es[edit_prompt_column])
    examples["input_ids"] = tokenize_captions(captions)
    return examples Finally, in the training loop, it starts by encoding the edited images into latent space:   Copied latents = vae.encode(batch["edited_pixel_values"].to(weight_dtype)).latent_dist.sample()
latents = latents * vae.config.scaling_factor Then, the script applies dropout to the original image and edit instruction embeddings to support CFG. This is wha