import math 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.invoke.devices import choose_torch_device from ldm.modules.x_transformer import ( Encoder, TransformerWrapper, ) # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test def _expand_mask(mask, dtype, tgt_len=None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = ( mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) ) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill( inverted_mask.to(torch.bool), torch.finfo(dtype).min ) def _build_causal_attention_mask(bsz, seq_len, dtype): # lazily create causal attention mask, with full attention between the vision tokens # pytorch uses additive attention mask; fill with -inf mask = torch.empty(bsz, seq_len, seq_len, dtype=dtype) mask.fill_(torch.tensor(torch.finfo(dtype).min)) mask.triu_(1) # zero out the lower diagonal mask = mask.unsqueeze(1) # expand mask return mask 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=choose_torch_device(), ): 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=choose_torch_device(), vq_interface=True, max_length=77 ): super().__init__() from transformers import ( BertTokenizerFast, ) # TODO: add to reuquirements # Modified to allow to run on non-internet connected compute nodes. # Model needs to be loaded into cache from an internet-connected machine # by running: # from transformers import BertTokenizerFast # BertTokenizerFast.from_pretrained("bert-base-uncased") try: self.tokenizer = BertTokenizerFast.from_pretrained( 'bert-base-uncased', local_files_only=True ) except OSError: raise SystemExit( "* Couldn't load Bert tokenizer files. Try running scripts/preload_models.py from an internet-conected machine." ) 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=choose_torch_device(), 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, embedding_manager=None): if self.use_tknz_fn: tokens = self.tknz_fn(text) # .to(self.device) else: tokens = text z = self.transformer( tokens, return_embeddings=True, embedding_manager=embedding_manager ) return z def encode(self, text, **kwargs): # output of length 77 return self(text, **kwargs) 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=choose_torch_device(), max_length=77, ): super().__init__() self.tokenizer = CLIPTokenizer.from_pretrained( version, local_files_only=True ) self.transformer = CLIPTextModel.from_pretrained( version, local_files_only=True ) self.device = device self.max_length = max_length self.freeze() def embedding_forward( self, input_ids=None, position_ids=None, inputs_embeds=None, embedding_manager=None, ) -> torch.Tensor: seq_length = ( input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2] ) if position_ids is None: position_ids = self.position_ids[:, :seq_length] if inputs_embeds is None: inputs_embeds = self.token_embedding(input_ids) if embedding_manager is not None: inputs_embeds = embedding_manager(input_ids, inputs_embeds) position_embeddings = self.position_embedding(position_ids) embeddings = inputs_embeds + position_embeddings return embeddings self.transformer.text_model.embeddings.forward = ( embedding_forward.__get__(self.transformer.text_model.embeddings) ) def encoder_forward( self, inputs_embeds, attention_mask=None, causal_attention_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): output_attentions = ( output_attentions if output_attentions is not None else self.config.output_attentions ) output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict ) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None hidden_states = inputs_embeds for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) layer_outputs = encoder_layer( hidden_states, attention_mask, causal_attention_mask, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) return hidden_states self.transformer.text_model.encoder.forward = encoder_forward.__get__( self.transformer.text_model.encoder ) def text_encoder_forward( self, input_ids=None, attention_mask=None, position_ids=None, output_attentions=None, output_hidden_states=None, return_dict=None, embedding_manager=None, ): output_attentions = ( output_attentions if output_attentions is not None else self.config.output_attentions ) output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict ) if input_ids is None: raise ValueError('You have to specify either input_ids') input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) hidden_states = self.embeddings( input_ids=input_ids, position_ids=position_ids, embedding_manager=embedding_manager, ) bsz, seq_len = input_shape # CLIP's text model uses causal mask, prepare it here. # https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324 causal_attention_mask = _build_causal_attention_mask( bsz, seq_len, hidden_states.dtype ).to(hidden_states.device) # expand attention_mask if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] attention_mask = _expand_mask( attention_mask, hidden_states.dtype ) last_hidden_state = self.encoder( inputs_embeds=hidden_states, attention_mask=attention_mask, causal_attention_mask=causal_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = self.final_layer_norm(last_hidden_state) return last_hidden_state self.transformer.text_model.forward = text_encoder_forward.__get__( self.transformer.text_model ) def transformer_forward( self, input_ids=None, attention_mask=None, position_ids=None, output_attentions=None, output_hidden_states=None, return_dict=None, embedding_manager=None, ): return self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, embedding_manager=embedding_manager, ) self.transformer.forward = transformer_forward.__get__( self.transformer ) def freeze(self): self.transformer = self.transformer.eval() for param in self.parameters(): param.requires_grad = False def forward(self, text, **kwargs): 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) z = self.transformer(input_ids=tokens, **kwargs) return z def encode(self, text, **kwargs): return self(text, **kwargs) class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder): fragment_weights_key = "fragment_weights" return_tokens_key = "return_tokens" def forward(self, text: list, **kwargs): ''' :param text: A batch of prompt strings, or, a batch of lists of fragments of prompt strings to which different weights shall be applied. :param kwargs: If the keyword arg "fragment_weights" is passed, it shall contain a batch of lists of weights for the prompt fragments. In this case text must contain batches of lists of prompt fragments. :return: A tensor of shape (B, 77, 768) containing weighted embeddings ''' if self.fragment_weights_key not in kwargs: # fallback to base class implementation return super().forward(text, **kwargs) fragment_weights = kwargs[self.fragment_weights_key] # self.transformer doesn't like receiving "fragment_weights" as an argument kwargs.pop(self.fragment_weights_key) should_return_tokens = False if self.return_tokens_key in kwargs: should_return_tokens = kwargs.get(self.return_tokens_key, False) # self.transformer doesn't like having extra kwargs kwargs.pop(self.return_tokens_key) batch_z = None batch_tokens = None for fragments, weights in zip(text, fragment_weights): # First, weight tokens in individual fragments by scaling the feature vectors as requested (effectively # applying a multiplier to the CFG scale on a per-token basis). # For tokens weighted<1, intuitively we want SD to become not merely *less* interested in the concept # captured by the fragment but actually *dis*interested in it (a 0.01 interest in "red" is still an active # interest, however small, in redness; what the user probably intends when they attach the number 0.01 to # "red" is to tell SD that it should almost completely *ignore* redness). # To do this, the embedding is lerped away from base_embedding in the direction of an embedding for a prompt # string from which the low-weighted fragment has been simply removed. The closer the weight is to zero, the # closer the resulting embedding is to an embedding for a prompt that simply lacks this fragment. # handle weights >=1 tokens, per_token_weights = self.get_tokens_and_weights(fragments, weights) base_embedding = self.build_weighted_embedding_tensor(tokens, per_token_weights, **kwargs) # this is our starting point embeddings = base_embedding.unsqueeze(0) per_embedding_weights = [1.0] # now handle weights <1 # Do this by building extra embeddings tensors that lack the words being <1 weighted. These will be lerped # with the embeddings tensors that have the words, such that if the weight of a word is 0.5, the resulting # embedding will be exactly half-way between the unweighted prompt and the prompt with the <1 weighted words # removed. # eg for "mountain:1 man:0.5", intuitively the "man" should be "half-gone". therefore, append an embedding # for "mountain" (i.e. without "man") to the already-produced embedding for "mountain man", and weight it # such that the resulting lerped embedding is exactly half-way between "mountain man" and "mountain". for index, fragment_weight in enumerate(weights): if fragment_weight < 1: fragments_without_this = fragments[:index] + fragments[index+1:] weights_without_this = weights[:index] + weights[index+1:] tokens, per_token_weights = self.get_tokens_and_weights(fragments_without_this, weights_without_this) embedding_without_this = self.build_weighted_embedding_tensor(tokens, per_token_weights, **kwargs) embeddings = torch.cat((embeddings, embedding_without_this.unsqueeze(0)), dim=1) # weight of the embedding *without* this fragment gets *stronger* as its weight approaches 0 # if fragment_weight = 0, basically we want embedding_without_this to completely overwhelm base_embedding # therefore: # fragment_weight = 1: we are at base_z => lerp weight 0 # fragment_weight = 0.5: we are halfway between base_z and here => lerp weight 1 # fragment_weight = 0: we're now entirely overriding base_z ==> lerp weight inf # so let's use tan(), because: # tan is 0.0 at 0, # 1.0 at PI/4, and # inf at PI/2 # -> tan((1-weight)*PI/2) should give us ideal lerp weights epsilon = 1e-9 fragment_weight = max(epsilon, fragment_weight) # inf is bad embedding_lerp_weight = math.tan((1.0 - fragment_weight) * math.pi / 2) # todo handle negative weight? per_embedding_weights.append(embedding_lerp_weight) lerped_embeddings = self.apply_embedding_weights(embeddings, per_embedding_weights, normalize=True).squeeze(0) #print(f"assembled tokens for '{fragments}' into tensor of shape {lerped_embeddings.shape}") # append to batch batch_z = lerped_embeddings.unsqueeze(0) if batch_z is None else torch.cat([batch_z, lerped_embeddings.unsqueeze(0)], dim=1) batch_tokens = tokens.unsqueeze(0) if batch_tokens is None else torch.cat([batch_tokens, tokens.unsqueeze(0)], dim=1) # should have shape (B, 77, 768) #print(f"assembled all tokens into tensor of shape {batch_z.shape}") if should_return_tokens: return batch_z, batch_tokens else: return batch_z def get_tokens(self, fragments: list[str], include_start_and_end_markers: bool = True) -> list[list[int]]: tokens = self.tokenizer( fragments, truncation=True, max_length=self.max_length, return_overflowing_tokens=False, padding='do_not_pad', return_tensors=None, # just give me a list of ints )['input_ids'] if include_start_and_end_markers: return tokens else: return [x[1:-1] for x in tokens] @classmethod def apply_embedding_weights(self, embeddings: torch.Tensor, per_embedding_weights: list[float], normalize:bool) -> torch.Tensor: per_embedding_weights = torch.tensor(per_embedding_weights, dtype=embeddings.dtype, device=embeddings.device) if normalize: per_embedding_weights = per_embedding_weights / torch.sum(per_embedding_weights) reshaped_weights = per_embedding_weights.reshape(per_embedding_weights.shape + (1, 1,)) #reshaped_weights = per_embedding_weights.reshape(per_embedding_weights.shape + (1,1,)).expand(embeddings.shape) return torch.sum(embeddings * reshaped_weights, dim=1) # lerped embeddings has shape (77, 768) def get_tokens_and_weights(self, fragments: list[str], weights: list[float]) -> (torch.Tensor, torch.Tensor): ''' :param fragments: :param weights: Per-fragment weights (CFG scaling). No need for these to be normalized. They will not be normalized here and that's fine. :return: ''' # empty is meaningful if len(fragments) == 0 and len(weights) == 0: fragments = [''] weights = [1] item_encodings = self.tokenizer( fragments, truncation=True, max_length=self.max_length, return_overflowing_tokens=True, padding='do_not_pad', return_tensors=None, # just give me a list of ints )['input_ids'] all_tokens = [] per_token_weights = [] #print("all fragments:", fragments, weights) for index, fragment in enumerate(item_encodings): weight = weights[index] #print("processing fragment", fragment, weight) fragment_tokens = item_encodings[index] #print("fragment", fragment, "processed to", fragment_tokens) # trim bos and eos markers before appending all_tokens.extend(fragment_tokens[1:-1]) per_token_weights.extend([weight] * (len(fragment_tokens) - 2)) if (len(all_tokens) + 2) > self.max_length: excess_token_count = (len(all_tokens) + 2) - self.max_length print(f">> Prompt is {excess_token_count} token(s) too long and has been truncated") all_tokens = all_tokens[:self.max_length - 2] per_token_weights = per_token_weights[:self.max_length - 2] # pad out to a 77-entry array: [eos_token, , eos_token, ..., eos_token] # (77 = self.max_length) pad_length = self.max_length - 1 - len(all_tokens) all_tokens.insert(0, self.tokenizer.bos_token_id) all_tokens.extend([self.tokenizer.eos_token_id] * pad_length) per_token_weights.insert(0, 1) per_token_weights.extend([1] * pad_length) all_tokens_tensor = torch.tensor(all_tokens, dtype=torch.long).to(self.device) per_token_weights_tensor = torch.tensor(per_token_weights, dtype=torch.float32).to(self.device) #print(f"assembled all_tokens_tensor with shape {all_tokens_tensor.shape}") return all_tokens_tensor, per_token_weights_tensor def build_weighted_embedding_tensor(self, tokens: torch.Tensor, per_token_weights: torch.Tensor, weight_delta_from_empty=True, **kwargs) -> torch.Tensor: ''' Build a tensor representing the passed-in tokens, each of which has a weight. :param tokens: A tensor of shape (77) containing token ids (integers) :param per_token_weights: A tensor of shape (77) containing weights (floats) :param method: Whether to multiply the whole feature vector for each token or just its distance from an "empty" feature vector :param kwargs: passed on to self.transformer() :return: A tensor of shape (1, 77, 768) representing the requested weighted embeddings. ''' #print(f"building weighted embedding tensor for {tokens} with weights {per_token_weights}") z = self.transformer(input_ids=tokens.unsqueeze(0), **kwargs) batch_weights_expanded = per_token_weights.reshape(per_token_weights.shape + (1,)).expand(z.shape) if weight_delta_from_empty: empty_tokens = self.tokenizer([''] * z.shape[0], truncation=True, max_length=self.max_length, padding='max_length', return_tensors='pt' )['input_ids'].to(self.device) empty_z = self.transformer(input_ids=empty_tokens, **kwargs) z_delta_from_empty = z - empty_z weighted_z = empty_z + (z_delta_from_empty * batch_weights_expanded) weighted_z_delta_from_empty = (weighted_z-empty_z) #print("weighted z has delta from empty with sum", weighted_z_delta_from_empty.sum().item(), "mean", weighted_z_delta_from_empty.mean().item() ) #print("using empty-delta method, first 5 rows:") #print(weighted_z[:5]) return weighted_z else: original_mean = z.mean() z *= batch_weights_expanded after_weighting_mean = z.mean() # correct the mean. not sure if this is right but it's what the automatic1111 fork of SD does mean_correction_factor = original_mean/after_weighting_mean z *= mean_correction_factor return z class FrozenCLIPTextEmbedder(nn.Module): """ Uses the CLIP transformer encoder for text. """ def __init__( self, version='ViT-L/14', device=choose_torch_device(), max_length=77, n_repeat=1, normalize=True, ): super().__init__() self.model, _ = clip.load(version, jit=False, device=device) 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=choose_torch_device(), 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 + 1.0) / 2.0 # 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)