let5see/IOPaint
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

add AnyText

Browse Source
This commit is contained in:
Qing
2024-01-21 23:25:50 +08:00
parent f5bd697687
commit 1905743886
17 changed files with 513 additions and 276 deletions
Download Patch File Download Diff File Expand all files Collapse all files

249
iopaint/model/anytext/ldm/modules/encoders/modules.py
View File

@@ -2,7 +2,14 @@ import torch
import torch.nn as nn
from torch.utils.checkpoint import checkpoint
from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel, AutoProcessor, CLIPVisionModelWithProjection
from transformers import (
T5Tokenizer,
T5EncoderModel,
CLIPTokenizer,
CLIPTextModel,
AutoProcessor,
CLIPVisionModelWithProjection,
)
from iopaint.model.anytext.ldm.util import count_params
@@ -18,7 +25,9 @@ def _expand_mask(mask, dtype, tgt_len=None):
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
return inverted_mask.masked_fill(
inverted_mask.to(torch.bool), torch.finfo(dtype).min
)
def _build_causal_attention_mask(bsz, seq_len, dtype):
@@ -30,6 +39,7 @@ def _build_causal_attention_mask(bsz, seq_len, dtype):
mask = mask.unsqueeze(1) # expand mask
return mask
class AbstractEncoder(nn.Module):
def __init__(self):
super().__init__()
@@ -39,13 +49,12 @@ class AbstractEncoder(nn.Module):
class IdentityEncoder(AbstractEncoder):
def encode(self, x):
return x
class ClassEmbedder(nn.Module):
def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1):
def __init__(self, embed_dim, n_classes=1000, key="class", ucg_rate=0.1):
super().__init__()
self.key = key
self.embedding = nn.Embedding(n_classes, embed_dim)
@@ -57,15 +66,17 @@ class ClassEmbedder(nn.Module):
key = self.key
# this is for use in crossattn
c = batch[key][:, None]
if self.ucg_rate > 0. and not disable_dropout:
mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
c = mask * c + (1-mask) * torch.ones_like(c)*(self.n_classes-1)
if self.ucg_rate > 0.0 and not disable_dropout:
mask = 1.0 - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
c = mask * c + (1 - mask) * torch.ones_like(c) * (self.n_classes - 1)
c = c.long()
c = self.embedding(c)
return c
def get_unconditional_conditioning(self, bs, device="cuda"):
uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
uc_class = (
self.n_classes - 1
) # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
uc = torch.ones((bs,), device=device) * uc_class
uc = {self.key: uc}
return uc
@@ -79,24 +90,34 @@ def disabled_train(self, mode=True):
class FrozenT5Embedder(AbstractEncoder):
"""Uses the T5 transformer encoder for text"""
def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
def __init__(
self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True
): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
super().__init__()
self.tokenizer = T5Tokenizer.from_pretrained(version)
self.transformer = T5EncoderModel.from_pretrained(version)
self.device = device
self.max_length = max_length # TODO: typical value?
self.max_length = max_length # TODO: typical value?
if freeze:
self.freeze()
def freeze(self):
self.transformer = self.transformer.eval()
#self.train = disabled_train
# self.train = disabled_train
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")
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)
@@ -109,13 +130,18 @@ class FrozenT5Embedder(AbstractEncoder):
class FrozenCLIPEmbedder(AbstractEncoder):
"""Uses the CLIP transformer encoder for text (from huggingface)"""
LAYERS = [
"last",
"pooled",
"hidden"
]
def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77,
freeze=True, layer="last", layer_idx=None): # clip-vit-base-patch32
LAYERS = ["last", "pooled", "hidden"]
def __init__(
self,
version="openai/clip-vit-large-patch14",
device="cuda",
max_length=77,
freeze=True,
layer="last",
layer_idx=None,
): # clip-vit-base-patch32
super().__init__()
assert layer in self.LAYERS
self.tokenizer = CLIPTokenizer.from_pretrained(version)
@@ -137,10 +163,19 @@ class FrozenCLIPEmbedder(AbstractEncoder):
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")
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, output_hidden_states=self.layer=="hidden")
outputs = self.transformer(
input_ids=tokens, output_hidden_states=self.layer == "hidden"
)
if self.layer == "last":
z = outputs.last_hidden_state
elif self.layer == "pooled":
@@ -153,77 +188,24 @@ class FrozenCLIPEmbedder(AbstractEncoder):
return self(text)
class FrozenOpenCLIPEmbedder(AbstractEncoder):
"""
Uses the OpenCLIP transformer encoder for text
"""
LAYERS = [
# "pooled",
"last",
"penultimate"
]
def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
freeze=True, layer="last"):
super().__init__()
assert layer in self.LAYERS
model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
del model.visual
self.model = model
self.device = device
self.max_length = max_length
if freeze:
self.freeze()
self.layer = layer
if self.layer == "last":
self.layer_idx = 0
elif self.layer == "penultimate":
self.layer_idx = 1
else:
raise NotImplementedError()
def freeze(self):
self.model = self.model.eval()
for param in self.parameters():
param.requires_grad = False
def forward(self, text):
tokens = open_clip.tokenize(text)
z = self.encode_with_transformer(tokens.to(self.device))
return z
def encode_with_transformer(self, text):
x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model]
x = x + self.model.positional_embedding
x = x.permute(1, 0, 2) # NLD -> LND
x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
x = x.permute(1, 0, 2) # LND -> NLD
x = self.model.ln_final(x)
return x
def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
for i, r in enumerate(self.model.transformer.resblocks):
if i == len(self.model.transformer.resblocks) - self.layer_idx:
break
if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint(r, x, attn_mask)
else:
x = r(x, attn_mask=attn_mask)
return x
def encode(self, text):
return self(text)
class FrozenCLIPT5Encoder(AbstractEncoder):
def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda",
clip_max_length=77, t5_max_length=77):
def __init__(
self,
clip_version="openai/clip-vit-large-patch14",
t5_version="google/t5-v1_1-xl",
device="cuda",
clip_max_length=77,
t5_max_length=77,
):
super().__init__()
self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length)
self.clip_encoder = FrozenCLIPEmbedder(
clip_version, device, max_length=clip_max_length
)
self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, "
f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params.")
print(
f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, "
f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params."
)
def encode(self, text):
return self(text)
@@ -236,7 +218,15 @@ class FrozenCLIPT5Encoder(AbstractEncoder):
class FrozenCLIPEmbedderT3(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, freeze=True, use_vision=False):
def __init__(
self,
version="openai/clip-vit-large-patch14",
device="cuda",
max_length=77,
freeze=True,
use_vision=False,
):
super().__init__()
self.tokenizer = CLIPTokenizer.from_pretrained(version)
self.transformer = CLIPTextModel.from_pretrained(version)
@@ -255,7 +245,11 @@ class FrozenCLIPEmbedderT3(AbstractEncoder):
inputs_embeds=None,
embedding_manager=None,
):
seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
seq_length = (
input_ids.shape[-1]
if input_ids is not None
else inputs_embeds.shape[-2]
)
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
if inputs_embeds is None:
@@ -266,7 +260,9 @@ class FrozenCLIPEmbedderT3(AbstractEncoder):
embeddings = inputs_embeds + position_embeddings
return embeddings
self.transformer.text_model.embeddings.forward = embedding_forward.__get__(self.transformer.text_model.embeddings)
self.transformer.text_model.embeddings.forward = embedding_forward.__get__(
self.transformer.text_model.embeddings
)
def encoder_forward(
self,
@@ -277,11 +273,19 @@ class FrozenCLIPEmbedderT3(AbstractEncoder):
output_hidden_states=None,
return_dict=None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
@@ -301,7 +305,9 @@ class FrozenCLIPEmbedderT3(AbstractEncoder):
encoder_states = encoder_states + (hidden_states,)
return hidden_states
self.transformer.text_model.encoder.forward = encoder_forward.__get__(self.transformer.text_model.encoder)
self.transformer.text_model.encoder.forward = encoder_forward.__get__(
self.transformer.text_model.encoder
)
def text_encoder_forward(
self,
@@ -313,22 +319,34 @@ class FrozenCLIPEmbedderT3(AbstractEncoder):
return_dict=None,
embedding_manager=None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is None:
raise ValueError("You have to specify either input_ids")
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids, embedding_manager=embedding_manager)
hidden_states = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
embedding_manager=embedding_manager,
)
bsz, seq_len = input_shape
# CLIP's text model uses causal mask, prepare it here.
# https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
causal_attention_mask = _build_causal_attention_mask(bsz, seq_len, hidden_states.dtype).to(
hidden_states.device
)
causal_attention_mask = _build_causal_attention_mask(
bsz, seq_len, hidden_states.dtype
).to(hidden_states.device)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
@@ -344,7 +362,9 @@ class FrozenCLIPEmbedderT3(AbstractEncoder):
last_hidden_state = self.final_layer_norm(last_hidden_state)
return last_hidden_state
self.transformer.text_model.forward = text_encoder_forward.__get__(self.transformer.text_model)
self.transformer.text_model.forward = text_encoder_forward.__get__(
self.transformer.text_model
)
def transformer_forward(
self,
@@ -363,7 +383,7 @@ class FrozenCLIPEmbedderT3(AbstractEncoder):
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
embedding_manager=embedding_manager
embedding_manager=embedding_manager,
)
self.transformer.forward = transformer_forward.__get__(self.transformer)
@@ -374,8 +394,15 @@ class FrozenCLIPEmbedderT3(AbstractEncoder):
param.requires_grad = False
def forward(self, text, **kwargs):
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")
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)
z = self.transformer(input_ids=tokens, **kwargs)
return z