mbeddings for the padding token. These need to have the same shape (batch_size and seq_length) as the conditional text_embeddings:


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>>> max_length = text_input.input_ids.shape[-1]
>>> 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 i