Pretrained models in 🧨 Diffusers are easily created from their model class with the parameters you want. For example, to create a [`UNet2DModel`]:  
```py
>>> from diffusers import UNet2DModel

>>> model = UNet2DModel(
...     sample_size=config.image_size,  # the target image resolution
...     in_channels=3,  # the number of input channels, 3 for RGB images
...     out_channels=3,  # the number of output channels
...     layers_per_block=2,  # how many ResNet layers to use per UNet block
...     block_out_channels=(128, 128, 256, 256, 512, 512),  # the number of output channels for each UNet block
...     down_block_types=(
...         "DownBlock2D",  # a regular ResNet downsampling block
...         "DownBlock2D",
...         "DownBlock2D",
...         "DownBlock2D",
...         "AttnDownBlock2D",  # a ResNet downsampling block with spatial self-attention
...         "DownBlock2D",
...     ),
...     up_block_types=(
...         "UpBlock2D",  # a regular ResNet upsampling block
...         "AttnUpBlock2D",  # a ResNet upsampling block with spatial self-attention
...         "UpBlock2D",
...         "UpBlock2D",
...         "UpBlock2D",
...         "UpBlock2D",
...     ),
... )
```  
It is often a good idea to quickly check the sample image shape matches the model output shape:  
```py
>>> sample_image = dataset[0]["images"].unsqueeze(0)
>>> print("Input shape:", sample_image.shape)
Input shape: torch.Size([1, 3, 128, 128])

>>> print("Output shape:", model(sample_image, timestep=0).sample.shape)
Output shape: torch.Size([1, 3, 128, 128])
```  
Great! Next, you'll need a scheduler to add some noise to the image.
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/tutorials/basic_training.md