mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
243 lines
8.5 KiB
Python
243 lines
8.5 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
from functools import partial
|
|
import clip
|
|
from einops import rearrange, repeat
|
|
from transformers import CLIPTokenizer, CLIPTextModel
|
|
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
|
|
|
|
|
|
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
|
|
# Modified to allow to run on non-internet connected compute nodes.
|
|
# Model needs to be loaded into cache from an internet-connected machine
|
|
# by running:
|
|
# from transformers import BertTokenizerFast
|
|
# BertTokenizerFast.from_pretrained("bert-base-uncased")
|
|
try:
|
|
self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased",local_files_only=True)
|
|
except OSError:
|
|
raise SystemExit("* Couldn't load Bert tokenizer files. Try running scripts/preload_models.py from an internet-conected machine.")
|
|
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 FrozenCLIPEmbedder(AbstractEncoder):
|
|
"""Uses the CLIP transformer encoder for text (from Hugging Face)"""
|
|
def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77):
|
|
super().__init__()
|
|
self.tokenizer = CLIPTokenizer.from_pretrained(version,local_files_only=True)
|
|
self.transformer = CLIPTextModel.from_pretrained(version,local_files_only=True)
|
|
self.device = device
|
|
self.max_length = max_length
|
|
self.freeze()
|
|
|
|
def freeze(self):
|
|
self.transformer = self.transformer.eval()
|
|
for param in self.parameters():
|
|
param.requires_grad = False
|
|
|
|
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)
|
|
outputs = self.transformer(input_ids=tokens)
|
|
|
|
z = outputs.last_hidden_state
|
|
return z
|
|
|
|
def encode(self, text):
|
|
return self(text)
|
|
|
|
|
|
class FrozenCLIPTextEmbedder(nn.Module):
|
|
"""
|
|
Uses the CLIP transformer encoder for text.
|
|
"""
|
|
def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True):
|
|
super().__init__()
|
|
self.model, _ = clip.load(version, jit=False, device="cpu")
|
|
self.device = device
|
|
self.max_length = max_length
|
|
self.n_repeat = n_repeat
|
|
self.normalize = normalize
|
|
|
|
def freeze(self):
|
|
self.model = self.model.eval()
|
|
for param in self.parameters():
|
|
param.requires_grad = False
|
|
|
|
def forward(self, text):
|
|
tokens = clip.tokenize(text).to(self.device)
|
|
z = self.model.encode_text(tokens)
|
|
if self.normalize:
|
|
z = z / torch.linalg.norm(z, dim=1, keepdim=True)
|
|
return z
|
|
|
|
def encode(self, text):
|
|
z = self(text)
|
|
if z.ndim==2:
|
|
z = z[:, None, :]
|
|
z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat)
|
|
return z
|
|
|
|
|
|
class FrozenClipImageEmbedder(nn.Module):
|
|
"""
|
|
Uses the CLIP image encoder.
|
|
"""
|
|
def __init__(
|
|
self,
|
|
model,
|
|
jit=False,
|
|
device='cuda' if torch.cuda.is_available() else 'cpu',
|
|
antialias=False,
|
|
):
|
|
super().__init__()
|
|
self.model, _ = clip.load(name=model, device=device, jit=jit)
|
|
|
|
self.antialias = antialias
|
|
|
|
self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
|
|
self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
|
|
|
|
def preprocess(self, x):
|
|
# normalize to [0,1]
|
|
x = kornia.geometry.resize(x, (224, 224),
|
|
interpolation='bicubic',align_corners=True,
|
|
antialias=self.antialias)
|
|
x = (x + 1.) / 2.
|
|
# renormalize according to clip
|
|
x = kornia.enhance.normalize(x, self.mean, self.std)
|
|
return x
|
|
|
|
def forward(self, x):
|
|
# x is assumed to be in range [-1,1]
|
|
return self.model.encode_image(self.preprocess(x))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from ldm.util import count_params
|
|
model = FrozenCLIPEmbedder()
|
|
count_params(model, verbose=True)
|