Spaces:
Running
on
Zero
Running
on
Zero
Update live_preview_helpers.py
Browse files- live_preview_helpers.py +1 -2
live_preview_helpers.py
CHANGED
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@@ -148,14 +148,13 @@ def flux_pipe_call_that_returns_an_iterable_of_images(
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joint_attention_kwargs=self.joint_attention_kwargs,
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return_dict=False,
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)[0]
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latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
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# Yield intermediate result
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latents_for_image = self._unpack_latents(latents, height, width, self.vae_scale_factor)
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latents_for_image = (latents_for_image / self.vae.config.scaling_factor) + self.vae.config.shift_factor
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image = self.vae.decode(latents_for_image, return_dict=False)[0]
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yield self.image_processor.postprocess(image, output_type=output_type)[0]
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torch.cuda.empty_cache()
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# Final image using good_vae
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latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
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joint_attention_kwargs=self.joint_attention_kwargs,
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return_dict=False,
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)[0]
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# Yield intermediate result
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latents_for_image = self._unpack_latents(latents, height, width, self.vae_scale_factor)
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latents_for_image = (latents_for_image / self.vae.config.scaling_factor) + self.vae.config.shift_factor
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image = self.vae.decode(latents_for_image, return_dict=False)[0]
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yield self.image_processor.postprocess(image, output_type=output_type)[0]
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torch.cuda.empty_cache()
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latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
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# Final image using good_vae
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latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
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