noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps) 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! 🚀 Let’s train on the Pokémon BLIP captions dataset to generate your own Pokémon. Set the environment variables MODEL_NAME and DATASET_NAME to the model and the dataset (either from the Hub or a local path). You should also specify a VAE other than the SDXL VAE (either from the Hub or a local path) with VAE_NAME to avoid numerical instabilities. 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 and --validation_epochs to the training command to keep track of results. This can be really useful for debugging the model and viewing intermediate results.   Copied export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"