add infotext entry for emphasis; put emphasis into a separate file, add an option to parse but still ignore emphasis

modules/sd_hijack_clip.py

@@ -3,7 +3,7 @@ from collections import namedtuple
 
 import torch
 
-from modules import prompt_parser, devices, sd_hijack
+from modules import prompt_parser, devices, sd_hijack, sd_emphasis
 from modules.shared import opts
 
 
@@ -88,7 +88,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
         Returns the list and the total number of tokens in the prompt.
         """
 
-        if opts.enable_emphasis:
+        if opts.emphasis != "None":
             parsed = prompt_parser.parse_prompt_attention(line)
         else:
             parsed = [[line, 1.0]]
@@ -249,6 +249,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
                     hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
                 self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
 
+        if any(x for x in texts if "(" in x or "[" in x) and opts.emphasis != "Original":
+            self.hijack.extra_generation_params["Emphasis"] = opts.emphasis
+
         if getattr(self.wrapped, 'return_pooled', False):
             return torch.hstack(zs), zs[0].pooled
         else:
@@ -274,14 +277,14 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
 
         pooled = getattr(z, 'pooled', None)
 
-        # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
-        batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
-        original_mean = z.mean()
-        z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
-        new_mean = z.mean()
+        emphasis = sd_emphasis.get_current_option(opts.emphasis)()
+        emphasis.tokens = remade_batch_tokens
+        emphasis.multipliers = torch.asarray(batch_multipliers).to(devices.device)
+        emphasis.z = z
 
-        if not getattr(opts, "disable_normalize_embeddings", False):
-            z = z * (original_mean / new_mean)
+        emphasis.after_transformers()
+
+        z = emphasis.z
 
         if pooled is not None:
             z.pooled = pooled