[Feat] Enable State Dict For Textual Inversion Loader

src/diffusers/loaders.py

@@ -470,7 +470,7 @@ def _maybe_convert_prompt(self, prompt: str, tokenizer: "PreTrainedTokenizer"):
 
     def load_textual_inversion(
         self,
-        pretrained_model_name_or_path: Union[str, List[str]],
+        pretrained_model_name_or_path: Union[str, List[str], Dict[str, torch.Tensor], List[Dict[str, torch.Tensor]]],
         token: Optional[Union[str, List[str]]] = None,
         **kwargs,
     ):
@@ -485,7 +485,7 @@ def load_textual_inversion(
         </Tip>
 
         Parameters:
-            pretrained_model_name_or_path (`str` or `os.PathLike` or `List[str or os.PathLike]`):
+            pretrained_model_name_or_path (`str` or `os.PathLike` or `List[str or os.PathLike]` or `Dict` or `List[Dict]`):
                 Can be either:
 
                     - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
@@ -494,6 +494,8 @@ def load_textual_inversion(
                     - A path to a *directory* containing textual inversion weights, e.g.
                       `./my_text_inversion_directory/`.
                     - A path to a *file* containing textual inversion weights, e.g. `./my_text_inversions.pt`.
+                    - A [torch state
+                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
 
                 Or a list of those elements.
             token (`str` or `List[str]`, *optional*):
@@ -618,7 +620,7 @@ def load_textual_inversion(
             "framework": "pytorch",
         }
 
-        if isinstance(pretrained_model_name_or_path, str):
+        if not isinstance(pretrained_model_name_or_path, list):
             pretrained_model_name_or_paths = [pretrained_model_name_or_path]
         else:
             pretrained_model_name_or_paths = pretrained_model_name_or_path
@@ -643,16 +645,38 @@ def load_textual_inversion(
         token_ids_and_embeddings = []
 
         for pretrained_model_name_or_path, token in zip(pretrained_model_name_or_paths, tokens):
-            # 1. Load textual inversion file
-            model_file = None
-            # Let's first try to load .safetensors weights
-            if (use_safetensors and weight_name is None) or (
-                weight_name is not None and weight_name.endswith(".safetensors")
-            ):
-                try:
+            if not isinstance(pretrained_model_name_or_path, dict):
+                # 1. Load textual inversion file
+                model_file = None
+                # Let's first try to load .safetensors weights
+                if (use_safetensors and weight_name is None) or (
+                    weight_name is not None and weight_name.endswith(".safetensors")
+                ):
+                    try:
+                        model_file = _get_model_file(
+                            pretrained_model_name_or_path,
+                            weights_name=weight_name or TEXT_INVERSION_NAME_SAFE,
+                            cache_dir=cache_dir,
+                            force_download=force_download,
+                            resume_download=resume_download,
+                            proxies=proxies,
+                            local_files_only=local_files_only,
+                            use_auth_token=use_auth_token,
+                            revision=revision,
+                            subfolder=subfolder,
+                            user_agent=user_agent,
+                        )
+                        state_dict = safetensors.torch.load_file(model_file, device="cpu")
+                    except Exception as e:
+                        if not allow_pickle:
+                            raise e
+
+                        model_file = None
+
+                if model_file is None:
                     model_file = _get_model_file(
                         pretrained_model_name_or_path,
-                        weights_name=weight_name or TEXT_INVERSION_NAME_SAFE,
+                        weights_name=weight_name or TEXT_INVERSION_NAME,
                         cache_dir=cache_dir,
                         force_download=force_download,
                         resume_download=resume_download,
@@ -663,28 +687,9 @@ def load_textual_inversion(
                         subfolder=subfolder,
                         user_agent=user_agent,
                     )
-                    state_dict = safetensors.torch.load_file(model_file, device="cpu")
-                except Exception as e:
-                    if not allow_pickle:
-                        raise e
-
-                    model_file = None
-
-            if model_file is None:
-                model_file = _get_model_file(
-                    pretrained_model_name_or_path,
-                    weights_name=weight_name or TEXT_INVERSION_NAME,
-                    cache_dir=cache_dir,
-                    force_download=force_download,
-                    resume_download=resume_download,
-                    proxies=proxies,
-                    local_files_only=local_files_only,
-                    use_auth_token=use_auth_token,
-                    revision=revision,
-                    subfolder=subfolder,
-                    user_agent=user_agent,
-                )
-                state_dict = torch.load(model_file, map_location="cpu")
+                    state_dict = torch.load(model_file, map_location="cpu")
+            else:
+                state_dict = pretrained_model_name_or_path
 
             # 2. Load token and embedding correcly from file
             loaded_token = None

tests/pipelines/test_pipelines.py

@@ -663,6 +663,65 @@ def test_text_inversion_download(self):
                 out = pipe(prompt, num_inference_steps=1, output_type="numpy").images
                 assert out.shape == (1, 128, 128, 3)
 
+        # single token state dict load
+        ten = {"<x>": torch.ones((32,))}
+        pipe.load_textual_inversion(ten)
+
+        token = pipe.tokenizer.convert_tokens_to_ids("<x>")
+        assert token == num_tokens + 10, "Added token must be at spot `num_tokens`"
+        assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 32
+        assert pipe._maybe_convert_prompt("<x>", pipe.tokenizer) == "<x>"
+
+        prompt = "hey <x>"
+        out = pipe(prompt, num_inference_steps=1, output_type="numpy").images
+        assert out.shape == (1, 128, 128, 3)
+
+        # multi embedding state dict load
+        ten1 = {"<xxxxx>": torch.ones((32,))}
+        ten2 = {"<xxxxxx>": 2 * torch.ones((1, 32))}
+
+        pipe.load_textual_inversion([ten1, ten2])
+
+        token = pipe.tokenizer.convert_tokens_to_ids("<xxxxx>")
+        assert token == num_tokens + 11, "Added token must be at spot `num_tokens`"
+        assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 32
+        assert pipe._maybe_convert_prompt("<xxxxx>", pipe.tokenizer) == "<xxxxx>"
+
+        token = pipe.tokenizer.convert_tokens_to_ids("<xxxxxx>")
+        assert token == num_tokens + 12, "Added token must be at spot `num_tokens`"
+        assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 64
+        assert pipe._maybe_convert_prompt("<xxxxxx>", pipe.tokenizer) == "<xxxxxx>"
+
+        prompt = "hey <xxxxx> <xxxxxx>"
+        out = pipe(prompt, num_inference_steps=1, output_type="numpy").images
+        assert out.shape == (1, 128, 128, 3)
+
+        # auto1111 multi-token state dict load
+        ten = {
+            "string_to_param": {
+                "*": torch.cat([3 * torch.ones((1, 32)), 4 * torch.ones((1, 32)), 5 * torch.ones((1, 32))])
+            },
+            "name": "<xxxx>",
+        }
+
+        pipe.load_textual_inversion(ten)
+
+        token = pipe.tokenizer.convert_tokens_to_ids("<xxxx>")
+        token_1 = pipe.tokenizer.convert_tokens_to_ids("<xxxx>_1")
+        token_2 = pipe.tokenizer.convert_tokens_to_ids("<xxxx>_2")
+
+        assert token == num_tokens + 13, "Added token must be at spot `num_tokens`"
+        assert token_1 == num_tokens + 14, "Added token must be at spot `num_tokens`"
+        assert token_2 == num_tokens + 15, "Added token must be at spot `num_tokens`"
+        assert pipe.text_encoder.get_input_embeddings().weight[-3].sum().item() == 96
+        assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 128
+        assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 160
+        assert pipe._maybe_convert_prompt("<xxxx>", pipe.tokenizer) == "<xxxx> <xxxx>_1 <xxxx>_2"
+
+        prompt = "hey <xxxx>"
+        out = pipe(prompt, num_inference_steps=1, output_type="numpy").images
+        assert out.shape == (1, 128, 128, 3)
+
     def test_download_ignore_files(self):
         # Check https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe-ignore-files/blob/72f58636e5508a218c6b3f60550dc96445547817/model_index.json#L4
         with tempfile.TemporaryDirectory() as tmpdirname: