mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
378 lines
14 KiB
Python
378 lines
14 KiB
Python
import os.path
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from cmath import log
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import torch
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from attr import dataclass
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from torch import nn
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import sys
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from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
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from ldm.data.personalized import per_img_token_list
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from transformers import CLIPTokenizer
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from functools import partial
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from picklescan.scanner import scan_file_path
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PROGRESSIVE_SCALE = 2000
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def get_clip_token_id_for_string(tokenizer: CLIPTokenizer, token_str: str) -> int:
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token_id = tokenizer.convert_tokens_to_ids(token_str)
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return token_id
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def get_embedding_for_clip_token_id(embedder, token_id):
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if type(token_id) is not torch.Tensor:
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token_id = torch.tensor(token_id, dtype=torch.int)
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return embedder(token_id.unsqueeze(0))[0, 0]
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class EmbeddingManager(nn.Module):
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def __init__(
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self,
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embedder,
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placeholder_strings=None,
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initializer_words=None,
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per_image_tokens=False,
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num_vectors_per_token=1,
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progressive_words=False,
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**kwargs,
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):
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super().__init__()
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self.embedder = embedder
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self.concepts_library=HuggingFaceConceptsLibrary()
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self.string_to_token_dict = {}
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self.string_to_param_dict = nn.ParameterDict()
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self.initial_embeddings = (
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nn.ParameterDict()
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) # These should not be optimized
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self.progressive_words = progressive_words
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self.progressive_counter = 0
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self.max_vectors_per_token = num_vectors_per_token
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if hasattr(
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embedder, 'tokenizer'
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): # using Stable Diffusion's CLIP encoder
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self.is_clip = True
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get_token_id_for_string = partial(
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get_clip_token_id_for_string, embedder.tokenizer
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)
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get_embedding_for_tkn_id = partial(
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get_embedding_for_clip_token_id,
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embedder.transformer.text_model.embeddings,
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)
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# per bug report #572
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#token_dim = 1280
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token_dim = 768
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else: # using LDM's BERT encoder
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self.is_clip = False
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get_token_id_for_string = partial(
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get_bert_token_id_for_string, embedder.tknz_fn
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)
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get_embedding_for_tkn_id = embedder.transformer.token_emb
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token_dim = 1280
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if per_image_tokens:
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placeholder_strings.extend(per_img_token_list)
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for idx, placeholder_string in enumerate(placeholder_strings):
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token_id = get_token_id_for_string(placeholder_string)
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if initializer_words and idx < len(initializer_words):
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init_word_token_id = get_token_id_for_string(initializer_words[idx])
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with torch.no_grad():
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init_word_embedding = get_embedding_for_tkn_id(init_word_token_id)
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token_params = torch.nn.Parameter(
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init_word_embedding.unsqueeze(0).repeat(
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num_vectors_per_token, 1
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),
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requires_grad=True,
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)
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self.initial_embeddings[
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placeholder_string
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] = torch.nn.Parameter(
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init_word_embedding.unsqueeze(0).repeat(
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num_vectors_per_token, 1
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),
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requires_grad=False,
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)
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else:
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token_params = torch.nn.Parameter(
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torch.rand(
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size=(num_vectors_per_token, token_dim),
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requires_grad=True,
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)
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)
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self.string_to_token_dict[placeholder_string] = token_id
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self.string_to_param_dict[placeholder_string] = token_params
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def forward(
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self,
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tokenized_text,
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embedded_text,
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):
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# torch.save(embedded_text, '/tmp/embedding-manager-uglysonic-pre-rewrite.pt')
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b, n, device = *tokenized_text.shape, tokenized_text.device
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for (
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placeholder_string,
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placeholder_token,
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) in self.string_to_token_dict.items():
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placeholder_embedding = self.string_to_param_dict[
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placeholder_string
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].to(device)
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if self.progressive_words:
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self.progressive_counter += 1
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max_step_tokens = (
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1 + self.progressive_counter // PROGRESSIVE_SCALE
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)
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else:
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max_step_tokens = self.max_vectors_per_token
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num_vectors_for_token = min(
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placeholder_embedding.shape[0], max_step_tokens
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)
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placeholder_rows, placeholder_cols = torch.where(
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tokenized_text == placeholder_token
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)
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if placeholder_rows.nelement() == 0:
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continue
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sorted_cols, sort_idx = torch.sort(
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placeholder_cols, descending=True
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)
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sorted_rows = placeholder_rows[sort_idx]
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for idx in range(sorted_rows.shape[0]):
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row = sorted_rows[idx]
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col = sorted_cols[idx]
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new_token_row = torch.cat(
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[
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tokenized_text[row][:col],
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torch.tensor([placeholder_token] * num_vectors_for_token, device=device),
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tokenized_text[row][col + 1 :],
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],
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axis=0,
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)[:n]
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new_embed_row = torch.cat(
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[
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embedded_text[row][:col],
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placeholder_embedding[:num_vectors_for_token],
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embedded_text[row][col + 1 :],
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],
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axis=0,
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)[:n]
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embedded_text[row] = new_embed_row
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tokenized_text[row] = new_token_row
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return embedded_text
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def save(self, ckpt_path):
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torch.save(
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{
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'string_to_token': self.string_to_token_dict,
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'string_to_param': self.string_to_param_dict,
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},
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ckpt_path,
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)
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def load(self, ckpt_paths, full=True):
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if len(ckpt_paths) == 0:
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return
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if type(ckpt_paths) != list:
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ckpt_paths = [ckpt_paths]
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ckpt_paths = self._expand_directories(ckpt_paths)
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for c in ckpt_paths:
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self._load(c,full)
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# remember that we know this term and don't try to download it again from the concepts library
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# note that if the concept name is also provided and different from the trigger term, they
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# both will be stored in this dictionary
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for term in self.string_to_param_dict.keys():
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term = term.strip('<').strip('>')
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self.concepts_loaded[term] = True
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print(f'>> Current embedding manager terms: {", ".join(self.string_to_param_dict.keys())}')
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def _expand_directories(self, paths:list[str]):
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expanded_paths = list()
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for path in paths:
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if os.path.isfile(path):
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expanded_paths.append(path)
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elif os.path.isdir(path):
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for root, _, files in os.walk(path):
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for name in files:
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expanded_paths.append(os.path.join(root,name))
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return [x for x in expanded_paths if os.path.splitext(x)[1] in ('.pt','.bin')]
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def _load(self, ckpt_path, full=True):
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try:
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scan_result = scan_file_path(ckpt_path)
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if scan_result.infected_files == 1:
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print(f'\n### Security Issues Found in Model: {scan_result.issues_count}')
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print('### For your safety, InvokeAI will not load this embed.')
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return
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except Exception:
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print(f"### WARNING::: Invalid or corrupt embeddings found. Ignoring: {ckpt_path}")
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return
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embedding_info = self.parse_embedding(ckpt_path)
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if embedding_info:
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self.max_vectors_per_token = embedding_info['num_vectors_per_token']
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self.add_embedding(embedding_info['name'], embedding_info['embedding'], full)
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else:
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print(f'>> Failed to load embedding located at {ckpt_path}. Unsupported file.')
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def add_embedding(self, token_str, embedding, full):
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if token_str in self.string_to_param_dict:
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print(f">> Embedding manager refusing to overwrite already-loaded term '{token_str}'")
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return
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if not full:
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embedding = embedding.half()
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if len(embedding.shape) == 1:
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embedding = embedding.unsqueeze(0)
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existing_token_id = get_clip_token_id_for_string(self.embedder.tokenizer, token_str)
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if existing_token_id == self.embedder.tokenizer.unk_token_id:
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num_tokens_added = self.embedder.tokenizer.add_tokens(token_str)
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current_embeddings = self.embedder.transformer.resize_token_embeddings(None)
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current_token_count = current_embeddings.num_embeddings
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new_token_count = current_token_count + num_tokens_added
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self.embedder.transformer.resize_token_embeddings(new_token_count)
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token_id = get_clip_token_id_for_string(self.embedder.tokenizer, token_str)
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self.string_to_token_dict[token_str] = token_id
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self.string_to_param_dict[token_str] = torch.nn.Parameter(embedding)
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def parse_embedding(self, embedding_file: str):
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file_type = embedding_file.split('.')[-1]
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if file_type == 'pt':
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return self.parse_embedding_pt(embedding_file)
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elif file_type == 'bin':
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return self.parse_embedding_bin(embedding_file)
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else:
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print(f'>> Not a recognized embedding file: {embedding_file}')
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def parse_embedding_pt(self, embedding_file):
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embedding_ckpt = torch.load(embedding_file, map_location='cpu')
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embedding_info = {}
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# Check if valid embedding file
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if 'string_to_token' and 'string_to_param' in embedding_ckpt:
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# Catch variants that do not have the expected keys or values.
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try:
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embedding_info['name'] = embedding_ckpt['name'] or os.path.basename(os.path.splitext(embedding_file)[0])
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# Check num of embeddings and warn user only the first will be used
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embedding_info['num_of_embeddings'] = len(embedding_ckpt["string_to_token"])
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if embedding_info['num_of_embeddings'] > 1:
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print('>> More than 1 embedding found. Will use the first one')
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embedding = list(embedding_ckpt['string_to_param'].values())[0]
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except (AttributeError,KeyError):
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return self.handle_broken_pt_variants(embedding_ckpt, embedding_file)
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embedding_info['embedding'] = embedding
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embedding_info['num_vectors_per_token'] = embedding.size()[0]
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embedding_info['token_dim'] = embedding.size()[1]
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try:
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embedding_info['trained_steps'] = embedding_ckpt['step']
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embedding_info['trained_model_name'] = embedding_ckpt['sd_checkpoint_name']
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embedding_info['trained_model_checksum'] = embedding_ckpt['sd_checkpoint']
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except AttributeError:
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print(">> No Training Details Found. Passing ...")
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# .pt files found at https://cyberes.github.io/stable-diffusion-textual-inversion-models/
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# They are actually .bin files
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elif len(embedding_ckpt.keys())==1:
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print('>> Detected .bin file masquerading as .pt file')
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embedding_info = self.parse_embedding_bin(embedding_file)
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else:
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print('>> Invalid embedding format')
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embedding_info = None
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return embedding_info
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def parse_embedding_bin(self, embedding_file):
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embedding_ckpt = torch.load(embedding_file, map_location='cpu')
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embedding_info = {}
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if list(embedding_ckpt.keys()) == 0:
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print(">> Invalid concepts file")
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embedding_info = None
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else:
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for token in list(embedding_ckpt.keys()):
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embedding_info['name'] = token or os.path.basename(os.path.splitext(embedding_file)[0])
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embedding_info['embedding'] = embedding_ckpt[token]
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embedding_info['num_vectors_per_token'] = 1 # All Concepts seem to default to 1
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embedding_info['token_dim'] = embedding_info['embedding'].size()[0]
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return embedding_info
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def handle_broken_pt_variants(self, embedding_ckpt:dict, embedding_file:str)->dict:
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'''
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This handles the broken .pt file variants. We only know of one at present.
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'''
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embedding_info = {}
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if isinstance(list(embedding_ckpt['string_to_token'].values())[0],torch.Tensor):
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print(f'>> Variant Embedding Detected. Parsing: {embedding_file}') # example at https://github.com/invoke-ai/InvokeAI/issues/1829
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token = list(embedding_ckpt['string_to_token'].keys())[0]
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embedding_info['name'] = os.path.basename(os.path.splitext(embedding_file)[0])
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embedding_info['embedding'] = embedding_ckpt['string_to_param'].state_dict()[token]
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embedding_info['num_vectors_per_token'] = embedding_info['embedding'].shape[0]
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embedding_info['token_dim'] = embedding_info['embedding'].size()[0]
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else:
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print('>> Invalid embedding format')
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embedding_info = None
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return embedding_info
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def has_embedding_for_token(self, token_str):
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return token_str in self.string_to_token_dict
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def get_embedding_norms_squared(self):
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all_params = torch.cat(
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list(self.string_to_param_dict.values()), axis=0
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) # num_placeholders x embedding_dim
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param_norm_squared = (all_params * all_params).sum(
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axis=-1
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) # num_placeholders
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return param_norm_squared
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def embedding_parameters(self):
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return self.string_to_param_dict.parameters()
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def embedding_to_coarse_loss(self):
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loss = 0.0
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num_embeddings = len(self.initial_embeddings)
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for key in self.initial_embeddings:
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optimized = self.string_to_param_dict[key]
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coarse = self.initial_embeddings[key].clone().to(optimized.device)
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loss = (
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loss
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+ (optimized - coarse)
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@ (optimized - coarse).T
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/ num_embeddings
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)
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return loss
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