diff --git a/backend/text_processing/engine.py b/backend/text_processing/engine.py
index 07cd2949..241d70cf 100644
--- a/backend/text_processing/engine.py
+++ b/backend/text_processing/engine.py
@@ -46,16 +46,28 @@ class CLIPEmbeddingForTextualInversion(torch.nn.Module):
 
 
 class ClassicTextProcessingEngine:
-    def __init__(self, text_encoder, tokenizer, chunk_length=75, embedding_dir='./embeddings', embedding_key='clip_l', embedding_expected_shape=768, emphasis_name="original"):
+    def __init__(self, text_encoder, tokenizer, chunk_length=75, embedding_dir='./embeddings', embedding_key='clip_l', embedding_expected_shape=768, emphasis_name="original", text_projection=None, minimal_clip_skip=1, clip_skip=1, return_pooled=False, callback_before_encode=None):
         super().__init__()
 
-        self.chunk_length = chunk_length
         self.embeddings = EmbeddingDatabase(tokenizer, embedding_expected_shape)
         self.embeddings.add_embedding_dir(embedding_dir)
         self.embeddings.load_textual_inversion_embeddings()
+
         self.text_encoder = text_encoder
         self.tokenizer = tokenizer
+
         self.emphasis = emphasis.get_current_option(emphasis_name)
+        self.text_projection = text_projection
+        self.minimal_clip_skip = minimal_clip_skip
+        self.clip_skip = clip_skip
+        self.return_pooled = return_pooled
+        self.callback_before_encode = callback_before_encode
+
+        self.chunk_length = chunk_length
+
+        self.id_start = self.tokenizer.bos_token_id
+        self.id_end = self.tokenizer.eos_token_id
+        self.id_pad = self.id_end
 
         model_embeddings = text_encoder.text_model.embeddings
         model_embeddings.token_embedding = CLIPEmbeddingForTextualInversion(model_embeddings.token_embedding, self.embeddings, textual_inversion_key=embedding_key)
@@ -82,13 +94,8 @@ class ClassicTextProcessingEngine:
             if mult != 1.0:
                 self.token_mults[ident] = mult
 
-        self.id_start = self.tokenizer.bos_token_id
-        self.id_end = self.tokenizer.eos_token_id
-        self.id_pad = self.id_end
-        self.return_pooled = True
-
-        # Todo: remove these
-        self.legacy_ucg_val = None  # for sgm codebase
+        # # Todo: remove these
+        # self.legacy_ucg_val = None  # for sgm codebase
 
     def empty_chunk(self):
         chunk = PromptChunk()
@@ -105,7 +112,20 @@ class ClassicTextProcessingEngine:
         return tokenized
 
     def encode_with_transformers(self, tokens):
-        raise NotImplementedError
+        self.text_encoder.transformer.text_model.embeddings.to(tokens.device)
+        outputs = self.text_encoder.transformer(tokens, output_hidden_states=True)
+
+        layer_id = - max(self.clip_skip, self.minimal_clip_skip)
+        z = outputs.hidden_states[layer_id]
+
+        if self.return_pooled:
+            pooled_output = outputs.pooler_output
+
+            if self.text_projection:
+                pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float()
+
+            z.pooled = pooled_output
+        return z
 
     def encode_embedding_init_text(self, init_text, nvpt):
         embedding_layer = self.text_encoder.transformer.text_model.embeddings
@@ -215,7 +235,10 @@ class ClassicTextProcessingEngine:
 
         return batch_chunks, token_count
 
-    def forward(self, texts):
+    def __call__(self, texts):
+        if self.callback_before_encode is not None:
+            self.callback_before_encode()
+
         batch_chunks, token_count = self.process_texts(texts)
 
         used_embeddings = {}