    original_image_embeds = image_mask * original_image_embeds That’s pretty much it! Aside from the differences described here, the rest of the script is very similar to the Text-to-image training script, so feel free to check it out for more details. 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’re happy with the changes to your script or if you’re okay with the default configuration, you’re ready to launch the training script! 🚀 This guide uses the fusing/instructpix2pix-1000-samples dataset, which is a smaller version of the original dataset. You can also create and use your own dataset if you’d like (see the Create a dataset for training guide). Set the MODEL_NAME environment variable to the name of the model (can be a model id on the Hub or a path to a local model), and the DATASET_ID to the name of the dataset on the Hub. The script creates and saves all the components (feature extractor, scheduler, text encoder, UNet, etc.) to a subfolder in your repository. For better results, try longer training runs with a larger dataset. We’ve only tested this training script on a smaller-scale dataset.  To monitor training progress with Weights and Biases, add the --report_to=wandb parameter to the training command and specify a validation image with --val_image_url and a validation prompt with --validation_prompt. This can be really useful for debugging the model. If you’re training on more than one GPU, add the --multi_gpu parameter to the accelerate launch command.   Copied accelerate launch --mixed_precision="fp16" train_instruct_pix2pix.py \