1, 2) / 255.0
    return F.center_crop(image, (256, 256))

real_images = torch.cat([preprocess_image(image) for image in real_images])
print(real_images.shape)
# torch.Size([10, 3, 256, 256]) We now load the DiTPipeline to generate images conditioned on the above-mentioned classes.   Copied from diffusers import DiTPipeline, DPMSolverMultistepScheduler

dit_pipeline = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256", torch_dtype=torch.float1