    ].fill_(0)  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! 🚀 In this guide, you’ll download and use these example cat images. You can also create and use your own dataset if you want (see the Create a dataset for training guide). Set the environment variable MODEL_NAME to a model id on the Hub or a path to a local model, INSTANCE_DIR  to the path where you just downloaded the cat images to, and OUTPUT_DIR to where you want to save the model. You’ll use <new1> as the special word to tie the newly learned embeddings to. The script creates and saves model checkpoints and a pytorch_custom_diffusion_weights.bin file to your repository. To monitor training progress with Weights and Biases, add the --report_to=wandb parameter to the training command and specify a validation prompt with --validation_prompt. This is useful for debugging and saving intermediate results. If you’re training on human faces, the Custom Diffusion team has found the following parameters to work well: --learning_rate=5e-6 --max_train_steps can be anywhere between 1000 and 2000 --freeze_model=crossattn use at least 15-20 images to train with