els to a GPU since, unlike the scheduler, they have trainable weights:   Copied >>> torch_device = "cuda"
>>> vae.to(torch_device)
>>> text_encoder.to(torch_device)
>>> unet.to(torch_device)  Create text embeddings The next step is to tokenize the text to generate embeddings. The text is used to condition the UNet model and steer the diffusion process towards something that resembles the input prompt. 💡 The guidance_scale parameter determines h