clip encoder

ldm/modules/encoders/modules.py

@@ -99,7 +99,7 @@ class BERTEmbedder(AbstractEncoder):
         return self(text)
 
 
-from transformers import T5Tokenizer, T5EncoderModel
+from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
 
 def disabled_train(self, mode=True):
     """Overwrite model.train with this function to make sure train/eval mode
@@ -115,10 +115,9 @@ class FrozenT5Embedder(AbstractEncoder):
         self.transformer = T5EncoderModel.from_pretrained(version)
         self.device = device
         self.max_length = max_length   # TODO: typical value?
-        self._freeze()
+        self.freeze()
 
-    @staticmethod
-    def _freeze(self):
+    def freeze(self):
         self.transformer = self.transformer.eval()
         self.train = disabled_train
         for param in self.parameters():
@@ -128,7 +127,36 @@ class FrozenT5Embedder(AbstractEncoder):
         batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
                                         return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
         tokens = batch_encoding["input_ids"].to(self.device)
-        outputs = model(input_ids=tokens)
+        outputs = self.transformer(input_ids=tokens)
+
+        z = outputs.last_hidden_state
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenCLIPEmbedder(AbstractEncoder):
+    """Uses the CLIP transformer encoder for text (from huggingface)"""
+    def __init__(self, version="clip-vit-large-patch14", device="cuda", max_length=77):  # clip-vit-base-patch32
+        super().__init__()
+        self.tokenizer = CLIPTokenizer.from_pretrained(version)
+        self.transformer = CLIPTextModel.from_pretrained(version)
+        self.device = device
+        self.max_length = max_length   # TODO: typical value?
+        self.freeze()
+
+    def freeze(self):
+        self.transformer = self.transformer.eval()
+        self.train = disabled_train
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        tokens = batch_encoding["input_ids"].to(self.device)
+        outputs = self.transformer(input_ids=tokens)
 
         z = outputs.last_hidden_state
         return z
@@ -172,8 +200,14 @@ class SpatialRescaler(nn.Module):
 if __name__ == "__main__":
     from ldm.util import count_params
     sentences = ["a hedgehog drinking a whiskey", "der mond ist aufgegangen", "Ein Satz mit vielen Sonderzeichen: äöü ß ?! : 'xx-y/@s'"]
-    model = FrozenT5Embedder()
+    model = FrozenT5Embedder(version="google/t5-v1_1-xl").cuda()
     count_params(model, True)
     z = model(sentences)
     print(z.shape)
-    print("done.")
+
+    model = FrozenCLIPEmbedder().cuda()
+    count_params(model, True)
+    z = model(sentences)
+    print(z.shape)
+
+    print("done.")