Fixed non-square highres fix generation
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@ -541,10 +541,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
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def create_dummy_mask(self, x):
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def create_dummy_mask(self, x, first_phase: bool = False):
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if self.sampler.conditioning_key in {'hybrid', 'concat'}:
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height = self.firstphase_height if first_phase else self.height
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width = self.firstphase_width if first_phase else self.width
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# The "masked-image" in this case will just be all zeros since the entire image is masked.
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image_conditioning = torch.zeros(x.shape[0], 3, self.height, self.width, device=x.device)
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image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
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image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning))
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# Add the fake full 1s mask to the first dimension.
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@ -567,7 +570,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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return samples
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x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
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samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x))
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samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, first_phase=True))
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samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
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