[Textual inversion] Relax loading textual inversion (huggingface#4903)

* [Textual inversion] Relax loading textual inversion

* up

loaders.py

@@ -663,6 +663,8 @@ def load_textual_inversion(
         self,
         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,
+        tokenizer: Optional[PreTrainedTokenizer] = None,
+        text_encoder: Optional[PreTrainedModel] = None,
         **kwargs,
     ):
         r"""
@@ -684,6 +686,11 @@ def load_textual_inversion(
             token (`str` or `List[str]`, *optional*):
                 Override the token to use for the textual inversion weights. If `pretrained_model_name_or_path` is a
                 list, then `token` must also be a list of equal length.
+            text_encoder ([`~transformers.CLIPTextModel`], *optional*):
+                Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
+                If not specified, function will take self.tokenizer.
+            tokenizer ([`~transformers.CLIPTokenizer`], *optional*):
+                A `CLIPTokenizer` to tokenize text. If not specified, function will take self.tokenizer.
             weight_name (`str`, *optional*):
                 Name of a custom weight file. This should be used when:
 
@@ -757,15 +764,18 @@ def load_textual_inversion(
         ```
 
         """
-        if not hasattr(self, "tokenizer") or not isinstance(self.tokenizer, PreTrainedTokenizer):
+        tokenizer = tokenizer or getattr(self, "tokenizer", None)
+        text_encoder = text_encoder or getattr(self, "text_encoder", None)
+
+        if tokenizer is None:
             raise ValueError(
-                f"{self.__class__.__name__} requires `self.tokenizer` of type `PreTrainedTokenizer` for calling"
+                f"{self.__class__.__name__} requires `self.tokenizer` or passing a `tokenizer` of type `PreTrainedTokenizer` for calling"
                 f" `{self.load_textual_inversion.__name__}`"
             )
 
-        if not hasattr(self, "text_encoder") or not isinstance(self.text_encoder, PreTrainedModel):
+        if text_encoder is None:
             raise ValueError(
-                f"{self.__class__.__name__} requires `self.text_encoder` of type `PreTrainedModel` for calling"
+                f"{self.__class__.__name__} requires `self.text_encoder` or passing a `text_encoder` of type `PreTrainedModel` for calling"
                 f" `{self.load_textual_inversion.__name__}`"
             )
 
@@ -830,7 +840,7 @@ def load_textual_inversion(
         token_ids_and_embeddings = []
 
         for pretrained_model_name_or_path, token in zip(pretrained_model_name_or_paths, tokens):
-            if not isinstance(pretrained_model_name_or_path, dict):
+            if not isinstance(pretrained_model_name_or_path, (dict, torch.Tensor)):
                 # 1. Load textual inversion file
                 model_file = None
                 # Let's first try to load .safetensors weights
@@ -897,18 +907,18 @@ def load_textual_inversion(
             else:
                 token = loaded_token
 
-            embedding = embedding.to(dtype=self.text_encoder.dtype, device=self.text_encoder.device)
+            embedding = embedding.to(dtype=text_encoder.dtype, device=text_encoder.device)
 
             # 3. Make sure we don't mess up the tokenizer or text encoder
-            vocab = self.tokenizer.get_vocab()
+            vocab = tokenizer.get_vocab()
             if token in vocab:
                 raise ValueError(
                     f"Token {token} already in tokenizer vocabulary. Please choose a different token name or remove {token} and embedding from the tokenizer and text encoder."
                 )
             elif f"{token}_1" in vocab:
                 multi_vector_tokens = [token]
                 i = 1
-                while f"{token}_{i}" in self.tokenizer.added_tokens_encoder:
+                while f"{token}_{i}" in tokenizer.added_tokens_encoder:
                     multi_vector_tokens.append(f"{token}_{i}")
                     i += 1
 
@@ -926,16 +936,16 @@ def load_textual_inversion(
                 embeddings = [embedding[0]] if len(embedding.shape) > 1 else [embedding]
 
             # add tokens and get ids
-            self.tokenizer.add_tokens(tokens)
-            token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
+            tokenizer.add_tokens(tokens)
+            token_ids = tokenizer.convert_tokens_to_ids(tokens)
             token_ids_and_embeddings += zip(token_ids, embeddings)
 
             logger.info(f"Loaded textual inversion embedding for {token}.")
 
         # resize token embeddings and set all new embeddings
-        self.text_encoder.resize_token_embeddings(len(self.tokenizer))
+        text_encoder.resize_token_embeddings(len(tokenizer))
         for token_id, embedding in token_ids_and_embeddings:
-            self.text_encoder.get_input_embeddings().weight.data[token_id] = embedding
+            text_encoder.get_input_embeddings().weight.data[token_id] = embedding
 
         # offload back
         if is_model_cpu_offload:

pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py

@@ -84,7 +84,7 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     return noise_cfg
 
 
-class StableDiffusionXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin):
+class StableDiffusionXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion XL.
 

pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -84,7 +84,9 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     return noise_cfg
 
 
-class StableDiffusionXLImg2ImgPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin):
+class StableDiffusionXLImg2ImgPipeline(
+    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
+):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion XL.
 

pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py

@@ -230,7 +230,9 @@ def prepare_mask_and_masked_image(image, mask, height, width, return_image: bool
     return mask, masked_image
 
 
-class StableDiffusionXLInpaintPipeline(DiffusionPipeline, LoraLoaderMixin, FromSingleFileMixin):
+class StableDiffusionXLInpaintPipeline(
+    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
+):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion XL.
 

pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py

@@ -62,7 +62,9 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     return noise_cfg
 
 
-class StableDiffusionXLInstructPix2PixPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin):
+class StableDiffusionXLInstructPix2PixPipeline(
+    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
+):
     r"""
     Pipeline for pixel-level image editing by following text instructions. Based on Stable Diffusion XL.
 

pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py

@@ -123,7 +123,9 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     return noise_cfg
 
 
-class StableDiffusionXLAdapterPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin):
+class StableDiffusionXLAdapterPipeline(
+    DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion augmented with T2I-Adapter
     https://arxiv.org/abs/2302.08453