import torch import torch.nn as nn from functools import partial import clip from einops import rearrange, repeat from transformers import CLIPTokenizer, CLIPTextModel import kornia from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test class AbstractEncoder(nn.Module): def __init__(self): super().__init__() def encode(self, *args, **kwargs): raise NotImplementedError class ClassEmbedder(nn.Module): def __init__(self, embed_dim, n_classes=1000, key='class'): super().__init__() self.key = key self.embedding = nn.Embedding(n_classes, embed_dim) def forward(self, batch, key=None): if key is None: key = self.key # this is for use in crossattn c = batch[key][:, None] c = self.embedding(c) return c class TransformerEmbedder(AbstractEncoder): """Some transformer encoder layers""" def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"): super().__init__() self.device = device self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, attn_layers=Encoder(dim=n_embed, depth=n_layer)) def forward(self, tokens): tokens = tokens.to(self.device) # meh z = self.transformer(tokens, return_embeddings=True) return z def encode(self, x): return self(x) class BERTTokenizer(AbstractEncoder): """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)""" def __init__(self, device="cuda", vq_interface=True, max_length=77): super().__init__() from transformers import BertTokenizerFast # TODO: add to reuquirements self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") self.device = device self.vq_interface = vq_interface self.max_length = max_length def forward(self, text): batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt", return_offsets_mapping=True) tokens = batch_encoding["input_ids"].to(self.device) offset_mapping = batch_encoding["offset_mapping"] return tokens, offset_mapping @torch.no_grad() def encode(self, text): tokens = self(text) if not self.vq_interface: return tokens return None, None, [None, None, tokens] def decode(self, text): return text class BERTEmbedder(AbstractEncoder): """Uses the BERT tokenizr model and add some transformer encoder layers""" def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77, device="cuda",use_tokenizer=True, embedding_dropout=0.0): super().__init__() self.use_tknz_fn = use_tokenizer if self.use_tknz_fn: self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len) self.device = device self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, attn_layers=Encoder(dim=n_embed, depth=n_layer), emb_dropout=embedding_dropout) def forward(self, text, return_offset_mapping=False): if self.use_tknz_fn: tokens, offset_mapping = self.tknz_fn(text)#.to(self.device) else: assert False tokens = text z = self.transformer(tokens, return_embeddings=True) if return_offset_mapping: return z, offset_mapping else: return z def encode(self, text, return_offset_mapping=False): # output of length 77 return self(text, return_offset_mapping) class SpatialRescaler(nn.Module): def __init__(self, n_stages=1, method='bilinear', multiplier=0.5, in_channels=3, out_channels=None, bias=False): super().__init__() self.n_stages = n_stages assert self.n_stages >= 0 assert method in ['nearest','linear','bilinear','trilinear','bicubic','area'] self.multiplier = multiplier self.interpolator = partial(torch.nn.functional.interpolate, mode=method) self.remap_output = out_channels is not None if self.remap_output: print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.') self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias) def forward(self,x): for stage in range(self.n_stages): x = self.interpolator(x, scale_factor=self.multiplier) if self.remap_output: x = self.channel_mapper(x) return x def encode(self, x): return self(x) class FrozenCLIPEmbedder(AbstractEncoder): """Uses the CLIP transformer encoder for text (from Hugging Face)""" def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77): super().__init__() self.tokenizer = CLIPTokenizer.from_pretrained(version) self.transformer = CLIPTextModel.from_pretrained(version) self.device = device self.max_length = max_length self.freeze() def freeze(self): self.transformer = self.transformer.eval() for param in self.parameters(): param.requires_grad = False def forward(self, text, return_pooler_output=False): batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt") tokens = batch_encoding["input_ids"].to(self.device) outputs = self.transformer(input_ids=tokens) z = outputs.last_hidden_state if not return_pooler_output: return z else: return z, outputs.pooler_output def encode(self, text, return_pooler_output=False): return self(text, return_pooler_output) class FrozenCLIPTextEmbedder(nn.Module): """ Uses the CLIP transformer encoder for text. """ def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True): super().__init__() self.model, _ = clip.load(version, jit=False, device="cpu") self.device = device self.max_length = max_length self.n_repeat = n_repeat self.normalize = normalize def freeze(self): self.model = self.model.eval() for param in self.parameters(): param.requires_grad = False def forward(self, text): tokens = clip.tokenize(text).to(self.device) z = self.model.encode_text(tokens) if self.normalize: z = z / torch.linalg.norm(z, dim=1, keepdim=True) return z def encode(self, text): z = self(text) if z.ndim==2: z = z[:, None, :] z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat) return z class FrozenClipImageEmbedder(nn.Module): """ Uses the CLIP image encoder. """ def __init__( self, model, jit=False, device='cuda' if torch.cuda.is_available() else 'cpu', antialias=False, ): super().__init__() self.model, _ = clip.load(name=model, device=device, jit=jit) self.antialias = antialias self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) def preprocess(self, x): # normalize to [0,1] x = kornia.geometry.resize(x, (224, 224), interpolation='bicubic',align_corners=True, antialias=self.antialias) x = (x + 1.) / 2. # renormalize according to clip x = kornia.enhance.normalize(x, self.mean, self.std) return x def forward(self, x): # x is assumed to be in range [-1,1] return self.model.encode_image(self.preprocess(x)) if __name__ == "__main__": from ldm.util import count_params model = FrozenCLIPEmbedder() count_params(model, verbose=True)