rch.cat([latents] * 2)

...     latent_model_input = scheduler.scale_model_input(latent_model_input, timestep=t)

...     # predict the noise residual
...     with torch.no_grad():
...         noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample

...     # perform guidance
...     noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
...     noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text 