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from typing import List, Optional, Union
import torch
from transformers import T5EncoderModel, T5Tokenizer
def _get_t5_prompt_embeds(
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
prompt: Union[str, List[str]],
num_videos_per_prompt: int = 1,
max_sequence_length: int = 226,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
text_input_ids=None,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
if tokenizer is not None:
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
else:
if text_input_ids is None:
raise ValueError("`text_input_ids` must be provided when the tokenizer is not specified.")
prompt_embeds = text_encoder(text_input_ids.to(device))[0]
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
# duplicate text embeddings for each generation per prompt, using mps friendly method
_, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
return prompt_embeds
def encode_prompt(
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
prompt: Union[str, List[str]],
num_videos_per_prompt: int = 1,
max_sequence_length: int = 226,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
text_input_ids=None,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
prompt_embeds = _get_t5_prompt_embeds(
tokenizer,
text_encoder,
prompt=prompt,
num_videos_per_prompt=num_videos_per_prompt,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
text_input_ids=text_input_ids,
)
return prompt_embeds
def compute_prompt_embeddings(
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
prompt: str,
max_sequence_length: int,
device: torch.device,
dtype: torch.dtype,
requires_grad: bool = False,
):
if requires_grad:
prompt_embeds = encode_prompt(
tokenizer,
text_encoder,
prompt,
num_videos_per_prompt=1,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
)
else:
with torch.no_grad():
prompt_embeds = encode_prompt(
tokenizer,
text_encoder,
prompt,
num_videos_per_prompt=1,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
)
return prompt_embeds
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