Delete Python_Infer_Utils/Swan.py

Python_Infer_Utils/Swan.py

@@ -1,318 +0,0 @@
-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
-
-
-ChickenFix = namedtuple('ChickenFix', ['offset', 'embedding'])
-last_extra_generation_params = {}
-
-
-class Chicken:
-    def __init__(self):
-        self.tokens = []
-        self.multipliers = []
-        self.fixes = []
-
-
-class Dog(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 Eagle:
-    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 = Dog(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 = Chicken()
-        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 = Chicken()
-        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 = Chicken()
-
-        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(ChickenFix(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