eddings = text_encoder(text_input.input_ids.to(torch_device))[0] You’ll also need to generate the unconditional text embeddings which are the embeddings for the padding token. These need to have the same shape (batch_size and seq_length) as the conditional text_embeddings:   Copied >>> max_length = text_input.input_ids.shape[-1]
>>> uncond_input = tokenizer([""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt")
>>>