from cmath import log
import torch
from torch import nn
import sys
from ldm.data.personalized import per_img_token_list
from transformers import CLIPTokenizer
from functools import partial
DEFAULT_PLACEHOLDER_TOKEN = ['*']
PROGRESSIVE_SCALE = 2000
def get_clip_token_for_string(tokenizer, string):
batch_encoding = tokenizer(
string,
truncation=True,
max_length=77,
return_length=True,
return_overflowing_tokens=False,
padding='max_length',
return_tensors='pt',
)
tokens = batch_encoding['input_ids']
""" assert (
torch.count_nonzero(tokens - 49407) == 2
), f"String '{string}' maps to more than a single token. Please use another string" """
return tokens[0, 1]
def get_bert_token_for_string(tokenizer, string):
token = tokenizer(string)
# assert torch.count_nonzero(token) == 3, f"String '{string}' maps to more than a single token. Please use another string"
token = token[0, 1]
return token
def get_embedding_for_clip_token(embedder, token):
return embedder(token.unsqueeze(0))[0, 0]
class EmbeddingManager(nn.Module):
def __init__(
self,
embedder,
placeholder_strings=None,
initializer_words=None,
per_image_tokens=False,
num_vectors_per_token=1,
progressive_words=False,
**kwargs,
):
super().__init__()
self.embedder = embedder
self.string_to_token_dict = {}
self.string_to_param_dict = nn.ParameterDict()
self.initial_embeddings = (
nn.ParameterDict()
) # These should not be optimized
self.progressive_words = progressive_words
self.progressive_counter = 0
self.max_vectors_per_token = num_vectors_per_token
if hasattr(
embedder, 'tokenizer'
): # using Stable Diffusion's CLIP encoder
self.is_clip = True
get_token_for_string = partial(
get_clip_token_for_string, embedder.tokenizer
)
get_embedding_for_tkn = partial(
get_embedding_for_clip_token,
embedder.transformer.text_model.embeddings,
)
# per bug report #572
#token_dim = 1280
token_dim = 768
else: # using LDM's BERT encoder
self.is_clip = False
get_token_for_string = partial(
get_bert_token_for_string, embedder.tknz_fn
)
get_embedding_for_tkn = embedder.transformer.token_emb
token_dim = 1280
if per_image_tokens:
placeholder_strings.extend(per_img_token_list)
for idx, placeholder_string in enumerate(placeholder_strings):
token = get_token_for_string(placeholder_string)
if initializer_words and idx < len(initializer_words):
init_word_token = get_token_for_string(initializer_words[idx])
with torch.no_grad():
init_word_embedding = get_embedding_for_tkn(
init_word_token.cpu()
)
token_params = torch.nn.Parameter(
init_word_embedding.unsqueeze(0).repeat(
num_vectors_per_token, 1
),
requires_grad=True,
)
self.initial_embeddings[
placeholder_string
] = torch.nn.Parameter(
init_word_embedding.unsqueeze(0).repeat(
num_vectors_per_token, 1
),
requires_grad=False,
)
else:
token_params = torch.nn.Parameter(
torch.rand(
size=(num_vectors_per_token, token_dim),
requires_grad=True,
)
)
self.string_to_token_dict[placeholder_string] = token
self.string_to_param_dict[placeholder_string] = token_params
def forward(
self,
tokenized_text,
embedded_text,
):
b, n, device = *tokenized_text.shape, tokenized_text.device
for (
placeholder_string,
placeholder_token,
) in self.string_to_token_dict.items():
placeholder_embedding = self.string_to_param_dict[
placeholder_string
].to(device)
if (
self.max_vectors_per_token == 1
): # If there's only one vector per token, we can do a simple replacement
placeholder_idx = torch.where(
tokenized_text == placeholder_token.to(device)
)
embedded_text[placeholder_idx] = placeholder_embedding
else: # otherwise, need to insert and keep track of changing indices
if self.progressive_words:
self.progressive_counter += 1
max_step_tokens = (
1 + self.progressive_counter // PROGRESSIVE_SCALE
)
else:
max_step_tokens = self.max_vectors_per_token
num_vectors_for_token = min(
placeholder_embedding.shape[0], max_step_tokens
)
if torch.cuda.is_available():
placeholder_rows, placeholder_cols = torch.where(
tokenized_text == placeholder_token.to(device)
)
else:
placeholder_rows, placeholder_cols = torch.where(
tokenized_text == placeholder_token
)
if placeholder_rows.nelement() == 0:
continue
sorted_cols, sort_idx = torch.sort(
placeholder_cols, descending=True
)
sorted_rows = placeholder_rows[sort_idx]
for idx in range(len(sorted_rows)):
row = sorted_rows[idx]
col = sorted_cols[idx]
new_token_row = torch.cat(
[
tokenized_text[row][:col],
placeholder_token.repeat(num_vectors_for_token).to(
device
),
tokenized_text[row][col + 1 :],
],
axis=0,
)[:n]
new_embed_row = torch.cat(
[
embedded_text[row][:col],
placeholder_embedding[:num_vectors_for_token],
embedded_text[row][col + 1 :],
],
axis=0,
)[:n]
embedded_text[row] = new_embed_row
tokenized_text[row] = new_token_row
return embedded_text
def save(self, ckpt_path):
torch.save(
{
'string_to_token': self.string_to_token_dict,
'string_to_param': self.string_to_param_dict,
},
ckpt_path,
)
def load(self, ckpt_path, full=True):
ckpt = torch.load(ckpt_path, map_location='cpu')
# Handle .pt textual inversion files
if 'string_to_token' in ckpt and 'string_to_param' in ckpt:
self.string_to_token_dict = ckpt["string_to_token"]
self.string_to_param_dict = ckpt["string_to_param"]
# Handle .bin textual inversion files from Huggingface Concepts
# https://huggingface.co/sd-concepts-library
else:
for token_str in list(ckpt.keys()):
token = get_clip_token_for_string(self.embedder.tokenizer, token_str)
self.string_to_token_dict[token_str] = token
ckpt[token_str] = torch.nn.Parameter(ckpt[token_str])
self.string_to_param_dict.update(ckpt)
if not full:
for key, value in self.string_to_param_dict.items():
self.string_to_param_dict[key] = torch.nn.Parameter(value.half())
print(f'Added terms: {", ".join(self.string_to_param_dict.keys())}')
def get_embedding_norms_squared(self):
all_params = torch.cat(
list(self.string_to_param_dict.values()), axis=0
) # num_placeholders x embedding_dim
param_norm_squared = (all_params * all_params).sum(
axis=-1
) # num_placeholders
return param_norm_squared
def embedding_parameters(self):
return self.string_to_param_dict.parameters()
def embedding_to_coarse_loss(self):
loss = 0.0
num_embeddings = len(self.initial_embeddings)
for key in self.initial_embeddings:
optimized = self.string_to_param_dict[key]
coarse = self.initial_embeddings[key].clone().to(optimized.device)
loss = (
loss
+ (optimized - coarse)
@ (optimized - coarse).T
/ num_embeddings
)
return loss