mirror of
https://github.com/lllyasviel/stable-diffusion-webui-forge.git
synced 2026-01-26 10:59:47 +00:00
remove unused
This commit is contained in:
layerdiffusion
@@ -1,384 +1,384 @@
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import math
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from collections import namedtuple
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import torch
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from modules import prompt_parser, devices, sd_hijack, sd_emphasis
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from modules.shared import opts
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class PromptChunk:
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"""
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This object contains token ids, weight (multipliers:1.4) and textual inversion embedding info for a chunk of prompt.
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If a prompt is short, it is represented by one PromptChunk, otherwise, multiple are necessary.
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Each PromptChunk contains an exact amount of tokens - 77, which includes one for start and end token,
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so just 75 tokens from prompt.
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"""
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def __init__(self):
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self.tokens = []
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self.multipliers = []
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self.fixes = []
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PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])
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"""An object of this type is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt
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chunk. Those objects are found in PromptChunk.fixes and, are placed into FrozenCLIPEmbedderWithCustomWordsBase.hijack.fixes, and finally
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are applied by sd_hijack.EmbeddingsWithFixes's forward function."""
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class TextConditionalModel(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.hijack = sd_hijack.model_hijack
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self.chunk_length = 75
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self.is_trainable = False
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self.input_key = 'txt'
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self.return_pooled = False
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self.comma_token = None
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self.id_start = None
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self.id_end = None
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self.id_pad = None
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def empty_chunk(self):
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"""creates an empty PromptChunk and returns it"""
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chunk = PromptChunk()
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chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1)
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chunk.multipliers = [1.0] * (self.chunk_length + 2)
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return chunk
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def get_target_prompt_token_count(self, token_count):
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"""returns the maximum number of tokens a prompt of a known length can have before it requires one more PromptChunk to be represented"""
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return math.ceil(max(token_count, 1) / self.chunk_length) * self.chunk_length
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def tokenize(self, texts):
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"""Converts a batch of texts into a batch of token ids"""
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raise NotImplementedError
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def encode_with_transformers(self, tokens):
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"""
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converts a batch of token ids (in python lists) into a single tensor with numeric representation of those tokens;
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All python lists with tokens are assumed to have same length, usually 77.
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if input is a list with B elements and each element has T tokens, expected output shape is (B, T, C), where C depends on
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model - can be 768 and 1024.
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Among other things, this call will read self.hijack.fixes, apply it to its inputs, and clear it (setting it to None).
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"""
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raise NotImplementedError
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def encode_embedding_init_text(self, init_text, nvpt):
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"""Converts text into a tensor with this text's tokens' embeddings. Note that those are embeddings before they are passed through
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transformers. nvpt is used as a maximum length in tokens. If text produces less teokens than nvpt, only this many is returned."""
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raise NotImplementedError
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def tokenize_line(self, line):
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"""
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this transforms a single prompt into a list of PromptChunk objects - as many as needed to
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represent the prompt.
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Returns the list and the total number of tokens in the prompt.
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"""
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if opts.emphasis != "None":
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parsed = prompt_parser.parse_prompt_attention(line)
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else:
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parsed = [[line, 1.0]]
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tokenized = self.tokenize([text for text, _ in parsed])
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chunks = []
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chunk = PromptChunk()
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token_count = 0
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last_comma = -1
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def next_chunk(is_last=False):
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"""puts current chunk into the list of results and produces the next one - empty;
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if is_last is true, tokens <end-of-text> tokens at the end won't add to token_count"""
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nonlocal token_count
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nonlocal last_comma
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nonlocal chunk
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if is_last:
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token_count += len(chunk.tokens)
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else:
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token_count += self.chunk_length
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to_add = self.chunk_length - len(chunk.tokens)
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if to_add > 0:
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chunk.tokens += [self.id_end] * to_add
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chunk.multipliers += [1.0] * to_add
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chunk.tokens = [self.id_start] + chunk.tokens + [self.id_end]
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chunk.multipliers = [1.0] + chunk.multipliers + [1.0]
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last_comma = -1
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chunks.append(chunk)
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chunk = PromptChunk()
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for tokens, (text, weight) in zip(tokenized, parsed):
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if text == 'BREAK' and weight == -1:
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next_chunk()
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continue
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position = 0
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while position < len(tokens):
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token = tokens[position]
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if token == self.comma_token:
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last_comma = len(chunk.tokens)
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# this is when we are at the end of allotted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack
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# is a setting that specifies that if there is a comma nearby, the text after the comma should be moved out of this chunk and into the next.
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elif opts.comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= opts.comma_padding_backtrack:
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break_location = last_comma + 1
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reloc_tokens = chunk.tokens[break_location:]
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reloc_mults = chunk.multipliers[break_location:]
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chunk.tokens = chunk.tokens[:break_location]
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chunk.multipliers = chunk.multipliers[:break_location]
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next_chunk()
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chunk.tokens = reloc_tokens
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chunk.multipliers = reloc_mults
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if len(chunk.tokens) == self.chunk_length:
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next_chunk()
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embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, position)
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if embedding is None:
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chunk.tokens.append(token)
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chunk.multipliers.append(weight)
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position += 1
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continue
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emb_len = int(embedding.vectors)
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if len(chunk.tokens) + emb_len > self.chunk_length:
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next_chunk()
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chunk.fixes.append(PromptChunkFix(len(chunk.tokens), embedding))
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chunk.tokens += [0] * emb_len
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chunk.multipliers += [weight] * emb_len
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position += embedding_length_in_tokens
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if chunk.tokens or not chunks:
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next_chunk(is_last=True)
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return chunks, token_count
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def process_texts(self, texts):
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"""
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Accepts a list of texts and calls tokenize_line() on each, with cache. Returns the list of results and maximum
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length, in tokens, of all texts.
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"""
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token_count = 0
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cache = {}
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batch_chunks = []
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for line in texts:
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if line in cache:
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chunks = cache[line]
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else:
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chunks, current_token_count = self.tokenize_line(line)
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token_count = max(current_token_count, token_count)
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cache[line] = chunks
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batch_chunks.append(chunks)
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return batch_chunks, token_count
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def forward(self, texts):
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"""
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Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts.
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Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will
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be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, for SD2 it's 1024, and for SDXL it's 1280.
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An example shape returned by this function can be: (2, 77, 768).
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For SDXL, instead of returning one tensor avobe, it returns a tuple with two: the other one with shape (B, 1280) with pooled values.
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Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one element
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is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
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"""
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batch_chunks, token_count = self.process_texts(texts)
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used_embeddings = {}
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chunk_count = max([len(x) for x in batch_chunks])
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zs = []
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for i in range(chunk_count):
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batch_chunk = [chunks[i] if i < len(chunks) else self.empty_chunk() for chunks in batch_chunks]
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tokens = [x.tokens for x in batch_chunk]
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multipliers = [x.multipliers for x in batch_chunk]
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self.hijack.fixes = [x.fixes for x in batch_chunk]
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for fixes in self.hijack.fixes:
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for _position, embedding in fixes:
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used_embeddings[embedding.name] = embedding
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devices.torch_npu_set_device()
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z = self.process_tokens(tokens, multipliers)
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zs.append(z)
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if opts.textual_inversion_add_hashes_to_infotext and used_embeddings:
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hashes = []
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for name, embedding in used_embeddings.items():
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shorthash = embedding.shorthash
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if not shorthash:
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continue
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name = name.replace(":", "").replace(",", "")
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hashes.append(f"{name}: {shorthash}")
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if hashes:
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if self.hijack.extra_generation_params.get("TI hashes"):
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hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
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self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
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if any(x for x in texts if "(" in x or "[" in x) and opts.emphasis != "Original":
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self.hijack.extra_generation_params["Emphasis"] = opts.emphasis
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if self.return_pooled:
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return torch.hstack(zs), zs[0].pooled
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else:
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return torch.hstack(zs)
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def process_tokens(self, remade_batch_tokens, batch_multipliers):
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"""
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sends one single prompt chunk to be encoded by transformers neural network.
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remade_batch_tokens is a batch of tokens - a list, where every element is a list of tokens; usually
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there are exactly 77 tokens in the list. batch_multipliers is the same but for multipliers instead of tokens.
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Multipliers are used to give more or less weight to the outputs of transformers network. Each multiplier
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corresponds to one token.
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"""
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tokens = torch.asarray(remade_batch_tokens).to(devices.device)
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# this is for SD2: SD1 uses the same token for padding and end of text, while SD2 uses different ones.
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if self.id_end != self.id_pad:
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for batch_pos in range(len(remade_batch_tokens)):
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index = remade_batch_tokens[batch_pos].index(self.id_end)
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tokens[batch_pos, index+1:tokens.shape[1]] = self.id_pad
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z = self.encode_with_transformers(tokens)
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pooled = getattr(z, 'pooled', None)
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emphasis = sd_emphasis.get_current_option(opts.emphasis)()
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emphasis.tokens = remade_batch_tokens
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emphasis.multipliers = torch.asarray(batch_multipliers).to(devices.device)
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emphasis.z = z
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emphasis.after_transformers()
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z = emphasis.z
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if pooled is not None:
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z.pooled = pooled
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return z
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class FrozenCLIPEmbedderWithCustomWordsBase(TextConditionalModel):
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"""A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
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have unlimited prompt length and assign weights to tokens in prompt.
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"""
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def __init__(self, wrapped, hijack):
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super().__init__()
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self.hijack = hijack
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self.wrapped = wrapped
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"""Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
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depending on model."""
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self.is_trainable = getattr(wrapped, 'is_trainable', False)
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self.input_key = getattr(wrapped, 'input_key', 'txt')
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self.return_pooled = getattr(self.wrapped, 'return_pooled', False)
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self.legacy_ucg_val = None # for sgm codebase
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def forward(self, texts):
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if opts.use_old_emphasis_implementation:
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import modules.sd_hijack_clip_old
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return modules.sd_hijack_clip_old.forward_old(self, texts)
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return super().forward(texts)
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class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
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def __init__(self, wrapped, hijack):
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super().__init__(wrapped, hijack)
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self.tokenizer = wrapped.tokenizer
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vocab = self.tokenizer.get_vocab()
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self.comma_token = vocab.get(',</w>', None)
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self.token_mults = {}
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tokens_with_parens = [(k, v) for k, v in vocab.items() if '(' in k or ')' in k or '[' in k or ']' in k]
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for text, ident in tokens_with_parens:
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mult = 1.0
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for c in text:
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if c == '[':
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mult /= 1.1
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if c == ']':
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mult *= 1.1
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if c == '(':
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mult *= 1.1
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if c == ')':
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mult /= 1.1
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if mult != 1.0:
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self.token_mults[ident] = mult
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self.id_start = self.wrapped.tokenizer.bos_token_id
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self.id_end = self.wrapped.tokenizer.eos_token_id
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self.id_pad = self.id_end
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def tokenize(self, texts):
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tokenized = self.wrapped.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
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return tokenized
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def encode_with_transformers(self, tokens):
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outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
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if opts.CLIP_stop_at_last_layers > 1:
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z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
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z = self.wrapped.transformer.text_model.final_layer_norm(z)
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else:
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z = outputs.last_hidden_state
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return z
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def encode_embedding_init_text(self, init_text, nvpt):
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embedding_layer = self.wrapped.transformer.text_model.embeddings
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ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
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embedded = embedding_layer.token_embedding.wrapped(ids.to(embedding_layer.token_embedding.wrapped.weight.device)).squeeze(0)
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return embedded
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class FrozenCLIPEmbedderForSDXLWithCustomWords(FrozenCLIPEmbedderWithCustomWords):
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def __init__(self, wrapped, hijack):
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super().__init__(wrapped, hijack)
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def encode_with_transformers(self, tokens):
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outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=self.wrapped.layer == "hidden")
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if opts.sdxl_clip_l_skip is True:
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z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
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elif self.wrapped.layer == "last":
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z = outputs.last_hidden_state
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else:
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z = outputs.hidden_states[self.wrapped.layer_idx]
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return z
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# import math
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# from collections import namedtuple
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#
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# import torch
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#
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# from modules import prompt_parser, devices, sd_hijack, sd_emphasis
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# from modules.shared import opts
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#
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#
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# class PromptChunk:
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# """
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# This object contains token ids, weight (multipliers:1.4) and textual inversion embedding info for a chunk of prompt.
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# If a prompt is short, it is represented by one PromptChunk, otherwise, multiple are necessary.
|
||||
# Each PromptChunk contains an exact amount of tokens - 77, which includes one for start and end token,
|
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# so just 75 tokens from prompt.
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# """
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#
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# def __init__(self):
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# self.tokens = []
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# self.multipliers = []
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# self.fixes = []
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#
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#
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# PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])
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# """An object of this type is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt
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# chunk. Those objects are found in PromptChunk.fixes and, are placed into FrozenCLIPEmbedderWithCustomWordsBase.hijack.fixes, and finally
|
||||
# are applied by sd_hijack.EmbeddingsWithFixes's forward function."""
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||||
#
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||||
#
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# class TextConditionalModel(torch.nn.Module):
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# def __init__(self):
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# super().__init__()
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#
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# self.hijack = sd_hijack.model_hijack
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# self.chunk_length = 75
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#
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# self.is_trainable = False
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# self.input_key = 'txt'
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# self.return_pooled = False
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||||
#
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||||
# self.comma_token = None
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||||
# self.id_start = None
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||||
# self.id_end = None
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||||
# self.id_pad = None
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||||
#
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# def empty_chunk(self):
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||||
# """creates an empty PromptChunk and returns it"""
|
||||
#
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||||
# chunk = PromptChunk()
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||||
# chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1)
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||||
# chunk.multipliers = [1.0] * (self.chunk_length + 2)
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||||
# return chunk
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||||
#
|
||||
# def get_target_prompt_token_count(self, token_count):
|
||||
# """returns the maximum number of tokens a prompt of a known length can have before it requires one more PromptChunk to be represented"""
|
||||
#
|
||||
# return math.ceil(max(token_count, 1) / self.chunk_length) * self.chunk_length
|
||||
#
|
||||
# def tokenize(self, texts):
|
||||
# """Converts a batch of texts into a batch of token ids"""
|
||||
#
|
||||
# raise NotImplementedError
|
||||
#
|
||||
# def encode_with_transformers(self, tokens):
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||||
# """
|
||||
# converts a batch of token ids (in python lists) into a single tensor with numeric representation of those tokens;
|
||||
# All python lists with tokens are assumed to have same length, usually 77.
|
||||
# if input is a list with B elements and each element has T tokens, expected output shape is (B, T, C), where C depends on
|
||||
# model - can be 768 and 1024.
|
||||
# Among other things, this call will read self.hijack.fixes, apply it to its inputs, and clear it (setting it to None).
|
||||
# """
|
||||
#
|
||||
# raise NotImplementedError
|
||||
#
|
||||
# def encode_embedding_init_text(self, init_text, nvpt):
|
||||
# """Converts text into a tensor with this text's tokens' embeddings. Note that those are embeddings before they are passed through
|
||||
# transformers. nvpt is used as a maximum length in tokens. If text produces less teokens than nvpt, only this many is returned."""
|
||||
#
|
||||
# raise NotImplementedError
|
||||
#
|
||||
# def tokenize_line(self, line):
|
||||
# """
|
||||
# this transforms a single prompt into a list of PromptChunk objects - as many as needed to
|
||||
# represent the prompt.
|
||||
# Returns the list and the total number of tokens in the prompt.
|
||||
# """
|
||||
#
|
||||
# if opts.emphasis != "None":
|
||||
# parsed = prompt_parser.parse_prompt_attention(line)
|
||||
# else:
|
||||
# parsed = [[line, 1.0]]
|
||||
#
|
||||
# tokenized = self.tokenize([text for text, _ in parsed])
|
||||
#
|
||||
# chunks = []
|
||||
# chunk = PromptChunk()
|
||||
# token_count = 0
|
||||
# last_comma = -1
|
||||
#
|
||||
# def next_chunk(is_last=False):
|
||||
# """puts current chunk into the list of results and produces the next one - empty;
|
||||
# if is_last is true, tokens <end-of-text> tokens at the end won't add to token_count"""
|
||||
# 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]
|
||||
#
|
||||
# if token == self.comma_token:
|
||||
# last_comma = len(chunk.tokens)
|
||||
#
|
||||
# # this is when we are at the end of allotted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack
|
||||
# # is a setting that specifies that if there is a comma nearby, the text after the comma should be moved out of this chunk and into the next.
|
||||
# elif opts.comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= opts.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.hijack.embedding_db.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):
|
||||
# """
|
||||
# Accepts a list of texts and calls tokenize_line() on each, with cache. Returns the list of results and maximum
|
||||
# length, in tokens, of all 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 forward(self, texts):
|
||||
# """
|
||||
# Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts.
|
||||
# Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will
|
||||
# be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, for SD2 it's 1024, and for SDXL it's 1280.
|
||||
# An example shape returned by this function can be: (2, 77, 768).
|
||||
# For SDXL, instead of returning one tensor avobe, it returns a tuple with two: the other one with shape (B, 1280) with pooled values.
|
||||
# Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one element
|
||||
# is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
|
||||
# """
|
||||
#
|
||||
# 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.hijack.fixes = [x.fixes for x in batch_chunk]
|
||||
#
|
||||
# for fixes in self.hijack.fixes:
|
||||
# for _position, embedding in fixes:
|
||||
# used_embeddings[embedding.name] = embedding
|
||||
# devices.torch_npu_set_device()
|
||||
# z = self.process_tokens(tokens, multipliers)
|
||||
# zs.append(z)
|
||||
#
|
||||
# if opts.textual_inversion_add_hashes_to_infotext and used_embeddings:
|
||||
# hashes = []
|
||||
# for name, embedding in used_embeddings.items():
|
||||
# shorthash = embedding.shorthash
|
||||
# if not shorthash:
|
||||
# continue
|
||||
#
|
||||
# name = name.replace(":", "").replace(",", "")
|
||||
# hashes.append(f"{name}: {shorthash}")
|
||||
#
|
||||
# if hashes:
|
||||
# if self.hijack.extra_generation_params.get("TI hashes"):
|
||||
# hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
|
||||
# self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
|
||||
#
|
||||
# if any(x for x in texts if "(" in x or "[" in x) and opts.emphasis != "Original":
|
||||
# self.hijack.extra_generation_params["Emphasis"] = opts.emphasis
|
||||
#
|
||||
# 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):
|
||||
# """
|
||||
# sends one single prompt chunk to be encoded by transformers neural network.
|
||||
# remade_batch_tokens is a batch of tokens - a list, where every element is a list of tokens; usually
|
||||
# there are exactly 77 tokens in the list. batch_multipliers is the same but for multipliers instead of tokens.
|
||||
# Multipliers are used to give more or less weight to the outputs of transformers network. Each multiplier
|
||||
# corresponds to one token.
|
||||
# """
|
||||
# tokens = torch.asarray(remade_batch_tokens).to(devices.device)
|
||||
#
|
||||
# # this is for SD2: SD1 uses the same token for padding and end of text, while SD2 uses different ones.
|
||||
# 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)
|
||||
#
|
||||
# emphasis = sd_emphasis.get_current_option(opts.emphasis)()
|
||||
# emphasis.tokens = remade_batch_tokens
|
||||
# emphasis.multipliers = torch.asarray(batch_multipliers).to(devices.device)
|
||||
# emphasis.z = z
|
||||
#
|
||||
# emphasis.after_transformers()
|
||||
#
|
||||
# z = emphasis.z
|
||||
#
|
||||
# if pooled is not None:
|
||||
# z.pooled = pooled
|
||||
#
|
||||
# return z
|
||||
#
|
||||
#
|
||||
# class FrozenCLIPEmbedderWithCustomWordsBase(TextConditionalModel):
|
||||
# """A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
|
||||
# have unlimited prompt length and assign weights to tokens in prompt.
|
||||
# """
|
||||
#
|
||||
# def __init__(self, wrapped, hijack):
|
||||
# super().__init__()
|
||||
#
|
||||
# self.hijack = hijack
|
||||
#
|
||||
# self.wrapped = wrapped
|
||||
# """Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
|
||||
# depending on model."""
|
||||
#
|
||||
# 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)
|
||||
#
|
||||
# self.legacy_ucg_val = None # for sgm codebase
|
||||
#
|
||||
# def forward(self, texts):
|
||||
# if opts.use_old_emphasis_implementation:
|
||||
# import modules.sd_hijack_clip_old
|
||||
# return modules.sd_hijack_clip_old.forward_old(self, texts)
|
||||
#
|
||||
# return super().forward(texts)
|
||||
#
|
||||
#
|
||||
# class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
|
||||
# def __init__(self, wrapped, hijack):
|
||||
# super().__init__(wrapped, hijack)
|
||||
# self.tokenizer = wrapped.tokenizer
|
||||
#
|
||||
# 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
|
||||
#
|
||||
# self.id_start = self.wrapped.tokenizer.bos_token_id
|
||||
# self.id_end = self.wrapped.tokenizer.eos_token_id
|
||||
# self.id_pad = self.id_end
|
||||
#
|
||||
# def tokenize(self, texts):
|
||||
# tokenized = self.wrapped.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
|
||||
#
|
||||
# return tokenized
|
||||
#
|
||||
# def encode_with_transformers(self, tokens):
|
||||
# outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
|
||||
#
|
||||
# if opts.CLIP_stop_at_last_layers > 1:
|
||||
# z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
|
||||
# z = self.wrapped.transformer.text_model.final_layer_norm(z)
|
||||
# else:
|
||||
# z = outputs.last_hidden_state
|
||||
#
|
||||
# return z
|
||||
#
|
||||
# def encode_embedding_init_text(self, init_text, nvpt):
|
||||
# embedding_layer = self.wrapped.transformer.text_model.embeddings
|
||||
# ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
|
||||
# embedded = embedding_layer.token_embedding.wrapped(ids.to(embedding_layer.token_embedding.wrapped.weight.device)).squeeze(0)
|
||||
#
|
||||
# return embedded
|
||||
#
|
||||
#
|
||||
# class FrozenCLIPEmbedderForSDXLWithCustomWords(FrozenCLIPEmbedderWithCustomWords):
|
||||
# def __init__(self, wrapped, hijack):
|
||||
# super().__init__(wrapped, hijack)
|
||||
#
|
||||
# def encode_with_transformers(self, tokens):
|
||||
# outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=self.wrapped.layer == "hidden")
|
||||
#
|
||||
# if opts.sdxl_clip_l_skip is True:
|
||||
# z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
|
||||
# elif self.wrapped.layer == "last":
|
||||
# z = outputs.last_hidden_state
|
||||
# else:
|
||||
# z = outputs.hidden_states[self.wrapped.layer_idx]
|
||||
#
|
||||
# return z
|
||||
|
||||
@@ -1,82 +1,82 @@
|
||||
from modules import sd_hijack_clip
|
||||
from modules import shared
|
||||
|
||||
|
||||
def process_text_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):
|
||||
id_start = self.id_start
|
||||
id_end = self.id_end
|
||||
maxlen = self.wrapped.max_length # you get to stay at 77
|
||||
used_custom_terms = []
|
||||
remade_batch_tokens = []
|
||||
hijack_comments = []
|
||||
hijack_fixes = []
|
||||
token_count = 0
|
||||
|
||||
cache = {}
|
||||
batch_tokens = self.tokenize(texts)
|
||||
batch_multipliers = []
|
||||
for tokens in batch_tokens:
|
||||
tuple_tokens = tuple(tokens)
|
||||
|
||||
if tuple_tokens in cache:
|
||||
remade_tokens, fixes, multipliers = cache[tuple_tokens]
|
||||
else:
|
||||
fixes = []
|
||||
remade_tokens = []
|
||||
multipliers = []
|
||||
mult = 1.0
|
||||
|
||||
i = 0
|
||||
while i < len(tokens):
|
||||
token = tokens[i]
|
||||
|
||||
embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
|
||||
|
||||
mult_change = self.token_mults.get(token) if shared.opts.emphasis != "None" else None
|
||||
if mult_change is not None:
|
||||
mult *= mult_change
|
||||
i += 1
|
||||
elif embedding is None:
|
||||
remade_tokens.append(token)
|
||||
multipliers.append(mult)
|
||||
i += 1
|
||||
else:
|
||||
emb_len = int(embedding.vec.shape[0])
|
||||
fixes.append((len(remade_tokens), embedding))
|
||||
remade_tokens += [0] * emb_len
|
||||
multipliers += [mult] * emb_len
|
||||
used_custom_terms.append((embedding.name, embedding.checksum()))
|
||||
i += embedding_length_in_tokens
|
||||
|
||||
if len(remade_tokens) > maxlen - 2:
|
||||
vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
|
||||
ovf = remade_tokens[maxlen - 2:]
|
||||
overflowing_words = [vocab.get(int(x), "") for x in ovf]
|
||||
overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
|
||||
hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
|
||||
|
||||
token_count = len(remade_tokens)
|
||||
remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
|
||||
remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
|
||||
cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
|
||||
|
||||
multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
|
||||
multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
|
||||
|
||||
remade_batch_tokens.append(remade_tokens)
|
||||
hijack_fixes.append(fixes)
|
||||
batch_multipliers.append(multipliers)
|
||||
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
|
||||
|
||||
|
||||
def forward_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):
|
||||
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = process_text_old(self, texts)
|
||||
|
||||
self.hijack.comments += hijack_comments
|
||||
|
||||
if used_custom_terms:
|
||||
embedding_names = ", ".join(f"{word} [{checksum}]" for word, checksum in used_custom_terms)
|
||||
self.hijack.comments.append(f"Used embeddings: {embedding_names}")
|
||||
|
||||
self.hijack.fixes = hijack_fixes
|
||||
return self.process_tokens(remade_batch_tokens, batch_multipliers)
|
||||
# from modules import sd_hijack_clip
|
||||
# from modules import shared
|
||||
#
|
||||
#
|
||||
# def process_text_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):
|
||||
# id_start = self.id_start
|
||||
# id_end = self.id_end
|
||||
# maxlen = self.wrapped.max_length # you get to stay at 77
|
||||
# used_custom_terms = []
|
||||
# remade_batch_tokens = []
|
||||
# hijack_comments = []
|
||||
# hijack_fixes = []
|
||||
# token_count = 0
|
||||
#
|
||||
# cache = {}
|
||||
# batch_tokens = self.tokenize(texts)
|
||||
# batch_multipliers = []
|
||||
# for tokens in batch_tokens:
|
||||
# tuple_tokens = tuple(tokens)
|
||||
#
|
||||
# if tuple_tokens in cache:
|
||||
# remade_tokens, fixes, multipliers = cache[tuple_tokens]
|
||||
# else:
|
||||
# fixes = []
|
||||
# remade_tokens = []
|
||||
# multipliers = []
|
||||
# mult = 1.0
|
||||
#
|
||||
# i = 0
|
||||
# while i < len(tokens):
|
||||
# token = tokens[i]
|
||||
#
|
||||
# embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
|
||||
#
|
||||
# mult_change = self.token_mults.get(token) if shared.opts.emphasis != "None" else None
|
||||
# if mult_change is not None:
|
||||
# mult *= mult_change
|
||||
# i += 1
|
||||
# elif embedding is None:
|
||||
# remade_tokens.append(token)
|
||||
# multipliers.append(mult)
|
||||
# i += 1
|
||||
# else:
|
||||
# emb_len = int(embedding.vec.shape[0])
|
||||
# fixes.append((len(remade_tokens), embedding))
|
||||
# remade_tokens += [0] * emb_len
|
||||
# multipliers += [mult] * emb_len
|
||||
# used_custom_terms.append((embedding.name, embedding.checksum()))
|
||||
# i += embedding_length_in_tokens
|
||||
#
|
||||
# if len(remade_tokens) > maxlen - 2:
|
||||
# vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
|
||||
# ovf = remade_tokens[maxlen - 2:]
|
||||
# overflowing_words = [vocab.get(int(x), "") for x in ovf]
|
||||
# overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
|
||||
# hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
|
||||
#
|
||||
# token_count = len(remade_tokens)
|
||||
# remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
|
||||
# remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
|
||||
# cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
|
||||
#
|
||||
# multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
|
||||
# multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
|
||||
#
|
||||
# remade_batch_tokens.append(remade_tokens)
|
||||
# hijack_fixes.append(fixes)
|
||||
# batch_multipliers.append(multipliers)
|
||||
# return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
|
||||
#
|
||||
#
|
||||
# def forward_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):
|
||||
# batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = process_text_old(self, texts)
|
||||
#
|
||||
# self.hijack.comments += hijack_comments
|
||||
#
|
||||
# if used_custom_terms:
|
||||
# embedding_names = ", ".join(f"{word} [{checksum}]" for word, checksum in used_custom_terms)
|
||||
# self.hijack.comments.append(f"Used embeddings: {embedding_names}")
|
||||
#
|
||||
# self.hijack.fixes = hijack_fixes
|
||||
# return self.process_tokens(remade_batch_tokens, batch_multipliers)
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
import os.path
|
||||
|
||||
|
||||
def should_hijack_ip2p(checkpoint_info):
|
||||
from modules import sd_models_config
|
||||
|
||||
ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
|
||||
cfg_basename = os.path.basename(sd_models_config.find_checkpoint_config_near_filename(checkpoint_info)).lower()
|
||||
|
||||
return "pix2pix" in ckpt_basename and "pix2pix" not in cfg_basename
|
||||
# import os.path
|
||||
#
|
||||
#
|
||||
# def should_hijack_ip2p(checkpoint_info):
|
||||
# from modules import sd_models_config
|
||||
#
|
||||
# ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
|
||||
# cfg_basename = os.path.basename(sd_models_config.find_checkpoint_config_near_filename(checkpoint_info)).lower()
|
||||
#
|
||||
# return "pix2pix" in ckpt_basename and "pix2pix" not in cfg_basename
|
||||
|
||||
@@ -1,71 +1,71 @@
|
||||
import open_clip.tokenizer
|
||||
import torch
|
||||
|
||||
from modules import sd_hijack_clip, devices
|
||||
from modules.shared import opts
|
||||
|
||||
tokenizer = open_clip.tokenizer._tokenizer
|
||||
|
||||
|
||||
class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
|
||||
def __init__(self, wrapped, hijack):
|
||||
super().__init__(wrapped, hijack)
|
||||
|
||||
self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
|
||||
self.id_start = tokenizer.encoder["<start_of_text>"]
|
||||
self.id_end = tokenizer.encoder["<end_of_text>"]
|
||||
self.id_pad = 0
|
||||
|
||||
def tokenize(self, texts):
|
||||
assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
|
||||
|
||||
tokenized = [tokenizer.encode(text) for text in texts]
|
||||
|
||||
return tokenized
|
||||
|
||||
def encode_with_transformers(self, tokens):
|
||||
# set self.wrapped.layer_idx here according to opts.CLIP_stop_at_last_layers
|
||||
z = self.wrapped.encode_with_transformer(tokens)
|
||||
|
||||
return z
|
||||
|
||||
def encode_embedding_init_text(self, init_text, nvpt):
|
||||
ids = tokenizer.encode(init_text)
|
||||
ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
|
||||
embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
|
||||
|
||||
return embedded
|
||||
|
||||
|
||||
class FrozenOpenCLIPEmbedder2WithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
|
||||
def __init__(self, wrapped, hijack):
|
||||
super().__init__(wrapped, hijack)
|
||||
|
||||
self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
|
||||
self.id_start = tokenizer.encoder["<start_of_text>"]
|
||||
self.id_end = tokenizer.encoder["<end_of_text>"]
|
||||
self.id_pad = 0
|
||||
|
||||
def tokenize(self, texts):
|
||||
assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
|
||||
|
||||
tokenized = [tokenizer.encode(text) for text in texts]
|
||||
|
||||
return tokenized
|
||||
|
||||
def encode_with_transformers(self, tokens):
|
||||
d = self.wrapped.encode_with_transformer(tokens)
|
||||
z = d[self.wrapped.layer]
|
||||
|
||||
pooled = d.get("pooled")
|
||||
if pooled is not None:
|
||||
z.pooled = pooled
|
||||
|
||||
return z
|
||||
|
||||
def encode_embedding_init_text(self, init_text, nvpt):
|
||||
ids = tokenizer.encode(init_text)
|
||||
ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
|
||||
embedded = self.wrapped.model.token_embedding.wrapped(ids.to(self.wrapped.model.token_embedding.wrapped.weight.device)).squeeze(0)
|
||||
|
||||
return embedded
|
||||
# import open_clip.tokenizer
|
||||
# import torch
|
||||
#
|
||||
# from modules import sd_hijack_clip, devices
|
||||
# from modules.shared import opts
|
||||
#
|
||||
# tokenizer = open_clip.tokenizer._tokenizer
|
||||
#
|
||||
#
|
||||
# class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
|
||||
# def __init__(self, wrapped, hijack):
|
||||
# super().__init__(wrapped, hijack)
|
||||
#
|
||||
# self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
|
||||
# self.id_start = tokenizer.encoder["<start_of_text>"]
|
||||
# self.id_end = tokenizer.encoder["<end_of_text>"]
|
||||
# self.id_pad = 0
|
||||
#
|
||||
# def tokenize(self, texts):
|
||||
# assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
|
||||
#
|
||||
# tokenized = [tokenizer.encode(text) for text in texts]
|
||||
#
|
||||
# return tokenized
|
||||
#
|
||||
# def encode_with_transformers(self, tokens):
|
||||
# # set self.wrapped.layer_idx here according to opts.CLIP_stop_at_last_layers
|
||||
# z = self.wrapped.encode_with_transformer(tokens)
|
||||
#
|
||||
# return z
|
||||
#
|
||||
# def encode_embedding_init_text(self, init_text, nvpt):
|
||||
# ids = tokenizer.encode(init_text)
|
||||
# ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
|
||||
# embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
|
||||
#
|
||||
# return embedded
|
||||
#
|
||||
#
|
||||
# class FrozenOpenCLIPEmbedder2WithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
|
||||
# def __init__(self, wrapped, hijack):
|
||||
# super().__init__(wrapped, hijack)
|
||||
#
|
||||
# self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
|
||||
# self.id_start = tokenizer.encoder["<start_of_text>"]
|
||||
# self.id_end = tokenizer.encoder["<end_of_text>"]
|
||||
# self.id_pad = 0
|
||||
#
|
||||
# def tokenize(self, texts):
|
||||
# assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
|
||||
#
|
||||
# tokenized = [tokenizer.encode(text) for text in texts]
|
||||
#
|
||||
# return tokenized
|
||||
#
|
||||
# def encode_with_transformers(self, tokens):
|
||||
# d = self.wrapped.encode_with_transformer(tokens)
|
||||
# z = d[self.wrapped.layer]
|
||||
#
|
||||
# pooled = d.get("pooled")
|
||||
# if pooled is not None:
|
||||
# z.pooled = pooled
|
||||
#
|
||||
# return z
|
||||
#
|
||||
# def encode_embedding_init_text(self, init_text, nvpt):
|
||||
# ids = tokenizer.encode(init_text)
|
||||
# ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
|
||||
# embedded = self.wrapped.model.token_embedding.wrapped(ids.to(self.wrapped.model.token_embedding.wrapped.weight.device)).squeeze(0)
|
||||
#
|
||||
# return embedded
|
||||
|
||||
@@ -1,32 +1,32 @@
|
||||
import torch
|
||||
|
||||
from modules import sd_hijack_clip, devices
|
||||
|
||||
|
||||
class FrozenXLMREmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords):
|
||||
def __init__(self, wrapped, hijack):
|
||||
super().__init__(wrapped, hijack)
|
||||
|
||||
self.id_start = wrapped.config.bos_token_id
|
||||
self.id_end = wrapped.config.eos_token_id
|
||||
self.id_pad = wrapped.config.pad_token_id
|
||||
|
||||
self.comma_token = self.tokenizer.get_vocab().get(',', None) # alt diffusion doesn't have </w> bits for comma
|
||||
|
||||
def encode_with_transformers(self, tokens):
|
||||
# there's no CLIP Skip here because all hidden layers have size of 1024 and the last one uses a
|
||||
# trained layer to transform those 1024 into 768 for unet; so you can't choose which transformer
|
||||
# layer to work with - you have to use the last
|
||||
|
||||
attention_mask = (tokens != self.id_pad).to(device=tokens.device, dtype=torch.int64)
|
||||
features = self.wrapped(input_ids=tokens, attention_mask=attention_mask)
|
||||
z = features['projection_state']
|
||||
|
||||
return z
|
||||
|
||||
def encode_embedding_init_text(self, init_text, nvpt):
|
||||
embedding_layer = self.wrapped.roberta.embeddings
|
||||
ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
|
||||
embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
|
||||
|
||||
return embedded
|
||||
# import torch
|
||||
#
|
||||
# from modules import sd_hijack_clip, devices
|
||||
#
|
||||
#
|
||||
# class FrozenXLMREmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords):
|
||||
# def __init__(self, wrapped, hijack):
|
||||
# super().__init__(wrapped, hijack)
|
||||
#
|
||||
# self.id_start = wrapped.config.bos_token_id
|
||||
# self.id_end = wrapped.config.eos_token_id
|
||||
# self.id_pad = wrapped.config.pad_token_id
|
||||
#
|
||||
# self.comma_token = self.tokenizer.get_vocab().get(',', None) # alt diffusion doesn't have </w> bits for comma
|
||||
#
|
||||
# def encode_with_transformers(self, tokens):
|
||||
# # there's no CLIP Skip here because all hidden layers have size of 1024 and the last one uses a
|
||||
# # trained layer to transform those 1024 into 768 for unet; so you can't choose which transformer
|
||||
# # layer to work with - you have to use the last
|
||||
#
|
||||
# attention_mask = (tokens != self.id_pad).to(device=tokens.device, dtype=torch.int64)
|
||||
# features = self.wrapped(input_ids=tokens, attention_mask=attention_mask)
|
||||
# z = features['projection_state']
|
||||
#
|
||||
# return z
|
||||
#
|
||||
# def encode_embedding_init_text(self, init_text, nvpt):
|
||||
# embedding_layer = self.wrapped.roberta.embeddings
|
||||
# ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
|
||||
# embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
|
||||
#
|
||||
# return embedded
|
||||
|
||||
Reference in New Issue
Block a user