from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-sdxl-custom-components", trust_remote_code=True)
assert pipeline.config.unet == ('diffusers_modules.local.my_unet_model', 'MyUNetModel')
assert pipeline.config.scheduler == ('diffusers_modules.local.my_scheduler', 'MyScheduler')
assert pipeline.__class__.__name__ == "StableDiffusionXLPipeline"
pipeline = pipeline.to(torch_device)
images = pipeline("test", num_inference_steps=2, output_type="np")[0]
assert images.shape == (1, 64, 64, 3)
