forked from FlintyLemming/ComfyUI-AnyText
416 lines
16 KiB
Python
416 lines
16 KiB
Python
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import torch
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import torch.nn as nn
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from torch.utils.checkpoint import checkpoint
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from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel, AutoProcessor, CLIPVisionModelWithProjection
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import open_clip
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from ...util import count_params
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def _expand_mask(mask, dtype, tgt_len=None):
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"""
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Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
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"""
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bsz, src_len = mask.size()
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tgt_len = tgt_len if tgt_len is not None else src_len
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expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
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inverted_mask = 1.0 - expanded_mask
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return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
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def _build_causal_attention_mask(bsz, seq_len, dtype):
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# lazily create causal attention mask, with full attention between the vision tokens
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# pytorch uses additive attention mask; fill with -inf
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mask = torch.empty(bsz, seq_len, seq_len, dtype=dtype)
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mask.fill_(torch.tensor(torch.finfo(dtype).min))
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mask.triu_(1) # zero out the lower diagonal
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mask = mask.unsqueeze(1) # expand mask
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return mask
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class AbstractEncoder(nn.Module):
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def __init__(self):
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super().__init__()
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def encode(self, *args, **kwargs):
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raise NotImplementedError
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class IdentityEncoder(AbstractEncoder):
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def encode(self, x):
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return x
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class ClassEmbedder(nn.Module):
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def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1):
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super().__init__()
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self.key = key
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self.embedding = nn.Embedding(n_classes, embed_dim)
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self.n_classes = n_classes
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self.ucg_rate = ucg_rate
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def forward(self, batch, key=None, disable_dropout=False):
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if key is None:
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key = self.key
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# this is for use in crossattn
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c = batch[key][:, None]
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if self.ucg_rate > 0. and not disable_dropout:
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mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
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c = mask * c + (1-mask) * torch.ones_like(c)*(self.n_classes-1)
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c = c.long()
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c = self.embedding(c)
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return c
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def get_unconditional_conditioning(self, bs, device="cuda"):
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uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
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uc = torch.ones((bs,), device=device) * uc_class
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uc = {self.key: uc}
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return uc
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def disabled_train(self, mode=True):
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"""Overwrite model.train with this function to make sure train/eval mode
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does not change anymore."""
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return self
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class FrozenT5Embedder(AbstractEncoder):
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"""Uses the T5 transformer encoder for text"""
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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
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super().__init__()
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self.tokenizer = T5Tokenizer.from_pretrained(version)
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self.transformer = T5EncoderModel.from_pretrained(version)
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self.device = device
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self.max_length = max_length # TODO: typical value?
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if freeze:
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self.freeze()
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def freeze(self):
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self.transformer = self.transformer.eval()
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#self.train = disabled_train
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for param in self.parameters():
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param.requires_grad = False
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def forward(self, text):
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batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
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return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
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tokens = batch_encoding["input_ids"].to(self.device)
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outputs = self.transformer(input_ids=tokens)
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z = outputs.last_hidden_state
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return z
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def encode(self, text):
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return self(text)
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class FrozenCLIPEmbedder(AbstractEncoder):
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"""Uses the CLIP transformer encoder for text (from huggingface)"""
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LAYERS = [
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"last",
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"pooled",
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"hidden"
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]
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def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77,
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freeze=True, layer="last", layer_idx=None): # clip-vit-base-patch32
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super().__init__()
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assert layer in self.LAYERS
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self.tokenizer = CLIPTokenizer.from_pretrained(version)
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self.transformer = CLIPTextModel.from_pretrained(version)
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self.device = device
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self.max_length = max_length
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if freeze:
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self.freeze()
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self.layer = layer
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self.layer_idx = layer_idx
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if layer == "hidden":
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assert layer_idx is not None
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assert 0 <= abs(layer_idx) <= 12
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def freeze(self):
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self.transformer = self.transformer.eval()
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# self.train = disabled_train
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for param in self.parameters():
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param.requires_grad = False
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def forward(self, text):
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batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
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return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
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tokens = batch_encoding["input_ids"].to(self.device)
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outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden")
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if self.layer == "last":
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z = outputs.last_hidden_state
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elif self.layer == "pooled":
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z = outputs.pooler_output[:, None, :]
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else:
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z = outputs.hidden_states[self.layer_idx]
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return z
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def encode(self, text):
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return self(text)
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class FrozenOpenCLIPEmbedder(AbstractEncoder):
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"""
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Uses the OpenCLIP transformer encoder for text
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"""
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LAYERS = [
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# "pooled",
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"last",
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"penultimate"
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]
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def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
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freeze=True, layer="last"):
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super().__init__()
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assert layer in self.LAYERS
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model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
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del model.visual
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self.model = model
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self.device = device
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self.max_length = max_length
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if freeze:
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self.freeze()
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self.layer = layer
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if self.layer == "last":
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self.layer_idx = 0
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elif self.layer == "penultimate":
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self.layer_idx = 1
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else:
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raise NotImplementedError()
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def freeze(self):
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self.model = self.model.eval()
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for param in self.parameters():
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param.requires_grad = False
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def forward(self, text):
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tokens = open_clip.tokenize(text)
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z = self.encode_with_transformer(tokens.to(self.device))
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return z
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def encode_with_transformer(self, text):
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x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model]
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x = x + self.model.positional_embedding
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x = x.permute(1, 0, 2) # NLD -> LND
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x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
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x = x.permute(1, 0, 2) # LND -> NLD
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x = self.model.ln_final(x)
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return x
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def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
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for i, r in enumerate(self.model.transformer.resblocks):
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if i == len(self.model.transformer.resblocks) - self.layer_idx:
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break
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if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting():
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x = checkpoint(r, x, attn_mask)
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else:
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x = r(x, attn_mask=attn_mask)
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return x
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def encode(self, text):
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return self(text)
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class FrozenCLIPT5Encoder(AbstractEncoder):
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def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda",
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clip_max_length=77, t5_max_length=77):
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super().__init__()
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self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length)
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self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
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print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, "
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f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params.")
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def encode(self, text):
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return self(text)
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def forward(self, text):
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clip_z = self.clip_encoder.encode(text)
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t5_z = self.t5_encoder.encode(text)
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return [clip_z, t5_z]
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class FrozenCLIPEmbedderT3(AbstractEncoder):
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"""Uses the CLIP transformer encoder for text (from Hugging Face)"""
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def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77, freeze=True, use_vision=False):
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super().__init__()
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self.tokenizer = CLIPTokenizer.from_pretrained(version)
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self.transformer = CLIPTextModel.from_pretrained(version)
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if use_vision:
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self.vit = CLIPVisionModelWithProjection.from_pretrained(version)
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self.processor = AutoProcessor.from_pretrained(version)
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self.device = device
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self.max_length = max_length
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if freeze:
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self.freeze()
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def embedding_forward(
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self,
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input_ids=None,
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position_ids=None,
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inputs_embeds=None,
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embedding_manager=None,
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):
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seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
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if position_ids is None:
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position_ids = self.position_ids[:, :seq_length]
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if inputs_embeds is None:
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inputs_embeds = self.token_embedding(input_ids)
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if embedding_manager is not None:
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inputs_embeds = embedding_manager(input_ids, inputs_embeds)
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position_embeddings = self.position_embedding(position_ids)
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embeddings = inputs_embeds + position_embeddings
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return embeddings
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self.transformer.text_model.embeddings.forward = embedding_forward.__get__(self.transformer.text_model.embeddings)
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def encoder_forward(
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self,
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inputs_embeds,
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attention_mask=None,
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causal_attention_mask=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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):
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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encoder_states = () if output_hidden_states else None
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all_attentions = () if output_attentions else None
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hidden_states = inputs_embeds
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for idx, encoder_layer in enumerate(self.layers):
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if output_hidden_states:
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encoder_states = encoder_states + (hidden_states,)
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layer_outputs = encoder_layer(
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hidden_states,
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attention_mask,
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causal_attention_mask,
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output_attentions=output_attentions,
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)
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hidden_states = layer_outputs[0]
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if output_attentions:
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all_attentions = all_attentions + (layer_outputs[1],)
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if output_hidden_states:
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encoder_states = encoder_states + (hidden_states,)
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return hidden_states
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self.transformer.text_model.encoder.forward = encoder_forward.__get__(self.transformer.text_model.encoder)
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def text_encoder_forward(
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self,
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input_ids=None,
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attention_mask=None,
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position_ids=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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embedding_manager=None,
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):
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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if input_ids is None:
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raise ValueError("You have to specify either input_ids")
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input_shape = input_ids.size()
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input_ids = input_ids.view(-1, input_shape[-1])
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hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids, embedding_manager=embedding_manager)
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bsz, seq_len = input_shape
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# CLIP's text model uses causal mask, prepare it here.
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# https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
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causal_attention_mask = _build_causal_attention_mask(bsz, seq_len, hidden_states.dtype).to(
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hidden_states.device
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)
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# expand attention_mask
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if attention_mask is not None:
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# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
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last_hidden_state = self.encoder(
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inputs_embeds=hidden_states,
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attention_mask=attention_mask,
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causal_attention_mask=causal_attention_mask,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
|
||
|
|
)
|
||
|
|
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)
|
||
|
|
|
||
|
|
def transformer_forward(
|
||
|
|
self,
|
||
|
|
input_ids=None,
|
||
|
|
attention_mask=None,
|
||
|
|
position_ids=None,
|
||
|
|
output_attentions=None,
|
||
|
|
output_hidden_states=None,
|
||
|
|
return_dict=None,
|
||
|
|
embedding_manager=None,
|
||
|
|
):
|
||
|
|
return self.text_model(
|
||
|
|
input_ids=input_ids,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
position_ids=position_ids,
|
||
|
|
output_attentions=output_attentions,
|
||
|
|
output_hidden_states=output_hidden_states,
|
||
|
|
return_dict=return_dict,
|
||
|
|
embedding_manager=embedding_manager
|
||
|
|
)
|
||
|
|
|
||
|
|
self.transformer.forward = transformer_forward.__get__(self.transformer)
|
||
|
|
|
||
|
|
def freeze(self):
|
||
|
|
self.transformer = self.transformer.eval()
|
||
|
|
for param in self.parameters():
|
||
|
|
param.requires_grad = False
|
||
|
|
|
||
|
|
def forward(self, text, **kwargs):
|
||
|
|
batch_encoding = self.tokenizer(text, truncation=False, max_length=self.max_length, return_length=True,
|
||
|
|
return_overflowing_tokens=False, padding="longest", return_tensors="pt")
|
||
|
|
input_ids = batch_encoding["input_ids"]
|
||
|
|
tokens_list = self.split_chunks(input_ids)
|
||
|
|
z_list = []
|
||
|
|
for tokens in tokens_list:
|
||
|
|
tokens = tokens.to(self.device)
|
||
|
|
_z = self.transformer(input_ids=tokens, **kwargs)
|
||
|
|
z_list += [_z]
|
||
|
|
return torch.cat(z_list, dim=1)
|
||
|
|
|
||
|
|
def encode(self, text, **kwargs):
|
||
|
|
return self(text, **kwargs)
|
||
|
|
|
||
|
|
def split_chunks(self, input_ids, chunk_size=75):
|
||
|
|
tokens_list = []
|
||
|
|
bs, n = input_ids.shape
|
||
|
|
id_start = input_ids[:, 0].unsqueeze(1) # dim --> [bs, 1]
|
||
|
|
id_end = input_ids[:, -1].unsqueeze(1)
|
||
|
|
if n == 2: # empty caption
|
||
|
|
tokens_list.append(torch.cat((id_start, )+(id_end, )*(chunk_size+1), dim=1))
|
||
|
|
|
||
|
|
trimmed_encoding = input_ids[:, 1:-1]
|
||
|
|
num_full_groups = (n - 2) // chunk_size
|
||
|
|
|
||
|
|
for i in range(num_full_groups):
|
||
|
|
group = trimmed_encoding[:, i * chunk_size:(i + 1) * chunk_size]
|
||
|
|
group_pad = torch.cat((id_start, group, id_end), dim=1)
|
||
|
|
tokens_list.append(group_pad)
|
||
|
|
|
||
|
|
remaining_columns = (n - 2) % chunk_size
|
||
|
|
if remaining_columns > 0:
|
||
|
|
remaining_group = trimmed_encoding[:, -remaining_columns:]
|
||
|
|
padding_columns = chunk_size - remaining_group.shape[1]
|
||
|
|
padding = id_end.expand(bs, padding_columns)
|
||
|
|
remaining_group_pad = torch.cat((id_start, remaining_group, padding, id_end), dim=1)
|
||
|
|
tokens_list.append(remaining_group_pad)
|
||
|
|
return tokens_list
|