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@@ -861,7 +861,6 @@ class Flux2Pipeline(DiffusionPipeline, Flux2LoraLoaderMixin):
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if output_type == "latent":
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image = latents
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else:
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torch.save({"pred": latents}, "pred_d.pt")
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latents = self._unpack_latents_with_ids(latents, latent_ids)
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latents_bn_mean = self.vae.bn.running_mean.view(1, -1, 1, 1).to(latents.device, latents.dtype)
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