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import torch
import torch.nn as nn
from functools import partial
import clip
from einops import rearrange, repeat
from transformers import CLIPTokenizer, CLIPTextModel,CLIPVisionModel,CLIPModel
import kornia
from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
from .xf import LayerNorm, Transformer
import math
class AbstractEncoder(nn.Module):
def __init__(self):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
class ClassEmbedder(nn.Module):
def __init__(self, embed_dim, n_classes=1000, key='class'):
super().__init__()
self.key = key
self.embedding = nn.Embedding(n_classes, embed_dim)
def forward(self, batch, key=None):
if key is None:
key = self.key
# this is for use in crossattn
c = batch[key][:, None]
c = self.embedding(c)
return c
class TransformerEmbedder(AbstractEncoder):
"""Some transformer encoder layers"""
def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
super().__init__()
self.device = device
self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
attn_layers=Encoder(dim=n_embed, depth=n_layer))
def forward(self, tokens):
tokens = tokens.to(self.device) # meh
z = self.transformer(tokens, return_embeddings=True)
return z
def encode(self, x):
return self(x)
class BERTTokenizer(AbstractEncoder):
""" Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
def __init__(self, device="cuda", vq_interface=True, max_length=77):
super().__init__()
from transformers import BertTokenizerFast # TODO: add to reuquirements
self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
self.device = device
self.vq_interface = vq_interface
self.max_length = max_length
def forward(self, text):
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
tokens = batch_encoding["input_ids"].to(self.device)
return tokens
@torch.no_grad()
def encode(self, text):
tokens = self(text)
if not self.vq_interface:
return tokens
return None, None, [None, None, tokens]
def decode(self, text):
return text
class BERTEmbedder(AbstractEncoder):
"""Uses the BERT tokenizr model and add some transformer encoder layers"""
def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
device="cuda",use_tokenizer=True, embedding_dropout=0.0):
super().__init__()
self.use_tknz_fn = use_tokenizer
if self.use_tknz_fn:
self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
self.device = device
self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
attn_layers=Encoder(dim=n_embed, depth=n_layer),
emb_dropout=embedding_dropout)
def forward(self, text):
if self.use_tknz_fn:
tokens = self.tknz_fn(text)#.to(self.device)
else:
tokens = text
z = self.transformer(tokens, return_embeddings=True)
return z
def encode(self, text):
# output of length 77
return self(text)
class SpatialRescaler(nn.Module):
def __init__(self,
n_stages=1,
method='bilinear',
multiplier=0.5,
in_channels=3,
out_channels=None,
bias=False):
super().__init__()
self.n_stages = n_stages
assert self.n_stages >= 0
assert method in ['nearest','linear','bilinear','trilinear','bicubic','area']
self.multiplier = multiplier
self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
self.remap_output = out_channels is not None
if self.remap_output:
print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.')
self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias)
def forward(self,x):
for stage in range(self.n_stages):
x = self.interpolator(x, scale_factor=self.multiplier)
if self.remap_output:
x = self.channel_mapper(x)
return x
def encode(self, x):
return self(x)
class FrozenCLIPImageEmbedder(AbstractEncoder):
"""Uses the CLIP transformer encoder for image (from Hugging Face)"""
def __init__(self, version="openai/clip-vit-large-patch14"):
super().__init__()
self.transformer = CLIPVisionModel.from_pretrained(version)
self.final_ln = LayerNorm(1024)
self.mapper = Transformer(
1,
1024,
5,
1,
)
self.freeze()
def freeze(self):
self.transformer = self.transformer.eval()
for param in self.parameters():
param.requires_grad = False
for param in self.mapper.parameters():
param.requires_grad = True
for param in self.final_ln.parameters():
param.requires_grad = True
def forward(self, image):
image = image.to('cuda')
outputs = self.transformer(pixel_values=image)
z = outputs.pooler_output
z = z.unsqueeze(1)
z = self.mapper(z)
z = self.final_ln(z)
return z
def encode(self, image):
return self(image)
class FrozenCLIPTextEmbedder(AbstractEncoder):
"""
Uses the CLIP transformer encoder for text (from Hugging Face)
"""
def __init__(self, version="openai/clip-vit-large-patch14"):
super().__init__()
self.tokenizer = CLIPTokenizer.from_pretrained(version)
self.text_model = CLIPTextModel.from_pretrained(version)
#up d_model 1024 to concat with ImageEmbedding
# self.linear_proj = nn.Linear(768, 1024)
self.final_ln = nn.LayerNorm(768)
encoder_layer = nn.TransformerEncoderLayer(
d_model=768,
nhead=8,
dim_feedforward=2048,
batch_first=True
)
self.mapper = nn.TransformerEncoder(encoder_layer, num_layers=2)
self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
self.freeze()
def freeze(self):
"""
Freezes the transformer weights while keeping
the mapper and final layer normalization trainable.
"""
self.text_model = self.text_model.eval()
for param in self.parameters():
param.requires_grad = False
for param in self.mapper.parameters():
param.requires_grad = True
for param in self.final_ln.parameters():
param.requires_grad = True
# self.linear_proj.requires_grad = True
def forward(self, text):
"""
Encodes text using the tokenizer and transformer.
Applies mapper and final layer normalization for processing.
"""
inputs = self.tokenizer(text, return_tensors="pt", padding=True, truncation=True)
inputs = {k: v.to('cuda') for k, v in inputs.items()}
outputs = self.text_model(**inputs)
z = outputs.pooler_output
# z = self.linear_proj(z) #768 -> 1024
z = z.unsqueeze(1)
z = self.mapper(z)
z = self.final_ln(z)
return z
def encode(self, text):
return self(text)
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