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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")
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