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d295ca1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | """CLIP text encoder for AeroGen. Uses transformers only (no ldm)."""
import torch.nn as nn
from transformers import CLIPTokenizer, CLIPTextModel
class AeroGenCLIPTextEncoder(nn.Module):
"""CLIP text encoder compatible with FrozenCLIPEmbedder interface.
Uses transformers CLIPTextModel + CLIPTokenizer. No ldm dependency.
"""
def __init__(self, version: str = "openai/clip-vit-large-patch14", device: str = "cuda", max_length: int = 77):
super().__init__()
self.tokenizer = CLIPTokenizer.from_pretrained(version)
self.transformer = CLIPTextModel.from_pretrained(version)
self.device = device
self.max_length = max_length
self.freeze()
def freeze(self):
self.transformer = self.transformer.eval()
for param in self.parameters():
param.requires_grad = False
def forward(self, text):
if isinstance(text, str):
text = [text]
batch_encoding = self.tokenizer(
text,
truncation=True,
max_length=self.max_length,
return_length=True,
return_overflowing_tokens=False,
padding="max_length",
return_tensors="pt",
)
device = next(self.parameters()).device
tokens = batch_encoding["input_ids"].to(device)
outputs = self.transformer(input_ids=tokens)
return outputs.last_hidden_state
def encode(self, text):
return self(text)
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