fix for incorrect embedding token length calculation (will break seeds that use embeddings, you're welcome!)

add option to input initialization text for embeddings

1 files changed, 4 insertions, 4 deletions

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index fd57e5c5..3fa06242 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -130,7 +130,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             while i < len(tokens):

                 token = tokens[i]

 

-                embedding = self.hijack.embedding_db.find_embedding_at_position(tokens, i)

+                embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)

 

                 if embedding is None:

                     remade_tokens.append(token)

@@ -142,7 +142,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     remade_tokens += [0] * emb_len

                     multipliers += [weight] * emb_len

                     used_custom_terms.append((embedding.name, embedding.checksum()))

-                    i += emb_len

+                    i += embedding_length_in_tokens

 

         if len(remade_tokens) > maxlen - 2:

             vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}

@@ -213,7 +213,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                 while i < len(tokens):

                     token = tokens[i]

 

-                    embedding = self.hijack.embedding_db.find_embedding_at_position(tokens, i)

+                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)

 

                     mult_change = self.token_mults.get(token) if opts.enable_emphasis else None

                     if mult_change is not None:

@@ -229,7 +229,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                         remade_tokens += [0] * emb_len

                         multipliers += [mult] * emb_len

                         used_custom_terms.append((embedding.name, embedding.checksum()))

-                        i += emb_len

+                        i += embedding_length_in_tokens

 

                 if len(remade_tokens) > maxlen - 2:

                     vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}