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

1 files changed, 12 insertions, 9 deletions

diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
index 89634fbf..98350ac4 100644
--- a/modules/sd_hijack_clip.py
+++ b/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

+

+        emphasis.after_transformers()

 

-        if not getattr(opts, "disable_normalize_embeddings", False):

-            z = z * (original_mean / new_mean)

+        z = emphasis.z

 

         if pooled is not None:

             z.pooled = pooled