>>> uncond_input = tokenizer([""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt")
>>> uncond_embeddings = text_encoder(uncond_input.input_ids.to(torch_device))[0]
```  
Let's concatenate the conditional and unconditional embeddings into a batch to avoid doing two forward passes:  
```py
>>> text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
```