Fix latent upscale highres fix #3888
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@ -653,6 +653,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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if opts.use_scale_latent_for_hires_fix:
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samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
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image_conditioning = self.txt2img_image_conditioning(samples)
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else:
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decoded_samples = decode_first_stage(self.sd_model, samples)
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@ -674,6 +675,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
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image_conditioning = self.img2img_image_conditioning(
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decoded_samples,
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samples,
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decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3])
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)
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shared.state.nextjob()
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self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
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@ -684,11 +691,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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x = None
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devices.torch_gc()
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image_conditioning = self.img2img_image_conditioning(
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decoded_samples,
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samples,
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decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3])
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)
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samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning)
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return samples
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