>>> model = UNet2DModel.from_pretrained(repo_id, use_safetensors=True) To access the model parameters, call model.config:   Copied >>> model.config The model configuration is a 🧊 frozen 🧊 dictionary, which means those parameters can’t be changed after the model is created. This is intentional and ensures that the parameters used to define the model architecture at the start remain the same, while other parameters can still be adjusted during inference. Some of the most important parameters are: sample_size: the height and width dimension of the input sample. in_channels: the number of input channels of the input sample. down_block_types and up_block_types: the type of down- and upsampling blocks used to create the UNet architecture. block_out_channels: the number of output channels of the downsampling blocks; also used in reverse order for the number of input channels of the upsampling blocks. layers_per_block: the number of ResNet blocks present in each UNet block. To use the model for inference, create the image shape with random Gaussian noise. It should have a batch axis because the model can receive multiple random noises, a channel axis corresponding to the number of input channels, and a sample_size axis for the height and width of the image:   Copied >>> import torch