diff --git a/backend/text_processing/classic_engine.py b/backend/text_processing/classic_engine.py
index bd2cf674..3f82fcd6 100644
--- a/backend/text_processing/classic_engine.py
+++ b/backend/text_processing/classic_engine.py
@@ -6,6 +6,8 @@ from backend.text_processing import parsing, emphasis
 from backend.text_processing.textual_inversion import EmbeddingDatabase
 from backend import memory_management
 
+from modules.shared import opts
+
 
 PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])
 last_extra_generation_params = {}
@@ -67,7 +69,7 @@ class ClassicTextProcessingEngine:
         self.text_encoder = text_encoder
         self.tokenizer = tokenizer
 
-        self.emphasis = emphasis.get_current_option(emphasis_name)()
+        self.emphasis = emphasis.get_current_option(opts.emphasis)()
         self.text_projection = text_projection
         self.minimal_clip_skip = minimal_clip_skip
         self.clip_skip = clip_skip
@@ -146,7 +148,7 @@ class ClassicTextProcessingEngine:
         return z
 
     def tokenize_line(self, line):
-        parsed = parsing.parse_prompt_attention(line)
+        parsed = parsing.parse_prompt_attention(line, self.emphasis.name)
 
         tokenized = self.tokenize([text for text, _ in parsed])
 
@@ -248,6 +250,8 @@ class ClassicTextProcessingEngine:
         return batch_chunks, token_count
 
     def __call__(self, texts):
+        self.emphasis = emphasis.get_current_option(opts.emphasis)()
+
         batch_chunks, token_count = self.process_texts(texts)
 
         used_embeddings = {}
diff --git a/backend/text_processing/parsing.py b/backend/text_processing/parsing.py
index 07dcdc16..cdc49122 100644
--- a/backend/text_processing/parsing.py
+++ b/backend/text_processing/parsing.py
@@ -20,7 +20,7 @@ re_attention = re.compile(r"""
 re_break = re.compile(r"\s*\bBREAK\b\s*", re.S)
 
 
-def parse_prompt_attention(text):
+def parse_prompt_attention(text, emphasis):
     res = []
     round_brackets = []
     square_brackets = []
@@ -32,44 +32,48 @@ def parse_prompt_attention(text):
         for p in range(start_position, len(res)):
             res[p][1] *= multiplier
 
-    for m in re_attention.finditer(text):
-        text = m.group(0)
-        weight = m.group(1)
+    if emphasis == "None":
+        # interpret literally
+        res = [[text, 1.0]]
+    else:
+        for m in re_attention.finditer(text):
+            text = m.group(0)
+            weight = m.group(1)
 
-        if text.startswith('\\'):
-            res.append([text[1:], 1.0])
-        elif text == '(':
-            round_brackets.append(len(res))
-        elif text == '[':
-            square_brackets.append(len(res))
-        elif weight is not None and round_brackets:
-            multiply_range(round_brackets.pop(), float(weight))
-        elif text == ')' and round_brackets:
-            multiply_range(round_brackets.pop(), round_bracket_multiplier)
-        elif text == ']' and square_brackets:
-            multiply_range(square_brackets.pop(), square_bracket_multiplier)
-        else:
-            parts = re.split(re_break, text)
-            for i, part in enumerate(parts):
-                if i > 0:
-                    res.append(["BREAK", -1])
-                res.append([part, 1.0])
+            if text.startswith('\\'):
+                res.append([text[1:], 1.0])
+            elif text == '(':
+                round_brackets.append(len(res))
+            elif text == '[':
+                square_brackets.append(len(res))
+            elif weight is not None and round_brackets:
+                multiply_range(round_brackets.pop(), float(weight))
+            elif text == ')' and round_brackets:
+                multiply_range(round_brackets.pop(), round_bracket_multiplier)
+            elif text == ']' and square_brackets:
+                multiply_range(square_brackets.pop(), square_bracket_multiplier)
+            else:
+                parts = re.split(re_break, text)
+                for i, part in enumerate(parts):
+                    if i > 0:
+                        res.append(["BREAK", -1])
+                    res.append([part, 1.0])
 
-    for pos in round_brackets:
-        multiply_range(pos, round_bracket_multiplier)
+        for pos in round_brackets:
+            multiply_range(pos, round_bracket_multiplier)
 
-    for pos in square_brackets:
-        multiply_range(pos, square_bracket_multiplier)
+        for pos in square_brackets:
+            multiply_range(pos, square_bracket_multiplier)
 
-    if len(res) == 0:
-        res = [["", 1.0]]
+        if len(res) == 0:
+            res = [["", 1.0]]
 
-    i = 0
-    while i + 1 < len(res):
-        if res[i][1] == res[i + 1][1]:
-            res[i][0] += res[i + 1][0]
-            res.pop(i + 1)
-        else:
-            i += 1
+        i = 0
+        while i + 1 < len(res):
+            if res[i][1] == res[i + 1][1]:
+                res[i][0] += res[i + 1][0]
+                res.pop(i + 1)
+            else:
+                i += 1
 
     return res
diff --git a/backend/text_processing/t5_engine.py b/backend/text_processing/t5_engine.py
index a6b3dc47..e00ccccf 100644
--- a/backend/text_processing/t5_engine.py
+++ b/backend/text_processing/t5_engine.py
@@ -4,6 +4,8 @@ from collections import namedtuple
 from backend.text_processing import parsing, emphasis
 from backend import memory_management
 
+from modules.shared import opts
+
 
 PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])
 
@@ -21,7 +23,7 @@ class T5TextProcessingEngine:
         self.text_encoder = text_encoder.transformer
         self.tokenizer = tokenizer
 
-        self.emphasis = emphasis.get_current_option(emphasis_name)()
+        self.emphasis = emphasis.get_current_option(opts.emphasis)()
         self.min_length = min_length
         self.id_end = 1
         self.id_pad = 0
@@ -64,7 +66,7 @@ class T5TextProcessingEngine:
         return z
 
     def tokenize_line(self, line):
-        parsed = parsing.parse_prompt_attention(line)
+        parsed = parsing.parse_prompt_attention(line, self.emphasis.name)
 
         tokenized = self.tokenize([text for text, _ in parsed])
 
@@ -111,6 +113,8 @@ class T5TextProcessingEngine:
         zs = []
         cache = {}
 
+        self.emphasis = emphasis.get_current_option(opts.emphasis)()
+
         for line in texts:
             if line in cache:
                 line_z_values = cache[line]