70 lines
2.6 KiB
Python
70 lines
2.6 KiB
Python
import torch
|
|
from torch import nn
|
|
from transformers import CLIPModel, CLIPProcessor
|
|
|
|
from opensora.utils.utils import get_precision
|
|
|
|
|
|
class T5Wrapper(nn.Module):
|
|
def __init__(self, args, **kwargs):
|
|
super(T5Wrapper, self).__init__()
|
|
self.model_name = args.text_encoder_name
|
|
if 'mt5' in self.model_name:
|
|
from transformers import MT5EncoderModel
|
|
self.text_enc = MT5EncoderModel.from_pretrained(self.model_name, cache_dir=args.cache_dir, **kwargs).eval()
|
|
|
|
elif 't5' in self.model_name:
|
|
from transformers import T5EncoderModel
|
|
self.text_enc = T5EncoderModel.from_pretrained(self.model_name, cache_dir=args.cache_dir, **kwargs).eval()
|
|
# self.text_enc = T5EncoderModel.from_pretrained("/storage/ongoing/new/Open-Sora-Plan/cache_dir/models--DeepFloyd--t5-v1_1-xxl/snapshots/c9c625d2ec93667ec579ede125fd3811d1f81d37", cache_dir=args.cache_dir, **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):
|
|
super(CLIPWrapper, self).__init__()
|
|
self.model_name = args.text_encoder_name
|
|
dtype = get_precision(args)
|
|
model_kwargs = {'cache_dir': args.cache_dir, 'low_cpu_mem_usage': True, 'torch_dtype': dtype}
|
|
self.text_enc = CLIPModel.from_pretrained(self.model_name, **model_kwargs).eval()
|
|
|
|
def forward(self, input_ids, attention_mask):
|
|
text_encoder_embs = self.text_enc.get_text_features(input_ids=input_ids, attention_mask=attention_mask)
|
|
return text_encoder_embs.detach()
|
|
|
|
|
|
|
|
text_encoder = {
|
|
'google/mt5-xl': T5Wrapper,
|
|
'google/mt5-xxl': T5Wrapper,
|
|
'google/umt5-xl': T5Wrapper,
|
|
'google/umt5-xxl': T5Wrapper,
|
|
'DeepFloyd/t5-v1_1-xxl': T5Wrapper,
|
|
'openai/clip-vit-large-patch14': CLIPWrapper
|
|
}
|
|
|
|
|
|
def get_text_enc(args):
|
|
"""deprecation"""
|
|
encoder_key = None
|
|
for key in text_encoder.keys():
|
|
if key in args.text_encoder_name:
|
|
encoder_key = key
|
|
break
|
|
text_enc = text_encoder.get(encoder_key, None)
|
|
assert text_enc is not None
|
|
return text_enc(args)
|
|
|
|
def get_text_warpper(text_encoder_name):
|
|
"""deprecation"""
|
|
encoder_key = None
|
|
for key in text_encoder.keys():
|
|
if key in text_encoder_name:
|
|
encoder_key = key
|
|
break
|
|
text_enc = text_encoder.get(encoder_key, None)
|
|
assert text_enc is not None
|
|
return text_enc
|