print=new_fingerprint_for_vae,
) After calculating the embeddings, the text encoder, VAE, and tokenizer are deleted to free up some memory:   Copied del text_encoders, tokenizers, vae
gc.collect()
torch.cuda.empty_cache() Finally, the training loop takes care of the rest. If you chose to apply a timestep bias strategy, you’ll see the timestep weights are calculated and added as noise:   Copied weights = generate_timestep_weights(args, noise_sch