ings = 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:


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>>> text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

Create random noise
	
Next, generate some initial random noise as a starting point for the diffusion process. This is the latent representation of the image, and it’ll be gradually denoised. A