mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
132 lines
4.6 KiB
Python
132 lines
4.6 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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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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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):
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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)
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return z
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def encode(self, text):
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# output of length 77
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return self(text)
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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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