import torch import torch.nn as nn import numpy as np from functools import partial from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test from ldm.util import default class AbstractEncoder(nn.Module): def __init__(self): super().__init__() def encode(self, *args, **kwargs): raise NotImplementedError class IdentityEncoder(AbstractEncoder): def encode(self, x): return x 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") tokens = batch_encoding["input_ids"].to(self.device) return tokens @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): if self.use_tknz_fn: tokens = self.tknz_fn(text)#.to(self.device) else: tokens = text z = self.transformer(tokens, return_embeddings=True) return z def encode(self, text): # output of length 77 return self(text) from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel def disabled_train(self, mode=True): """Overwrite model.train with this function to make sure train/eval mode does not change anymore.""" return self class FrozenT5Embedder(AbstractEncoder): """Uses the T5 transformer encoder for text""" 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 super().__init__() self.tokenizer = T5Tokenizer.from_pretrained(version) self.transformer = T5EncoderModel.from_pretrained(version) self.device = device self.max_length = max_length # TODO: typical value? self.freeze() def freeze(self): self.transformer = self.transformer.eval() #self.train = disabled_train for param in self.parameters(): param.requires_grad = False 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") tokens = batch_encoding["input_ids"].to(self.device) outputs = self.transformer(input_ids=tokens) z = outputs.last_hidden_state return z def encode(self, text): return self(text) class FrozenCLIPEmbedder(AbstractEncoder): """Uses the CLIP transformer encoder for text (from huggingface)""" def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77): # clip-vit-base-patch32 super().__init__() self.tokenizer = CLIPTokenizer.from_pretrained(version) self.transformer = CLIPTextModel.from_pretrained(version) self.device = device self.max_length = max_length # TODO: typical value? self.freeze() def freeze(self): self.transformer = self.transformer.eval() #self.train = disabled_train for param in self.parameters(): param.requires_grad = False 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") tokens = batch_encoding["input_ids"].to(self.device) outputs = self.transformer(input_ids=tokens) z = outputs.last_hidden_state return z def encode(self, text): return self(text) 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) from ldm.util import instantiate_from_config from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like class LowScaleEncoder(nn.Module): def __init__(self, model_config, linear_start, linear_end, timesteps=1000, max_noise_level=250, output_size=64, scale_factor=1.0): super().__init__() self.max_noise_level = max_noise_level self.model = instantiate_from_config(model_config) self.augmentation_schedule = self.register_schedule(timesteps=timesteps, linear_start=linear_start, linear_end=linear_end) self.out_size = output_size self.scale_factor = scale_factor def register_schedule(self, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) alphas = 1. - betas alphas_cumprod = np.cumprod(alphas, axis=0) alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.linear_start = linear_start self.linear_end = linear_end assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' to_torch = partial(torch.tensor, dtype=torch.float32) self.register_buffer('betas', to_torch(betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) def q_sample(self, x_start, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) def forward(self, x): z = self.model.encode(x).sample() z = z * self.scale_factor noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() z = self.q_sample(z, noise_level) if self.out_size is not None: z = torch.nn.functional.interpolate(z, size=self.out_size, mode="nearest") # TODO: experiment with mode # z = z.repeat_interleave(2, -2).repeat_interleave(2, -1) return z, noise_level def decode(self, z): z = z / self.scale_factor return self.model.decode(z) if __name__ == "__main__": from ldm.util import count_params sentences = ["a hedgehog drinking a whiskey", "der mond ist aufgegangen", "Ein Satz mit vielen Sonderzeichen: äöü ß ?! : 'xx-y/@s'"] model = FrozenT5Embedder(version="google/t5-v1_1-xl").cuda() count_params(model, True) z = model(sentences) print(z.shape) model = FrozenCLIPEmbedder().cuda() count_params(model, True) z = model(sentences) print(z.shape) print("done.")