add embedding layer impl

backend/text_processing/engine.py

@@ -1,9 +1,9 @@
 import math
-from collections import namedtuple
-
 import torch
 
+from collections import namedtuple
 from backend.text_processing import parsing, emphasis
+from textual_inversion import EmbeddingDatabase
 
 
 PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])
@@ -16,28 +16,48 @@ class PromptChunk:
         self.fixes = []
 
 
-class ClassicTextProcessingEngine(torch.nn.Module):
-    def __init__(self, wrapped, hijack):
+class CLIPEmbeddingForTextualInversion(torch.nn.Module):
+    def __init__(self, wrapped, embeddings, textual_inversion_key='clip_l'):
         super().__init__()
-        self.chunk_length = 75
-
-        self.is_trainable = False
-        self.input_key = 'txt'
-        self.return_pooled = False
-
-        self.comma_token = None
-
-        self.hijack = hijack
-
         self.wrapped = wrapped
+        self.embeddings = embeddings
+        self.textual_inversion_key = textual_inversion_key
+        self.weight = self.wrapped.weight
 
-        self.is_trainable = getattr(wrapped, 'is_trainable', False)
-        self.input_key = getattr(wrapped, 'input_key', 'txt')
-        self.return_pooled = getattr(self.wrapped, 'return_pooled', False)
+    def forward(self, input_ids):
+        batch_fixes = self.embeddings.fixes
+        self.embeddings.fixes = None
 
-        self.legacy_ucg_val = None  # for sgm codebase
+        inputs_embeds = self.wrapped(input_ids)
 
-        self.tokenizer = wrapped.tokenizer
+        if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:
+            return inputs_embeds
+
+        vecs = []
+        for fixes, tensor in zip(batch_fixes, inputs_embeds):
+            for offset, embedding in fixes:
+                emb = embedding.vec[self.textual_inversion_key] if isinstance(embedding.vec, dict) else embedding.vec
+                emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
+                tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]]).to(dtype=inputs_embeds.dtype)
+
+            vecs.append(tensor)
+
+        return torch.stack(vecs)
+
+
+class ClassicTextProcessingEngine:
+    def __init__(self, text_encoder, tokenizer, chunk_length=75, embedding_dir='./embeddings', embedding_key='clip_l', embedding_expected_shape=768):
+        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
+
+        model_embeddings = text_encoder.text_model.embeddings
+        model_embeddings.token_embedding = CLIPEmbeddingForTextualInversion(model_embeddings.token_embedding, self.embeddings, textual_inversion_key=embedding_key)
 
         vocab = self.tokenizer.get_vocab()
 
@@ -61,10 +81,13 @@ class ClassicTextProcessingEngine(torch.nn.Module):
             if mult != 1.0:
                 self.token_mults[ident] = mult
 
-        self.id_start = self.wrapped.tokenizer.bos_token_id
-        self.id_end = self.wrapped.tokenizer.eos_token_id
+        self.id_start = self.tokenizer.bos_token_id
+        self.id_end = self.tokenizer.eos_token_id
         self.id_pad = self.id_end
 
+        # Todo: remove these
+        self.legacy_ucg_val = None  # for sgm codebase
+
     def empty_chunk(self):
         chunk = PromptChunk()
         chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1)