319 lines
12 KiB
Python
319 lines
12 KiB
Python
import torch
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import torch.nn as nn
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import numpy as np
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from functools import partial
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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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from ldm.util import default
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import clip
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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'):
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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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from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
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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): # 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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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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def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77): # clip-vit-base-patch32
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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 # TODO: typical value?
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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 FrozenCLIPImageEmbedder(AbstractEncoder):
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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='ViT-L/14',
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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.device = device
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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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# Expects inputs in the range -1, 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)).float()
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def encode(self, im):
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return self(im).unsqueeze(1)
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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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from ldm.util import instantiate_from_config
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from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
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class LowScaleEncoder(nn.Module):
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def __init__(self, model_config, linear_start, linear_end, timesteps=1000, max_noise_level=250, output_size=64,
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scale_factor=1.0):
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super().__init__()
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self.max_noise_level = max_noise_level
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self.model = instantiate_from_config(model_config)
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self.augmentation_schedule = self.register_schedule(timesteps=timesteps, linear_start=linear_start,
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linear_end=linear_end)
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self.out_size = output_size
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self.scale_factor = scale_factor
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def register_schedule(self, beta_schedule="linear", timesteps=1000,
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linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
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betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
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cosine_s=cosine_s)
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alphas = 1. - betas
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alphas_cumprod = np.cumprod(alphas, axis=0)
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alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
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timesteps, = betas.shape
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self.num_timesteps = int(timesteps)
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self.linear_start = linear_start
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self.linear_end = linear_end
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assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
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to_torch = partial(torch.tensor, dtype=torch.float32)
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self.register_buffer('betas', to_torch(betas))
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self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
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self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
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# calculations for diffusion q(x_t | x_{t-1}) and others
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self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
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self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
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self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
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self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
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self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
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def q_sample(self, x_start, t, noise=None):
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noise = default(noise, lambda: torch.randn_like(x_start))
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return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
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extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
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def forward(self, x):
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z = self.model.encode(x).sample()
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z = z * self.scale_factor
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noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
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z = self.q_sample(z, noise_level)
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if self.out_size is not None:
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z = torch.nn.functional.interpolate(z, size=self.out_size, mode="nearest") # TODO: experiment with mode
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# z = z.repeat_interleave(2, -2).repeat_interleave(2, -1)
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return z, noise_level
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def decode(self, z):
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z = z / self.scale_factor
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return self.model.decode(z)
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if __name__ == "__main__":
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from ldm.util import count_params
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sentences = ["a hedgehog drinking a whiskey", "der mond ist aufgegangen", "Ein Satz mit vielen Sonderzeichen: äöü ß ?! : 'xx-y/@s'"]
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model = FrozenT5Embedder(version="google/t5-v1_1-xl").cuda()
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count_params(model, True)
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z = model(sentences)
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print(z.shape)
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model = FrozenCLIPEmbedder().cuda()
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count_params(model, True)
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z = model(sentences)
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print(z.shape)
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print("done.")
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