caling_factor Then, the script applies dropout to the original image and edit instruction embeddings to support CFG. This is what enables the model to modulate the influence of the edit instruction and original image on the edited image.   Copied encoder_hidden_states = text_encoder(batch["input_ids"])[0]
original_image_embeds = vae.encode(batch["original_pixel_values"].to(weight_dtype)).latent_dist.mode()

if args.conditioning_dropout_prob is 