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| import torch | |
| import torch.nn as nn | |
| from torch.utils.checkpoint import checkpoint | |
| from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel | |
| import open_clip | |
| from ldm.util import default, count_params | |
| 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', ucg_rate=0.1): | |
| super().__init__() | |
| self.key = key | |
| self.embedding = nn.Embedding(n_classes, embed_dim) | |
| self.n_classes = n_classes | |
| self.ucg_rate = ucg_rate | |
| def forward(self, batch, key=None, disable_dropout=False): | |
| if key is None: | |
| key = self.key | |
| # this is for use in crossattn | |
| c = batch[key][:, None] | |
| if self.ucg_rate > 0. and not disable_dropout: | |
| mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate) | |
| c = mask * c + (1-mask) * torch.ones_like(c)*(self.n_classes-1) | |
| c = c.long() | |
| c = self.embedding(c) | |
| return c | |
| def get_unconditional_conditioning(self, bs, device="cuda"): | |
| uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000) | |
| uc = torch.ones((bs,), device=device) * uc_class | |
| uc = {self.key: uc} | |
| return uc | |
| 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, freeze=True): # 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? | |
| if freeze: | |
| 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)""" | |
| LAYERS = [ | |
| "last", | |
| "pooled", | |
| "hidden" | |
| ] | |
| def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77, | |
| freeze=True, layer="last", layer_idx=None): # clip-vit-base-patch32 | |
| super().__init__() | |
| assert layer in self.LAYERS | |
| self.tokenizer = CLIPTokenizer.from_pretrained(version) | |
| self.transformer = CLIPTextModel.from_pretrained(version) | |
| self.device = device | |
| self.max_length = max_length | |
| if freeze: | |
| self.freeze() | |
| self.layer = layer | |
| self.layer_idx = layer_idx | |
| if layer == "hidden": | |
| assert layer_idx is not None | |
| assert 0 <= abs(layer_idx) <= 12 | |
| 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, output_hidden_states=self.layer=="hidden") | |
| if self.layer == "last": | |
| z = outputs.last_hidden_state | |
| elif self.layer == "pooled": | |
| z = outputs.pooler_output[:, None, :] | |
| else: | |
| z = outputs.hidden_states[self.layer_idx] | |
| return z | |
| def encode(self, text): | |
| return self(text) | |
| class FrozenOpenCLIPEmbedder(AbstractEncoder): | |
| """ | |
| Uses the OpenCLIP transformer encoder for text | |
| """ | |
| LAYERS = [ | |
| #"pooled", | |
| "last", | |
| "penultimate" | |
| ] | |
| def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77, | |
| freeze=True, layer="last"): | |
| super().__init__() | |
| assert layer in self.LAYERS | |
| model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version) | |
| del model.visual | |
| self.model = model | |
| self.device = device | |
| self.max_length = max_length | |
| if freeze: | |
| self.freeze() | |
| self.layer = layer | |
| if self.layer == "last": | |
| self.layer_idx = 0 | |
| elif self.layer == "penultimate": | |
| self.layer_idx = 1 | |
| else: | |
| raise NotImplementedError() | |
| def freeze(self): | |
| self.model = self.model.eval() | |
| for param in self.parameters(): | |
| param.requires_grad = False | |
| def forward(self, text): | |
| tokens = open_clip.tokenize(text) | |
| z = self.encode_with_transformer(tokens.to(self.device)) | |
| return z | |
| def encode_with_transformer(self, text): | |
| x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model] | |
| x = x + self.model.positional_embedding | |
| x = x.permute(1, 0, 2) # NLD -> LND | |
| x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) | |
| x = x.permute(1, 0, 2) # LND -> NLD | |
| x = self.model.ln_final(x) | |
| return x | |
| def text_transformer_forward(self, x: torch.Tensor, attn_mask = None): | |
| for i, r in enumerate(self.model.transformer.resblocks): | |
| if i == len(self.model.transformer.resblocks) - self.layer_idx: | |
| break | |
| if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting(): | |
| x = checkpoint(r, x, attn_mask) | |
| else: | |
| x = r(x, attn_mask=attn_mask) | |
| return x | |
| def encode(self, text): | |
| return self(text) | |
| class FrozenCLIPT5Encoder(AbstractEncoder): | |
| def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda", | |
| clip_max_length=77, t5_max_length=77): | |
| super().__init__() | |
| self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length) | |
| self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length) | |
| print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, " | |
| f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params.") | |
| def encode(self, text): | |
| return self(text) | |
| def forward(self, text): | |
| clip_z = self.clip_encoder.encode(text) | |
| t5_z = self.t5_encoder.encode(text) | |
| return [clip_z, t5_z] | |