finish pndm sampler
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@ -49,16 +49,6 @@ class PNDMPipeline(DiffusionPipeline):
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image = self.scheduler.step(model_output, t, image)["prev_sample"]
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# for i, t in enumerate(tqdm(self.scheduler.prk_timesteps)):
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# model_output = self.unet(image, t)["sample"]
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#
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# image = self.scheduler.step_prk(model_output, t, image, i=i)["prev_sample"]
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#
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# for i, t in enumerate(tqdm(self.scheduler.plms_timesteps)):
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# model_output = self.unet(image, t)["sample"]
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#
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# image = self.scheduler.step_plms(model_output, t, image, i=i)["prev_sample"]
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image = (image / 2 + 0.5).clamp(0, 1)
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image = image.cpu().permute(0, 2, 3, 1).numpy()
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if output_type == "pil":
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