496 lines
18 KiB
Python
496 lines
18 KiB
Python
import torch
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import torch.nn as nn
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from functools import partial
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import clip
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from einops import rearrange, repeat
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from transformers import CLIPTokenizer, CLIPTextModel
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import kornia
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from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
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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 ClassEmbedder(nn.Module):
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def __init__(self, embed_dim, n_classes=1000, key='class'):
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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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def forward(self, batch, key=None):
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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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c = self.embedding(c)
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return c
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class TransformerEmbedder(AbstractEncoder):
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"""Some transformer encoder layers"""
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def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
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super().__init__()
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self.device = device
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self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
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attn_layers=Encoder(dim=n_embed, depth=n_layer))
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def forward(self, tokens):
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tokens = tokens.to(self.device) # meh
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z = self.transformer(tokens, return_embeddings=True)
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return z
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def encode(self, x):
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return self(x)
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class BERTTokenizer(AbstractEncoder):
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""" Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
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def __init__(self, device="cuda", vq_interface=True, max_length=77):
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super().__init__()
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from transformers import BertTokenizerFast # TODO: add to reuquirements
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self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
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self.device = device
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self.vq_interface = vq_interface
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self.max_length = max_length
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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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return tokens
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@torch.no_grad()
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def encode(self, text):
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tokens = self(text)
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if not self.vq_interface:
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return tokens
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return None, None, [None, None, tokens]
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def decode(self, text):
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return text
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class BERTEmbedder(AbstractEncoder):
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"""Uses the BERT tokenizr model and add some transformer encoder layers"""
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def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
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device="cuda",use_tokenizer=True, embedding_dropout=0.0):
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super().__init__()
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self.use_tknz_fn = use_tokenizer
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if self.use_tknz_fn:
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self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
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self.device = device
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self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
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attn_layers=Encoder(dim=n_embed, depth=n_layer),
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emb_dropout=embedding_dropout)
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def forward(self, text, embedding_manager=None):
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if self.use_tknz_fn:
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tokens = self.tknz_fn(text)#.to(self.device)
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else:
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tokens = text
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z = self.transformer(tokens, return_embeddings=True, embedding_manager=embedding_manager)
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return z
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def encode(self, text, **kwargs):
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# output of length 77
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return self(text, **kwargs)
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class SpatialRescaler(nn.Module):
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def __init__(self,
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n_stages=1,
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method='bilinear',
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multiplier=0.5,
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in_channels=3,
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out_channels=None,
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bias=False):
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super().__init__()
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self.n_stages = n_stages
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assert self.n_stages >= 0
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assert method in ['nearest','linear','bilinear','trilinear','bicubic','area']
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self.multiplier = multiplier
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self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
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self.remap_output = out_channels is not None
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if self.remap_output:
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print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.')
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self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias)
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def forward(self,x):
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for stage in range(self.n_stages):
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x = self.interpolator(x, scale_factor=self.multiplier)
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if self.remap_output:
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x = self.channel_mapper(x)
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return x
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def encode(self, x):
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return self(x)
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class FrozenCLIPEmbedder(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):
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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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self.device = device
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self.max_length = max_length
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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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) -> torch.Tensor:
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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,
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)
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last_hidden_state = self.final_layer_norm(last_hidden_state)
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return last_hidden_state
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self.transformer.text_model.forward = text_encoder_forward.__get__(self.transformer.text_model)
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def transformer_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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return self.text_model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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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,
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embedding_manager = embedding_manager
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)
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self.transformer.forward = transformer_forward.__get__(self.transformer)
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# def update_embedding_func(self, embedding_manager):
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# text_model = self.transformer.text_model
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# # text_model.old_embeddings = text_model.embeddings
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# # def new_embeddings(
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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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# # ) -> torch.Tensor:
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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 = text_model.old_embeddings.position_ids[:, :seq_length]
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# # if inputs_embeds is None:
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# # inputs_embeds = text_model.old_embeddings.token_embedding(input_ids)
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# # inputs_embeds = embedding_manager(input_ids, inputs_embeds)
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# # position_embeddings = text_model.old_embeddings.position_embedding(position_ids)
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# # embeddings = inputs_embeds + position_embeddings
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# # return embeddings
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# # del text_model.embeddings
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# # text_model.embeddings = new_embeddings
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# # class NewEmbeddings(torch.nn.Module):
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# # def __init__(self, orig_embedder):
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# # super().__init__()
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# # self.orig_embedder = orig_embedder
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# # def 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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# # ) -> torch.Tensor:
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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.orig_embedder.position_ids[:, :seq_length]
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# # if inputs_embeds is None:
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# # inputs_embeds = self.orig_embedder.token_embedding(input_ids)
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# # inputs_embeds = embedding_manager(input_ids, inputs_embeds)
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# # position_embeddings = self.orig_embedder.position_embedding(position_ids)
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# # embeddings = inputs_embeds + position_embeddings
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# # return embeddings
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# # # self.new_embeddings =
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# # # text_model.embeddings = new_embeddings.__call__.__get__(text_model)
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# # text_model.embeddings = NewEmbeddings(text_model.embeddings)
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# class NewEmbeddings(torch.nn.Module):
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# def __init__(self, orig_embedder, embedding_manager):
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# super().__init__()
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# self.embedding_manager = embedding_manager
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# self.orig_embedder = orig_embedder
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# def 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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# ) -> torch.Tensor:
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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.orig_embedder.position_ids[:, :seq_length]
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# if inputs_embeds is None:
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# inputs_embeds = self.orig_embedder.token_embedding(input_ids)
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# # init_embeds = inputs_embeds.clone()
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# inputs_embeds = self.embedding_manager(input_ids, inputs_embeds)
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# # print(inputs_embeds - init_embeds)
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# # print((inputs_embeds - init_embeds).max())
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# # exit(0)
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# position_embeddings = self.orig_embedder.position_embedding(position_ids)
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# embeddings = inputs_embeds + position_embeddings
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# return embeddings
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# # self.new_embeddings =
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# # text_model.embeddings = new_embeddings.__call__.__get__(text_model)
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# text_model.embeddings = NewEmbeddings(text_model.embeddings, embedding_manager)
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def freeze(self):
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self.transformer = self.transformer.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, **kwargs):
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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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z = self.transformer(input_ids=tokens, **kwargs)
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return z
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def encode(self, text, **kwargs):
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return self(text, **kwargs)
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class FrozenCLIPTextEmbedder(nn.Module):
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"""
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Uses the CLIP transformer encoder for text.
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"""
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def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True):
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super().__init__()
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self.model, _ = clip.load(version, jit=False, device="cpu")
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self.device = device
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self.max_length = max_length
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self.n_repeat = n_repeat
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self.normalize = normalize
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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 = clip.tokenize(text).to(self.device)
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z = self.model.encode_text(tokens)
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if self.normalize:
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z = z / torch.linalg.norm(z, dim=1, keepdim=True)
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return z
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def encode(self, text):
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z = self(text)
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if z.ndim==2:
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z = z[:, None, :]
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z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat)
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return z
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class FrozenClipImageEmbedder(nn.Module):
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"""
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Uses the CLIP image encoder.
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"""
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def __init__(
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self,
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model,
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jit=False,
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device='cuda' if torch.cuda.is_available() else 'cpu',
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antialias=False,
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):
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super().__init__()
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self.model, _ = clip.load(name=model, device=device, jit=jit)
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self.antialias = antialias
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self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
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self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
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def preprocess(self, x):
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# normalize to [0,1]
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x = kornia.geometry.resize(x, (224, 224),
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interpolation='bicubic',align_corners=True,
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antialias=self.antialias)
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x = (x + 1.) / 2.
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# renormalize according to clip
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x = kornia.enhance.normalize(x, self.mean, self.std)
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return x
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def forward(self, x):
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# x is assumed to be in range [-1,1]
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return self.model.encode_image(self.preprocess(x))
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if __name__ == "__main__":
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from ldm.util import count_params
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model = FrozenCLIPEmbedder()
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count_params(model, verbose=True) |