @@ def calcsubmit(txt):
 #==================================================================#
 
 def _generate(txt, minimum, maximum, found_entries):
-    gen_in = tokenizer.encode(txt, return_tensors="pt", max_length=int(vars.max_length), truncation=True).long()
+    gen_in = torch.tensor(txt, dtype=torch.long)[None]
     if(vars.sp is not None):
         soft_tokens = torch.arange(
             model.config.vocab_size,
             model.config.vocab_size + vars.sp.shape[0],
         )
         gen_in = torch.cat((soft_tokens[None], gen_in), dim=-1)
+    assert gen_in.shape[-1] + vars.genamt <= vars.max_length
 
     if(vars.hascuda and vars.usegpu):
         gen_in = gen_in.to(vars.gpu_device)
@@ -2459,11 +2481,14 @@ def _generate(txt, minimum, maximum, found_entries):
                 num_return_sequences=numseqs
                 )
             already_generated += len(genout[0]) - len(gen_in[0])
+            assert already_generated <= vars.genamt
             if(model.kai_scanner.halt or not model.kai_scanner.regeneration_required):
                 break
             assert genout.ndim >= 2
             assert genout.shape[0] == vars.numseqs
-            if(already_generated != vars.lua_koboldbridge.generated_cols):
+            if(vars.lua_koboldbridge.generated_cols and vars.generated_tkns != vars.lua_koboldbridge.generated_cols):
+                raise RuntimeError("Inconsistency detected between KoboldAI Python and Lua backends")
+            if(already_generated != vars.generated_tkns):
                 raise RuntimeError("WI scanning error")
             for r in range(vars.numseqs):
                 for c in range(already_generated):
@@ -2474,8 +2499,8 @@ def _generate(txt, minimum, maximum, found_entries):
                 txt = tokenizer.decode(genout[i, -already_generated:])
                 winfo, mem, anotetxt, _found_entries = calcsubmitbudgetheader(txt, force_use_txt=True)
                 found_entries[i].update(_found_entries)
-                txt, _, _ = calcsubmitbudget(len(vars._actions), winfo, mem, anotetxt, vars._actions)
-                encoded.append(tokenizer.encode(txt, return_tensors="pt", max_length=int(vars.max_length), truncation=True)[0].long().to(genout.device))
+                txt, _, _ = calcsubmitbudget(len(vars._actions), winfo, mem, anotetxt, vars._actions, submission=txt)
+                encoded.append(torch.tensor(txt, dtype=torch.long, device=genout.device))
             max_length = len(max(encoded, key=len))
             encoded = torch.stack(tuple(torch.nn.functional.pad(e, (max_length - len(e), 0), value=model.config.pad_token_id or model.config.eos_token_id) for e in encoded))
             genout = torch.cat(
@@ -2492,6 +2517,7 @@ def _generate(txt, minimum, maximum, found_entries):
                     device=genout.device,
                 )
                 genout = torch.cat((soft_tokens.tile(vars.numseqs, 1), genout), dim=-1)
+            assert genout.shape[-1] + vars.genamt - already_generated <= vars.max_length
             diff = genout.shape[-1] - gen_in.shape[-1]
             minimum += diff
             maximum += diff
@@ -2503,14 +2529,16 @@ def _generate(txt, minimum, maximum, found_entries):
     
 
 def generate(txt, minimum, maximum, found_entries=None):    
+    vars.generated_tkns = 0
+
     if(found_entries is None):
         found_entries = set()
     found_entries = tuple(found_entries.copy() for _ in range(vars.numseqs))
 
-    print("{0}Min:{1}, Max:{2}, Txt:{3}{4}".format(colors.YELLOW, minimum, maximum, txt, colors.END))
+    print("{0}Min:{1}, Max:{2}, Txt:{3}{4}".format(colors.YELLOW, minimum, maximum, tokenizer.decode(txt), colors.END))
 
     # Store context in memory to use it for comparison with generated content
-    vars.lastctx = txt
+    vars.lastctx = tokenizer.decode(txt)
 
     # Clear CUDA cache if using GPU
     if(vars.hascuda and (vars.usegpu or vars.breakmodel)):
@@ -2536,7 +2564,7 @@ def generate(txt, minimum, maximum, found_entries=None):
         return
 
     for i in range(vars.numseqs):
-        vars.lua_koboldbridge.generated[i+1][vars.lua_koboldbridge.generated_cols] = genout[i, -1].item()
+        vars.lua_koboldbridge.generated[i+1][vars.generated_tkns] = int(genout[i, -1].item())
         vars.lua_koboldbridge.outputs[i+1] = tokenizer.decode(genout[i, -already_generated:])
 
     execute_outmod()
@@ -2707,7 +2735,7 @@ def tpumtjgenerate(txt, minimum, maximum, found_entries=None):
         found_entries = set()
     found_entries = tuple(found_entries.copy() for _ in range(vars.numseqs))
 
-    print("{0}Min:{1}, Max:{2}, Txt:{3}{4}".format(colors.YELLOW, minimum, maximum, txt, colors.END))
+    print("{0}Min:{1}, Max:{2}, Txt:{3}{4}".format(colors.YELLOW, minimum, maximum, tokenizer.decode(txt), colors.END))
 
     # Submit input text to generator
     try:
@@ -2737,7 +2765,7 @@ def tpumtjgenerate(txt, minimum, maximum, found_entries=None):
 
         genout = tpool.execute(
             tpu_mtj_backend.infer,
-            txt,
+            np.uint32(txt),
             gen_len = maximum-minimum+1,
             temp=vars.temp,
             top_p=vars.top_p,
@@ -2804,8 +2832,8 @@ def getnewcontent(txt):
         return txt
     
     # Tokenize the last context and the generated content
-    ctxtokens = tokenizer.encode(vars.lastctx, max_length=1+int(vars.max_length), truncation=True)
-    txttokens = tokenizer.encode(txt, max_length=1+int(vars.max_length), truncation=True)
+    ctxtokens = tokenizer.encode(vars.lastctx, max_length=int(2e9), truncation=True)
+    txttokens = tokenizer.encode(txt, max_length=int(2e9), truncation=True)
     dif       = (len(txttokens) - len(ctxtokens)) * -1
     
     # Remove the context from the returned text
diff --git a/tpu_mtj_backend.py b/tpu_mtj_backend.py
index ffb7b3e3..845edd30 100644
--- a/tpu_mtj_backend.py
+++ b/tpu_mtj_backend.py
@@ -268,7 +268,7 @@ class PenalizingCausalTransformer(CausalTransformer):
 
 
 def infer(
-    context: str,
+    context: np.array,
     top_p=0.9,
     temp=0.5,
     top_k=0,
@@ -281,7 +281,7 @@ def infer(
 ) -> List[str]:
     maps.thread_resources.env = thread_resources_env
     total_batch = 1
-    tokens = np.uint32(tokenizer.encode(context, max_length=params["seq"] - (soft_tokens.shape[0] if soft_tokens is not None else 0), truncation=True))
+    tokens = context
     if(soft_tokens is not None):
         tokens = np.uint32(np.concatenate((soft_tokens, tokens)))
     provided_ctx = tokens.shape[0]
diff --git a/utils.py b/utils.py
index 95480c96..31184ed7 100644
--- a/utils.py
+++ b/utils.py
@@ -73,13 +73,15 @@ def addsentencespacing(txt, vars):
     # Get last character of last action
     if(len(vars.actions) > 0):
         if(len(vars.actions[vars.actions.get_last_key()]) > 0):
-            lastchar = vars.actions[vars.actions.get_last_key()][-1]
+            action = vars.actions[vars.actions.get_last_key()]
+            lastchar = action[-1] if len(action) else ""
         else:
             # Last action is blank, this should never happen, but
             # since it did let's bail out.
             return txt
     else:
-        lastchar = vars.prompt[-1]
+        action = vars.prompt
+        lastchar = action[-1] if len(action) else ""
     if(lastchar == "." or lastchar == "!" or lastchar == "?" or lastchar == "," or lastchar == ";" or lastchar == ":"):
         txt = " " + txt
     return txt
@@ -88,13 +90,15 @@ def singlelineprocessing(txt, vars):
     txt = vars.regex_sl.sub('', txt)
     if(len(vars.actions) > 0):
         if(len(vars.actions[vars.actions.get_last_key()]) > 0):
-            lastchar = vars.actions[vars.actions.get_last_key()][-1]
+            action = vars.actions[vars.actions.get_last_key()]
+            lastchar = action[-1] if len(action) else ""
         else:
             # Last action is blank, this should never happen, but
             # since it did let's bail out.
             return txt
     else:
-        lastchar = vars.prompt[-1]
+        action = vars.prompt
+        lastchar = action[-1] if len(action) else ""
     if(lastchar != "\n"):
         txt = txt + "\n"
     return txt