initial commit

backend/text_processing/classic_engine.py

@@ -0,0 +1,318 @@
+import math
+import torch
+
+from collections import namedtuple
+from backend.text_processing import parsing, emphasis
+from backend.text_processing.textual_inversion import EmbeddingDatabase
+from backend import memory_management
+
+from modules.shared import opts
+
+
+PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])
+last_extra_generation_params = {}
+
+
+class PromptChunk:
+    def __init__(self):
+        self.tokens = []
+        self.multipliers = []
+        self.fixes = []
+
+
+class CLIPEmbeddingForTextualInversion(torch.nn.Module):
+    def __init__(self, wrapped, embeddings, textual_inversion_key='clip_l'):
+        super().__init__()
+        self.wrapped = wrapped
+        self.embeddings = embeddings
+        self.textual_inversion_key = textual_inversion_key
+        self.weight = self.wrapped.weight
+
+    def forward(self, input_ids):
+        batch_fixes = self.embeddings.fixes
+        self.embeddings.fixes = None
+
+        inputs_embeds = self.wrapped(input_ids)
+
+        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 = emb.to(inputs_embeds)
+                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=None, embedding_key='clip_l', embedding_expected_shape=768, emphasis_name="Original",
+            text_projection=False, minimal_clip_skip=1, clip_skip=1, return_pooled=False, final_layer_norm=True
+    ):
+        super().__init__()
+
+        self.embeddings = EmbeddingDatabase(tokenizer, embedding_expected_shape)
+
+        if isinstance(embedding_dir, str):
+            self.embeddings.add_embedding_dir(embedding_dir)
+            self.embeddings.load_textual_inversion_embeddings()
+
+        self.embedding_key = embedding_key
+
+        self.text_encoder = text_encoder
+        self.tokenizer = tokenizer
+
+        self.emphasis = emphasis.get_current_option(opts.emphasis)()
+        self.text_projection = text_projection
+        self.minimal_clip_skip = minimal_clip_skip
+        self.clip_skip = clip_skip
+        self.return_pooled = return_pooled
+        self.final_layer_norm = final_layer_norm
+
+        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.tokenizer.pad_token_id
+
+        model_embeddings = text_encoder.transformer.text_model.embeddings
+        model_embeddings.token_embedding = CLIPEmbeddingForTextualInversion(model_embeddings.token_embedding, self.embeddings, textual_inversion_key=embedding_key)
+
+        vocab = self.tokenizer.get_vocab()
+
+        self.comma_token = vocab.get(',</w>', None)
+
+        self.token_mults = {}
+
+        tokens_with_parens = [(k, v) for k, v in vocab.items() if '(' in k or ')' in k or '[' in k or ']' in k]
+        for text, ident in tokens_with_parens:
+            mult = 1.0
+            for c in text:
+                if c == '[':
+                    mult /= 1.1
+                if c == ']':
+                    mult *= 1.1
+                if c == '(':
+                    mult *= 1.1
+                if c == ')':
+                    mult /= 1.1
+
+            if mult != 1.0:
+                self.token_mults[ident] = mult
+
+    def empty_chunk(self):
+        chunk = PromptChunk()
+        chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1)
+        chunk.multipliers = [1.0] * (self.chunk_length + 2)
+        return chunk
+
+    def get_target_prompt_token_count(self, token_count):
+        return math.ceil(max(token_count, 1) / self.chunk_length) * self.chunk_length
+
+    def tokenize(self, texts):
+        tokenized = self.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
+
+        return tokenized
+
+    def encode_with_transformers(self, tokens):
+        target_device = memory_management.text_encoder_device()
+
+        self.text_encoder.transformer.text_model.embeddings.position_ids = self.text_encoder.transformer.text_model.embeddings.position_ids.to(device=target_device)
+        self.text_encoder.transformer.text_model.embeddings.position_embedding = self.text_encoder.transformer.text_model.embeddings.position_embedding.to(dtype=torch.float32)
+        self.text_encoder.transformer.text_model.embeddings.token_embedding = self.text_encoder.transformer.text_model.embeddings.token_embedding.to(dtype=torch.float32)
+
+        tokens = tokens.to(target_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.final_layer_norm:
+            z = self.text_encoder.transformer.text_model.final_layer_norm(z)
+
+        if self.return_pooled:
+            pooled_output = outputs.pooler_output
+
+            if self.text_projection and self.embedding_key != 'clip_l':
+                pooled_output = self.text_encoder.transformer.text_projection(pooled_output)
+
+            z.pooled = pooled_output
+        return z
+
+    def tokenize_line(self, line):
+        parsed = parsing.parse_prompt_attention(line, self.emphasis.name)
+
+        tokenized = self.tokenize([text for text, _ in parsed])
+
+        chunks = []
+        chunk = PromptChunk()
+        token_count = 0
+        last_comma = -1
+
+        def next_chunk(is_last=False):
+            nonlocal token_count
+            nonlocal last_comma
+            nonlocal chunk
+
+            if is_last:
+                token_count += len(chunk.tokens)
+            else:
+                token_count += self.chunk_length
+
+            to_add = self.chunk_length - len(chunk.tokens)
+            if to_add > 0:
+                chunk.tokens += [self.id_end] * to_add
+                chunk.multipliers += [1.0] * to_add
+
+            chunk.tokens = [self.id_start] + chunk.tokens + [self.id_end]
+            chunk.multipliers = [1.0] + chunk.multipliers + [1.0]
+
+            last_comma = -1
+            chunks.append(chunk)
+            chunk = PromptChunk()
+
+        for tokens, (text, weight) in zip(tokenized, parsed):
+            if text == 'BREAK' and weight == -1:
+                next_chunk()
+                continue
+
+            position = 0
+            while position < len(tokens):
+                token = tokens[position]
+
+                comma_padding_backtrack = 20
+
+                if token == self.comma_token:
+                    last_comma = len(chunk.tokens)
+
+                elif comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= comma_padding_backtrack:
+                    break_location = last_comma + 1
+
+                    reloc_tokens = chunk.tokens[break_location:]
+                    reloc_mults = chunk.multipliers[break_location:]
+
+                    chunk.tokens = chunk.tokens[:break_location]
+                    chunk.multipliers = chunk.multipliers[:break_location]
+
+                    next_chunk()
+                    chunk.tokens = reloc_tokens
+                    chunk.multipliers = reloc_mults
+
+                if len(chunk.tokens) == self.chunk_length:
+                    next_chunk()
+
+                embedding, embedding_length_in_tokens = self.embeddings.find_embedding_at_position(tokens, position)
+                if embedding is None:
+                    chunk.tokens.append(token)
+                    chunk.multipliers.append(weight)
+                    position += 1
+                    continue
+
+                emb_len = int(embedding.vectors)
+                if len(chunk.tokens) + emb_len > self.chunk_length:
+                    next_chunk()
+
+                chunk.fixes.append(PromptChunkFix(len(chunk.tokens), embedding))
+
+                chunk.tokens += [0] * emb_len
+                chunk.multipliers += [weight] * emb_len
+                position += embedding_length_in_tokens
+
+        if chunk.tokens or not chunks:
+            next_chunk(is_last=True)
+
+        return chunks, token_count
+
+    def process_texts(self, texts):
+        token_count = 0
+
+        cache = {}
+        batch_chunks = []
+        for line in texts:
+            if line in cache:
+                chunks = cache[line]
+            else:
+                chunks, current_token_count = self.tokenize_line(line)
+                token_count = max(current_token_count, token_count)
+
+                cache[line] = chunks
+
+            batch_chunks.append(chunks)
+
+        return batch_chunks, token_count
+
+    def __call__(self, texts):
+        self.emphasis = emphasis.get_current_option(opts.emphasis)()
+
+        batch_chunks, token_count = self.process_texts(texts)
+
+        used_embeddings = {}
+        chunk_count = max([len(x) for x in batch_chunks])
+
+        zs = []
+        for i in range(chunk_count):
+            batch_chunk = [chunks[i] if i < len(chunks) else self.empty_chunk() for chunks in batch_chunks]
+
+            tokens = [x.tokens for x in batch_chunk]
+            multipliers = [x.multipliers for x in batch_chunk]
+            self.embeddings.fixes = [x.fixes for x in batch_chunk]
+
+            for fixes in self.embeddings.fixes:
+                for _position, embedding in fixes:
+                    used_embeddings[embedding.name] = embedding
+
+            z = self.process_tokens(tokens, multipliers)
+            zs.append(z)
+
+        global last_extra_generation_params
+
+        if used_embeddings:
+            names = []
+
+            for name, embedding in used_embeddings.items():
+                print(f'[Textual Inversion] Used Embedding [{name}] in CLIP of [{self.embedding_key}]')
+                names.append(name.replace(":", "").replace(",", ""))
+
+            if "TI" in last_extra_generation_params:
+                last_extra_generation_params["TI"] += ", " + ", ".join(names)
+            else:
+                last_extra_generation_params["TI"] = ", ".join(names)
+
+        if any(x for x in texts if "(" in x or "[" in x) and self.emphasis.name != "Original":
+            last_extra_generation_params["Emphasis"] = self.emphasis.name
+
+        if self.return_pooled:
+            return torch.hstack(zs), zs[0].pooled
+        else:
+            return torch.hstack(zs)
+
+    def process_tokens(self, remade_batch_tokens, batch_multipliers):
+        tokens = torch.asarray(remade_batch_tokens)
+
+        if self.id_end != self.id_pad:
+            for batch_pos in range(len(remade_batch_tokens)):
+                index = remade_batch_tokens[batch_pos].index(self.id_end)
+                tokens[batch_pos, index + 1:tokens.shape[1]] = self.id_pad
+
+        z = self.encode_with_transformers(tokens)
+
+        pooled = getattr(z, 'pooled', None)
+
+        self.emphasis.tokens = remade_batch_tokens
+        self.emphasis.multipliers = torch.asarray(batch_multipliers).to(z)
+        self.emphasis.z = z
+        self.emphasis.after_transformers()
+        z = self.emphasis.z
+
+        if pooled is not None:
+            z.pooled = pooled
+
+        return z