ldm/modules/encoders/modules.py

import torch
import torch.nn as nn
from functools import partial
import clip
from einops import rearrange, repeat
from transformers import CLIPTokenizer, CLIPTextModel,CLIPVisionModel,CLIPModel
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
from .xf import LayerNorm, Transformer
import math

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")
        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)


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 FrozenCLIPImageEmbedder(AbstractEncoder):
    """Uses the CLIP transformer encoder for image (from Hugging Face)"""
    def __init__(self, version="openai/clip-vit-large-patch14"):
        super().__init__()
        self.transformer = CLIPVisionModel.from_pretrained(version)
        self.final_ln = LayerNorm(1024)
        self.mapper = Transformer(
                1,
                1024,
                5,
                1,
            )

        self.freeze()

    def freeze(self):
        self.transformer = self.transformer.eval()
        for param in self.parameters():
            param.requires_grad = False
        for param in self.mapper.parameters():
            param.requires_grad = True
        for param in self.final_ln.parameters():
            param.requires_grad = True

    def forward(self, image):
        image = image.to('cuda')
        outputs = self.transformer(pixel_values=image)
        z = outputs.pooler_output
        z = z.unsqueeze(1)
        z = self.mapper(z)
        z = self.final_ln(z)
        return z

    def encode(self, image):
        return self(image)
    

class FrozenCLIPTextEmbedder(AbstractEncoder):
    """
    Uses the CLIP transformer encoder for text (from Hugging Face)
    """
    def __init__(self, version="openai/clip-vit-large-patch14"):
        super().__init__()
        
        self.tokenizer = CLIPTokenizer.from_pretrained(version)
        self.text_model = CLIPTextModel.from_pretrained(version)
        
        #up d_model 1024 to concat with ImageEmbedding
        # self.linear_proj = nn.Linear(768, 1024)
        self.final_ln = nn.LayerNorm(768)
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=768,    
            nhead=8,
            dim_feedforward=2048,
            batch_first=True
        )
        self.mapper = nn.TransformerEncoder(encoder_layer, num_layers=2)
        self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

        self.freeze()

    def freeze(self):
        """
        Freezes the transformer weights while keeping
        the mapper and final layer normalization trainable.
        """
        self.text_model = self.text_model.eval()
        for param in self.parameters():
            param.requires_grad = False
        for param in self.mapper.parameters():
            param.requires_grad = True
        for param in self.final_ln.parameters():
            param.requires_grad = True
        # self.linear_proj.requires_grad = True

    def forward(self, text):
        """
        Encodes text using the tokenizer and transformer.
        Applies mapper and final layer normalization for processing.
        """
        inputs = self.tokenizer(text, return_tensors="pt", padding=True, truncation=True)
        inputs = {k: v.to('cuda') for k, v in inputs.items()}
        outputs = self.text_model(**inputs)
        
        z = outputs.pooler_output
        # z = self.linear_proj(z)    #768 -> 1024
        z = z.unsqueeze(1)
        z = self.mapper(z)
        z = self.final_ln(z)

        return z
    
    def encode(self, text):
        return self(text)