    return examples Finally, the training loop handles compressing the images to latent space with the EfficientNetEncoder, adding noise to the latents, and predicting the noise residual with the WuerstchenPrior model.   Copied pred_noise = prior(noisy_latents, timesteps, prompt_embeds) If you want to learn more about how the training loop works, check out the Understanding pipelines, models and schedulers tutorial which breaks down the basic pattern of the denoising process.  Launch the script Once you’ve made all your changes or you’re okay with the default configuration, you’re ready to launch the training script! 🚀 Set the DATASET_NAME environment variable to the dataset name from the Hub. This guide uses the Pokémon BLIP captions dataset, but you can create and train on your own datasets as well (see the Create a dataset for training guide). To monitor training progress with Weights & Biases, add the --report_to=wandb parameter to the training command. You’ll also need to add the --validation_prompt to the training command to keep track of results. This can be really useful for debugging the model and viewing intermediate results.   Copied export DATASET_NAME="lambdalabs/pokemon-blip-captions"