cleanup

Author: damian0815

ldm/modules/embedding_manager.py

@@ -15,21 +15,21 @@
 PROGRESSIVE_SCALE = 2000
 
 
-def get_clip_token_id_for_string(tokenizer: CLIPTokenizer, token_str: str):
+def get_clip_token_id_for_string(tokenizer: CLIPTokenizer, token_str: str) -> int:
     token_id = tokenizer.convert_tokens_to_ids(token_str)
     return token_id
 
-def get_bert_token_for_string(tokenizer, string):
+def get_bert_token_id_for_string(tokenizer, string) -> int:
     token = tokenizer(string)
     # assert torch.count_nonzero(token) == 3, f"String '{string}' maps to more than a single token. Please use another string"
-
     token = token[0, 1]
-
-    return token
+    return token.item()
 
 
-def get_embedding_for_clip_token(embedder, token):
-    return embedder(token.unsqueeze(0))[0, 0]
+def get_embedding_for_clip_token_id(embedder, token_id):
+    if type(token_id) is not torch.Tensor:
+        token_id = torch.tensor(token_id, dtype=torch.int)
+    return embedder(token_id.unsqueeze(0))[0, 0]
 
 @dataclass
 class TextualInversion:
@@ -183,9 +183,6 @@ def overwrite_textual_inversion_embeddings(self, prompt_token_ids: list[int], pr
         return overwritten_prompt_embeddings
 
 
-
-
-
 class EmbeddingManager(nn.Module):
     def __init__(
         self,
@@ -222,8 +219,8 @@ def __init__(
             get_token_id_for_string = partial(
                 get_clip_token_id_for_string, embedder.tokenizer
             )
-            get_embedding_for_tkn = partial(
-                get_embedding_for_clip_token,
+            get_embedding_for_tkn_id = partial(
+                get_embedding_for_clip_token_id,
                 embedder.transformer.text_model.embeddings,
             )
             # per bug report #572
@@ -232,9 +229,9 @@ def __init__(
         else:   # using LDM's BERT encoder
             self.is_clip = False
             get_token_id_for_string = partial(
-                get_bert_token_for_string, embedder.tknz_fn
+                get_bert_token_id_for_string, embedder.tknz_fn
             )
-            get_embedding_for_tkn = embedder.transformer.token_emb
+            get_embedding_for_tkn_id = embedder.transformer.token_emb
             token_dim = 1280
 
         if per_image_tokens:
@@ -248,9 +245,7 @@ def __init__(
                 init_word_token_id = get_token_id_for_string(initializer_words[idx])
 
                 with torch.no_grad():
-                    init_word_embedding = get_embedding_for_tkn(
-                        init_word_token_id.cpu()
-                    )
+                    init_word_embedding = get_embedding_for_tkn_id(init_word_token_id)
 
                 token_params = torch.nn.Parameter(
                     init_word_embedding.unsqueeze(0).repeat(