Update README.md
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@ -195,11 +195,11 @@ model = UNetModel.from_pretrained(model_id)
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scheduler = PNDMScheduler()
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# load model and scheduler
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ddpm = PNDM(unet=model, noise_scheduler=scheduler)
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pndm = PNDM(unet=model, noise_scheduler=scheduler)
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# run pipeline in inference (sample random noise and denoise)
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with torch.no_grad():
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image = ddpm()
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image = pndm()
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# process image to PIL
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image_processed = image.cpu().permute(0, 2, 3, 1)
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