orch.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.float16)
dit_pipeline.scheduler = DPMSolverMultistepScheduler.from_config(dit_p