import torch import torch.nn as nn from transformers import PreTrainedModel, T5ForConditionalGeneration, T5Config, AutoTokenizer from .configuration_emuru import EmuruConfig from diffusers import AutoencoderKL from einops.layers.torch import Rearrange from einops import repeat from torchvision.transforms import functional as F from typing import Optional, Tuple, List, Any from PIL import Image class Emuru(PreTrainedModel): """ Emuru is a conditional generative model that integrates a T5-based decoder with a VAE for image generation conditioned on text and style images. Attributes: config_class (Type): Configuration class for the model. tokenizer (AutoTokenizer): Tokenizer loaded from the provided tokenizer configuration. T5 (T5ForConditionalGeneration): T5 model adapted for conditional generation. sos (nn.Embedding): Start-of-sequence embedding. vae_to_t5 (nn.Linear): Linear projection from VAE latent space to T5 hidden space. t5_to_vae (nn.Linear): Linear projection from T5 hidden space back to VAE latent space. padding_token (nn.Parameter): Non-trainable parameter for padding tokens. padding_token_threshold (nn.Parameter): Non-trainable parameter for padding token threshold. vae (AutoencoderKL): Pre-trained Variational Autoencoder. query_rearrange (Rearrange): Layer to rearrange VAE latent representations for queries. z_rearrange (Rearrange): Layer to rearrange T5 outputs back to VAE latent dimensions. mse_criterion (nn.MSELoss): Mean squared error loss function. """ config_class = EmuruConfig def __init__(self, config: EmuruConfig) -> None: """ Initialize the Emuru model. Args: config (EmuruConfig): Configuration object containing model hyperparameters and paths. """ super().__init__(config) self.tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name_or_path) t5_config = T5Config.from_pretrained(config.t5_name_or_path) t5_config.vocab_size = len(self.tokenizer) self.T5 = T5ForConditionalGeneration(t5_config) self.T5.lm_head = nn.Identity() self.sos = nn.Embedding(1, t5_config.d_model) vae_latent_size = 8 * config.vae_channels * config.slices_per_query self.vae_to_t5 = nn.Linear(vae_latent_size, t5_config.d_model) self.t5_to_vae = nn.Linear(t5_config.d_model, vae_latent_size, bias=False) self.padding_token = nn.Parameter(torch.empty((1, vae_latent_size)), requires_grad=False) self.padding_token_threshold = nn.Parameter(torch.empty(1), requires_grad=False) self.vae = AutoencoderKL.from_pretrained(config.vae_name_or_path) self.set_training(self.vae, False) self.query_rearrange = Rearrange('b c h (w q) -> b w (q c h)', q=config.slices_per_query) self.z_rearrange = Rearrange('b w (q c h) -> b c h (w q)', c=config.vae_channels, q=config.slices_per_query) self.mse_criterion = nn.MSELoss() self.init_weights() def set_training(self, model: nn.Module, training: bool) -> None: """ Set the training mode for a given model and freeze/unfreeze parameters accordingly. Args: model (nn.Module): The model to set the training mode for. training (bool): If True, set the model to training mode; otherwise, evaluation mode. """ model.train() if training else model.eval() for param in model.parameters(): param.requires_grad = training def forward( self, img: Optional[torch.Tensor] = None, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, noise: float = 0, **kwargs: Any ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ Forward pass of the model. Args: img (Optional[torch.Tensor]): Input image tensor. input_ids (Optional[torch.Tensor]): Tokenized input IDs. attention_mask (Optional[torch.Tensor]): Attention mask for the inputs. noise (float): Amount of noise to add in image encoding. **kwargs: Additional arguments. Returns: Tuple containing: - mse_loss (torch.Tensor): Mean squared error loss. - pred_latent (torch.Tensor): Predicted latent representations. - z (torch.Tensor): Sampled latent vector from VAE. """ decoder_inputs_embeds, z_sequence, z = self._img_encode(img, noise) output = self.T5(input_ids, attention_mask=attention_mask, decoder_inputs_embeds=decoder_inputs_embeds) vae_latent = self.t5_to_vae(output.logits[:, :-1]) pred_latent = self.z_rearrange(vae_latent) mse_loss = self.mse_criterion(vae_latent, z_sequence) return mse_loss, pred_latent, z @torch.inference_mode() def generate( self, style_text: str, gen_text: str, style_img: torch.Tensor, **kwargs: Any ) -> Image.Image: """ Generate an image by combining style and generation texts with a style image. Args: style_text (str): Style-related text prompt. gen_text (str): Generation-related text prompt. style_img (torch.Tensor): Style image tensor. Expected shape is either 3D or 4D. **kwargs: Additional keyword arguments. Returns: Image.Image: Generated image as a PIL image. """ if style_img.ndim == 3: style_img = style_img.unsqueeze(0) elif style_img.ndim == 4: pass else: raise ValueError('style_img must be 3D or 4D') texts = [style_text + ' ' + gen_text] imgs, _, img_ends = self._generate(texts=texts, imgs=style_img, **kwargs) imgs = (imgs + 1) / 2 return F.to_pil_image(imgs[0, ..., style_img.size(-1):img_ends.item()].detach().cpu()) @torch.inference_mode() def generate_batch( self, style_texts: List[str], gen_texts: List[str], style_imgs: torch.Tensor, lengths: List[int], **kwargs: Any ) -> List[Image.Image]: """ Generate a batch of images from lists of style texts, generation texts, and style images. Args: style_texts (List[str]): List of style-related text prompts. gen_texts (List[str]): List of generation-related text prompts. style_imgs (torch.Tensor): Batch of style images (4D tensor). lengths (List[int]): List of lengths corresponding to each image. **kwargs: Additional keyword arguments. Returns: List[Image.Image]: List of generated images as PIL images. """ assert style_imgs.ndim == 4, 'style_imgs must be 4D' assert len(style_texts) == len(style_imgs), 'style_texts and style_imgs must have the same length' assert len(gen_texts) == len(style_imgs), 'gen_texts and style_imgs must have the same length' texts = [style_text + ' ' + gen_text for style_text, gen_text in zip(style_texts, gen_texts)] imgs, _, img_ends = self._generate(texts=texts, imgs=style_imgs, lengths=lengths, **kwargs) imgs = (imgs + 1) / 2 out_imgs = [] for i, end in enumerate(img_ends): start = lengths[i] out_imgs.append(F.to_pil_image(imgs[i, ..., start:end].detach().cpu())) return out_imgs def _generate( self, texts: Optional[List[str]] = None, imgs: Optional[torch.Tensor] = None, lengths: Optional[List[int]] = None, input_ids: Optional[torch.Tensor] = None, z_sequence: Optional[torch.Tensor] = None, max_new_tokens: int = 256, stopping_criteria: str = 'latent', stopping_after: int = 10, stopping_patience: int = 1 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ Internal generation routine that combines textual and visual inputs to iteratively generate latent representations and decode them into images. Args: texts (Optional[List[str]]): List of text prompts. imgs (Optional[torch.Tensor]): Input image tensor. lengths (Optional[List[int]]): Desired lengths for each image in latent space. input_ids (Optional[torch.Tensor]): Tokenized input IDs. z_sequence (Optional[torch.Tensor]): Precomputed latent sequence. max_new_tokens (int): Maximum tokens to generate. stopping_criteria (str): Criteria for stopping ('latent' or 'none'). stopping_after (int): Number of tokens to check for stopping condition. stopping_patience (int): Patience parameter for stopping condition. Returns: Tuple containing: - imgs (torch.Tensor): Generated images. - canvas_sequence (torch.Tensor): Generated latent canvas sequence. - img_ends (torch.Tensor): End indices for each generated image. """ assert texts is not None or input_ids is not None, 'Either texts or input_ids must be provided' assert imgs is not None or z_sequence is not None, 'Either imgs or z_sequence must be provided' if input_ids is None: input_ids = self.tokenizer(texts, return_tensors='pt', padding=True).input_ids input_ids = input_ids.to(self.device) if z_sequence is None: _, z_sequence, _ = self._img_encode(imgs) if lengths is None: lengths = [imgs.size(-1)] * imgs.size(0) lengths = torch.tensor(lengths).to(self.device) lengths = (lengths / 8).ceil().int() z_sequence_mask = torch.zeros((z_sequence.size(0), lengths.max() + max_new_tokens)) z_sequence_mask = z_sequence_mask.bool().to(self.device) for i, l in enumerate(lengths): z_sequence_mask[i, :l] = True canvas_sequence = z_sequence[:, :lengths.min()] sos = repeat(self.sos.weight, '1 d -> b 1 d', b=input_ids.size(0)) pad_token = repeat(self.padding_token, '1 d -> b 1 d', b=input_ids.size(0)) seq_stops = torch.ones(z_sequence.size(0), dtype=torch.int) * -1 for token_idx in range(lengths.min(), lengths.max() + max_new_tokens): if len(z_sequence) == 0: decoder_inputs_embeds = sos else: decoder_inputs_embeds = self.vae_to_t5(canvas_sequence) decoder_inputs_embeds = torch.cat([sos, decoder_inputs_embeds], dim=1) output = self.T5(input_ids, decoder_inputs_embeds=decoder_inputs_embeds) vae_latent = self.t5_to_vae(output.logits[:, -1:]) mask_slice = z_sequence_mask[:, token_idx].unsqueeze(-1) if token_idx < z_sequence.size(1): seq_slice = torch.where(mask_slice, z_sequence[:, token_idx], vae_latent[:, 0]) else: seq_slice = vae_latent[:, 0] canvas_sequence = torch.cat([canvas_sequence, seq_slice.unsqueeze(1)], dim=1) if stopping_criteria == 'latent': similarity = torch.nn.functional.cosine_similarity(canvas_sequence, pad_token, dim=-1) windows = (similarity > self.padding_token_threshold).unfold(1, min(stopping_after, similarity.size(-1)), 1) window_sums = windows.to(torch.int).sum(dim=2) for i in range(similarity.size(0)): idx = (window_sums[i] > (stopping_after - stopping_patience)).nonzero(as_tuple=True)[0] if idx.numel() > 0: seq_stops[i] = idx[0].item() if torch.all(seq_stops >= 0): break elif stopping_criteria == 'none': pass imgs = torch.clamp(self.vae.decode(self.z_rearrange(canvas_sequence)).sample, -1, 1) return imgs, canvas_sequence, seq_stops * 8 def _img_encode( self, img: torch.Tensor, noise: float = 0 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ Encode the input image into a latent representation using the VAE. Args: img (torch.Tensor): Input image tensor. noise (float): Standard deviation of noise to add to the latent sequence. Returns: Tuple containing: - decoder_inputs_embeds (torch.Tensor): Embeddings to be used as T5 decoder inputs. - z_sequence (torch.Tensor): Rearranged latent sequence from the VAE. - z (torch.Tensor): Sampled latent vector from the VAE. """ posterior = self.vae.encode(img.float()) z = posterior.latent_dist.sample() z_sequence = self.query_rearrange(z) noise_sequence = z_sequence if noise > 0: noise_sequence = z_sequence + torch.randn_like(z_sequence) * noise decoder_inputs_embeds = self.vae_to_t5(noise_sequence) sos = repeat(self.sos.weight, '1 d -> b 1 d', b=decoder_inputs_embeds.size(0)) decoder_inputs_embeds = torch.cat([sos, decoder_inputs_embeds], dim=1) return decoder_inputs_embeds, z_sequence, z def compute_padding_token(self) -> None: """ Compute and update the padding token. Raises: NotImplementedError: This method must be implemented. """ raise NotImplementedError("compute_padding_token not implemented") def compute_padding_token_threshold(self) -> None: """ Compute and update the padding token threshold. Raises: NotImplementedError: This method must be implemented. """ raise NotImplementedError("compute_padding_token_threshold not implemented")