handle multiple tokens and embeddings in single file

invokeai/backend/stable_diffusion/textual_inversion_manager.py

@@ -1,7 +1,7 @@
 import traceback
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Optional, Union
+from typing import Optional, Union, List
 
 import safetensors.torch
 import torch
@@ -12,6 +12,16 @@ from transformers import CLIPTextModel, CLIPTokenizer
 
 from .concepts_lib import HuggingFaceConceptsLibrary
 
+@dataclass
+class EmbeddingInfo:
+    name: str
+    embedding: torch.Tensor
+    num_vectors_per_token: int
+    token_dim: int 
+    trained_steps: int = None
+    trained_model_name: str = None
+    trained_model_checksum: str = None
+
 @dataclass
 class TextualInversion:
     trigger_string: str
@@ -72,23 +82,17 @@ class TextualInversionManager(BaseTextualInversionManager):
         if str(ckpt_path).endswith(".DS_Store"):
             return
 
-        embedding_info = self._parse_embedding(str(ckpt_path))
+        embedding_list = self._parse_embedding(str(ckpt_path))
-
+        for embedding_info in embedding_list:
-        if embedding_info is None:
+            if (self.text_encoder.get_input_embeddings().weight.data[0].shape[0] != embedding_info.token_dim):
-            # We've already put out an error message about the bad embedding in _parse_embedding, so just return.
-            return
-        elif (
-            self.text_encoder.get_input_embeddings().weight.data[0].shape[0]
-            != embedding_info["token_dim"]
-        ):
                 print(
-                f"   ** Notice: {ckpt_path.parents[0].name}/{ckpt_path.name} was trained on a model with an incompatible token dimension: {self.text_encoder.get_input_embeddings().weight.data[0].shape[0]} vs {embedding_info['token_dim']}."
+                    f"   ** Notice: {ckpt_path.parents[0].name}/{ckpt_path.name} was trained on a model with an incompatible token dimension: {self.text_encoder.get_input_embeddings().weight.data[0].shape[0]} vs {embedding_info.token_dim}."
                 )
-            return
+                continue
 
             # Resolve the situation in which an earlier embedding has claimed the same
             # trigger string. We replace the trigger with '<source_file>', as we used to.
-        trigger_str = embedding_info["name"]
+            trigger_str = embedding_info.name
             sourcefile = (
                 f"{ckpt_path.parent.name}/{ckpt_path.name}"
                 if ckpt_path.name == "learned_embeds.bin"
@@ -109,7 +113,7 @@ class TextualInversionManager(BaseTextualInversionManager):
             try:
                 self._add_textual_inversion(
                     trigger_str,
-                embedding_info["embedding"],
+                    embedding_info.embedding,
                     defer_injecting_tokens=defer_injecting_tokens,
                 )
                 # remember which source file claims this trigger
@@ -295,23 +299,24 @@ class TextualInversionManager(BaseTextualInversionManager):
 
         return token_id
 
-    def _parse_embedding(self, embedding_file: str)->dict:
+
+    def _parse_embedding(self, embedding_file: str)->List[EmbeddingInfo]:
         suffix = Path(embedding_file).suffix
         try:
             if suffix in [".pt",".ckpt",".bin"]:
                 scan_result = scan_file_path(embedding_file)
-                if scan_result.infected_files == 1:
+                if scan_result.infected_files > 0:
                     print(
                         f"   ** Security Issues Found in Model: {scan_result.issues_count}"
                     )
                     print("   ** For your safety, InvokeAI will not load this embed.")
-                    return
+                    return list()
                 ckpt = torch.load(embedding_file,map_location="cpu")
             else:
                 ckpt = safetensors.torch.load_file(embedding_file)
         except Exception as e:
             print(f"   ** Notice: unrecognized embedding file format: {embedding_file}: {e}")
-            return None
+            return list()
         
         # try to figure out what kind of embedding file it is and parse accordingly
         keys = list(ckpt.keys())
@@ -327,79 +332,78 @@ class TextualInversionManager(BaseTextualInversionManager):
         else:
             return self._parse_embedding_v4(ckpt, embedding_file)     # usually a '.bin' file
 
-    def _parse_embedding_v1(self, embedding_ckpt: dict, file_path: str):
+    def _parse_embedding_v1(self, embedding_ckpt: dict, file_path: str)->List[EmbeddingInfo]:
         basename = Path(file_path).stem
         print(f'   | Loading v1 embedding file: {basename}')
 
-        embedding_info = {}
+        embeddings = list()
-        embedding_info["name"] = embedding_ckpt["name"]
+        token_counter = -1
-                
+        for token,embedding in embedding_ckpt["string_to_param"].items():
-        # Check num of embeddings and warn user only the first will be used
+            if token_counter < 0:
-        embedding_info["num_of_embeddings"] = len(
+                trigger = embedding_ckpt["name"]
-            embedding_ckpt["string_to_token"]
+            elif token_counter == 0:
+                trigger = f'<basename>'
+            else:
+                trigger = f'<{basename}-{int(token_counter:=token_counter)}>'
+            token_counter += 1
+            embedding_info = EmbeddingInfo(
+                name = trigger,
+                embedding = embedding,
+                num_vectors_per_token = embedding.size()[0],
+                token_dim = embedding.size()[1],
+                trained_steps = embedding_ckpt["step"],
+                trained_model_name = embedding_ckpt["sd_checkpoint_name"],
+                trained_model_checksum = embedding_ckpt["sd_checkpoint"]
             )
-        if embedding_info["num_of_embeddings"] > 1:
+            embeddings.append(embedding_info)
-            print("   | More than 1 embedding found. Will use the first one")
+        return embeddings
-        embedding = list(embedding_ckpt["string_to_param"].values())[0]
-        embedding_info["embedding"] = embedding
-        embedding_info["num_vectors_per_token"] = embedding.size()[0]
-        embedding_info["token_dim"] = embedding.size()[1]
-        embedding_info["trained_steps"] = embedding_ckpt["step"]
-        embedding_info["trained_model_name"] = embedding_ckpt[
-            "sd_checkpoint_name"
-        ]
-        embedding_info["trained_model_checksum"] = embedding_ckpt[
-            "sd_checkpoint"
-        ]
-        return embedding_info
 
     def _parse_embedding_v2 (
         self, embedding_ckpt: dict, file_path: str
-    ) -> dict:
+    ) -> List[EmbeddingInfo]:
         """
         This handles embedding .pt file variant #2.
         """
         basename = Path(file_path).stem
         print(f'   | Loading v2 embedding file: {basename}')
-        embedding_info = {}
+        embeddings = list()
+        
         if isinstance(
             list(embedding_ckpt["string_to_token"].values())[0], torch.Tensor
         ):
-            for token in list(embedding_ckpt["string_to_token"].keys()):
+            token_counter = 0
-                embedding_info["name"] = (
+            for token,embedding in embedding_ckpt["string_to_param"].items():
-                    token
+                trigger = token if token != '*' \
-                    if token != "*"
+                    else f'<{basename}>' if token_counter == 0 \
-                    else f"<{basename}>"
+                         else f'<{basename}-{int(token_counter:=token_counter+1)}>'
+                embedding_info = EmbeddingInfo(
+                    name = trigger,
+                    embedding = embedding,
+                    num_vectors_per_token = embedding.size()[0],
+                    token_dim = embedding.size()[1],
                 )
-                embedding_info["embedding"] = embedding_ckpt[
+                embeddings.append(embedding_info)
-                    "string_to_param"
-                ].state_dict()[token]
-                embedding_info["num_vectors_per_token"] = embedding_info[
-                    "embedding"
-                ].shape[0]
-                embedding_info["token_dim"] = embedding_info["embedding"].size()[1]
         else:
             print(f"   ** {basename}: Unrecognized embedding format")
-            embedding_info = None
 
-        return embedding_info
+        return embeddings
 
-    def _parse_embedding_v3(self, embedding_ckpt: dict, file_path: str):
+    def _parse_embedding_v3(self, embedding_ckpt: dict, file_path: str)->List[EmbeddingInfo]:
         """
         Parse 'version 3' of the .pt textual inversion embedding files.
         """
         basename = Path(file_path).stem
         print(f'   | Loading v3 embedding file: {basename}')
-        embedding_info = {}
-        embedding_info["name"] = f'<{basename}>'
-        embedding_info["num_of_embeddings"] = 1
         embedding = embedding_ckpt['emb_params']
-        embedding_info["embedding"] = embedding
+        embedding_info = EmbeddingInfo(
-        embedding_info["num_vectors_per_token"] = embedding.size()[0]
+            name = f'<{basename}>',
-        embedding_info["token_dim"] = embedding.size()[1]
+            embedding = embedding,
-        return embedding_info
+            num_vectors_per_token = embedding.size()[0],
+            token_dim = embedding.size()[1],
+        )
+        return [embedding_info]
     
-    def _parse_embedding_v4(self, embedding_ckpt: dict, filepath: str):
+    def _parse_embedding_v4(self, embedding_ckpt: dict, filepath: str)->List[EmbeddingInfo]:
         """
         Parse 'version 4' of the textual inversion embedding files. This one
         is usually associated with .bin files trained by HuggingFace diffusers.
@@ -408,17 +412,17 @@ class TextualInversionManager(BaseTextualInversionManager):
         short_path = Path(filepath).parents[0].name+'/'+Path(filepath).name
         
         print(f'   | Loading v4 embedding file: {short_path}')
-        embedding_info = {}
+        
+        embeddings = list()
         if list(embedding_ckpt.keys()) == 0:
             print(f"   ** Invalid embeddings file: {short_path}")
-            embedding_info = None
         else:
-            for token in list(embedding_ckpt.keys()):
+            for token,embedding in embedding_ckpt.items():
-                embedding_info["name"] = (
+                embedding_info = EmbeddingInfo(
-                    token
+                    name = token or f"<{basename}>",
-                    or f"<{basename}>"
+                    embedding = embedding,
+                    num_vectors_per_token = 1,  # All Concepts seem to default to 1
+                    token_dim = embedding.size()[0],
                 )
-                embedding_info["embedding"] = embedding_ckpt[token]
+                embeddings.append(embedding_info)
-                embedding_info["num_vectors_per_token"] = 1  # All Concepts seem to default to 1
+        return embeddings
-                embedding_info["token_dim"] = embedding_info["embedding"].size()[0]
-        return embedding_info