).predicted_image_embedding 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! 🚀 You’ll train on the Pokémon BLIP captions dataset to generate your own Pokémon, but you can also create and train on your own dataset by following the Create a dataset for training guide. Set the environment variable DATASET_NAME to the name of the dataset on the Hub or if you’re training on your own files, set the environment variable TRAIN_DIR to a path to your dataset. If you’re training on more than one GPU, add the --multi_gpu parameter to the accelerate launch command. 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. prior model decoder model    Copied export DATASET_NAME="lambdalabs/pokemon-blip-captions"