40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
import torch
|
|
from torch import nn
|
|
from transformers import T5EncoderModel
|
|
|
|
from opensora.utils.utils import get_precision
|
|
|
|
|
|
class T5Wrapper(nn.Module):
|
|
def __init__(self, args):
|
|
super(T5Wrapper, self).__init__()
|
|
self.model_name = args.text_encoder_name
|
|
dtype = get_precision(args)
|
|
t5_model_kwargs = {'cache_dir': './cache_dir', 'low_cpu_mem_usage': True, 'torch_dtype': dtype}
|
|
self.text_enc = T5EncoderModel.from_pretrained(self.model_name, **t5_model_kwargs).eval()
|
|
|
|
def forward(self, input_ids, attention_mask):
|
|
text_encoder_embs = self.text_enc(input_ids=input_ids, attention_mask=attention_mask)['last_hidden_state']
|
|
return text_encoder_embs.detach()
|
|
|
|
class CLIPWrapper(nn.Module):
|
|
def __init__(self, args, device, cache_dir='./cache_dir', **kwargs):
|
|
super(CLIPWrapper, self).__init__()
|
|
raise NotImplementedError
|
|
|
|
def forward(self, prompts): # prompts = ['I am a test caption', 'Test twice']
|
|
raise NotImplementedError
|
|
|
|
|
|
text_encoder = {
|
|
'DeepFloyd/t5-v1_1-xxl': T5Wrapper,
|
|
'clip': CLIPWrapper
|
|
}
|
|
|
|
|
|
def get_text_enc(args):
|
|
"""deprecation"""
|
|
text_enc = text_encoder.get(args.text_encoder_name, None)
|
|
assert text_enc is not None
|
|
return text_enc(args)
|