Adding support for .bin files from huggingface concepts (invoke-ai#498)

* Adding support for .bin files from huggingface concepts

* Updating documentation to include huggingface .bin info

Author: chromaticist

docs/features/TEXTUAL_INVERSION.md

@@ -1,6 +1,8 @@
 # **Personalizing Text-to-Image Generation**
 
-You may personalize the generated images to provide your own styles or objects by training a new LDM checkpoint and introducing a new vocabulary to the fixed model.
+You may personalize the generated images to provide your own styles or objects by training a new LDM checkpoint and introducing a new vocabulary to the fixed model as a (.pt) embeddings file. Alternatively, you may use or train HuggingFace Concepts embeddings files (.bin) from https://huggingface.co/sd-concepts-library and its associated notebooks.
+
+**Training**
 
 To train, prepare a folder that contains images sized at 512x512 and execute the following:
 
@@ -26,9 +28,11 @@ On a RTX3090, the process for SD will take ~1h @1.6 iterations/sec.
 
 _Note_: According to the associated paper, the optimal number of images is 3-5. Your model may not converge if you use more images than that.
 
-Training will run indefinately, but you may wish to stop it before the heat death of the universe, when you find a low loss epoch or around ~5000 iterations.
+Training will run indefinitely, but you may wish to stop it before the heat death of the universe, when you find a low loss epoch or around ~5000 iterations.
+
+**Running**
 
-Once the model is trained, specify the trained .pt file when starting dream using
+Once the model is trained, specify the trained .pt or .bin file when starting dream using
 
 ```
 (ldm) ~/stable-diffusion$ python3 ./scripts/dream.py --embedding_path /path/to/embedding.pt --full_precision
@@ -46,7 +50,7 @@ This also works with image2image
 dream> "waterfall and rainbow in the style of *" --init_img=./init-images/crude_drawing.png --strength=0.5 -s100 -n4
 ```
 
-It's also possible to train multiple token (modify the placeholder string in `configs/stable-diffusion/v1-finetune.yaml`) and combine LDM checkpoints using:
+For .pt files it's also possible to train multiple tokens (modify the placeholder string in `configs/stable-diffusion/v1-finetune.yaml`) and combine LDM checkpoints using:
 
 ```
 (ldm) ~/stable-diffusion$ python3 ./scripts/merge_embeddings.py \

ldm/modules/embedding_manager.py

@@ -24,9 +24,9 @@ def get_clip_token_for_string(tokenizer, string):
         return_tensors='pt',
     )
     tokens = batch_encoding['input_ids']
-    assert (
+    """ assert (
         torch.count_nonzero(tokens - 49407) == 2
-    ), f"String '{string}' maps to more than a single token. Please use another string"
+    ), f"String '{string}' maps to more than a single token. Please use another string" """
 
     return tokens[0, 1]
 
@@ -57,8 +57,9 @@ def __init__(
     ):
         super().__init__()
 
-        self.string_to_token_dict = {}
+        self.embedder = embedder
 
+        self.string_to_token_dict = {}
         self.string_to_param_dict = nn.ParameterDict()
 
         self.initial_embeddings = (
@@ -217,12 +218,28 @@ def save(self, ckpt_path):
 
     def load(self, ckpt_path, full=True):
         ckpt = torch.load(ckpt_path, map_location='cpu')
-        self.string_to_token_dict = ckpt["string_to_token"]
-        self.string_to_param_dict = ckpt["string_to_param"]
+
+        # Handle .pt textual inversion files
+        if 'string_to_token' in ckpt and 'string_to_param' in ckpt:
+            self.string_to_token_dict = ckpt["string_to_token"]
+            self.string_to_param_dict = ckpt["string_to_param"]
+
+        # Handle .bin textual inversion files from Huggingface Concepts
+        # https://huggingface.co/sd-concepts-library
+        else:
+            for token_str in list(ckpt.keys()):
+                token = get_clip_token_for_string(self.embedder.tokenizer, token_str)
+                self.string_to_token_dict[token_str] = token
+                ckpt[token_str] = torch.nn.Parameter(ckpt[token_str])
+                
+            self.string_to_param_dict.update(ckpt)
+
         if not full:
             for key, value in self.string_to_param_dict.items():
                 self.string_to_param_dict[key] = torch.nn.Parameter(value.half())
 
+        print(f'Added terms: {", ".join(self.string_to_param_dict.keys())}')
+
     def get_embedding_norms_squared(self):
         all_params = torch.cat(
             list(self.string_to_param_dict.values()), axis=0