Merge branch 'main' into perf/lowmem_sequential_guidance

ldm/generate.py

@@ -964,6 +964,7 @@ class Generate:
 
         seed_everything(random.randrange(0, np.iinfo(np.uint32).max))
         if self.embedding_path is not None:
+            print(f'>> Loading embeddings from {self.embedding_path}')
             for root, _, files in os.walk(self.embedding_path):
                 for name in files:
                     ti_path = os.path.join(root, name)
@@ -971,7 +972,7 @@ class Generate:
                         ti_path, defer_injecting_tokens=True
                     )
             print(
-                f'>> Textual inversions available: {", ".join(self.model.textual_inversion_manager.get_all_trigger_strings())}'
+                f'>> Textual inversion triggers: {", ".join(self.model.textual_inversion_manager.get_all_trigger_strings())}'
             )
 
         self.model_name = model_name

ldm/invoke/training/textual_inversion_training.py

@@ -441,6 +441,7 @@ class TextualInversionDataset(Dataset):
         self.image_paths = [
             os.path.join(self.data_root, file_path)
             for file_path in os.listdir(self.data_root)
+            if os.path.isfile(file_path) and file_path.endswith(('.png','.PNG','.jpg','.JPG','.jpeg','.JPEG','.gif','.GIF'))
         ]
 
         self.num_images = len(self.image_paths)

ldm/modules/textual_inversion_manager.py

@@ -61,8 +61,13 @@ class TextualInversionManager:
 
     def load_textual_inversion(self, ckpt_path: Union[str,Path], defer_injecting_tokens: bool = False):
         ckpt_path = Path(ckpt_path)
+
+        if not ckpt_path.is_file():
+            return
+        
         if str(ckpt_path).endswith(".DS_Store"):
             return
+        
         try:
             scan_result = scan_file_path(str(ckpt_path))
             if scan_result.infected_files == 1:
@@ -87,7 +92,7 @@ class TextualInversionManager:
             != embedding_info['token_dim']
         ):
             print(
-                f"** Notice: {ckpt_path.parents[0].name}/{ckpt_path.name} was trained on a model with a different token dimension. It can't be used with this model."
+                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
 
@@ -333,7 +338,6 @@ class TextualInversionManager:
         # .pt files found at https://cyberes.github.io/stable-diffusion-textual-inversion-models/
         # They are actually .bin files
         elif len(embedding_ckpt.keys()) == 1:
-            print(">> Detected .bin file masquerading as .pt file")
             embedding_info = self._parse_embedding_bin(embedding_file)
 
         else:
@@ -372,9 +376,6 @@ class TextualInversionManager:
         if isinstance(
             list(embedding_ckpt["string_to_token"].values())[0], torch.Tensor
         ):
-            print(
-                ">> Detected .pt file variant 1"
-            )  # example at https://github.com/invoke-ai/InvokeAI/issues/1829
             for token in list(embedding_ckpt["string_to_token"].keys()):
                 embedding_info["name"] = (
                     token
@@ -387,7 +388,7 @@ class TextualInversionManager:
                 embedding_info["num_vectors_per_token"] = embedding_info[
                     "embedding"
                 ].shape[0]
-                embedding_info["token_dim"] = embedding_info["embedding"].size()[0]
+                embedding_info["token_dim"] = embedding_info["embedding"].size()[1]
         else:
             print(">> Invalid embedding format")
             embedding_info = None