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this time for sure
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@ -538,8 +538,12 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
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return x
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class DecodedSamples(list):
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already_decoded = True
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def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
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samples = []
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samples = DecodedSamples()
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for i in range(batch.shape[0]):
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sample = decode_first_stage(model, batch[i:i + 1])[0]
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@ -793,7 +797,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
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samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
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x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
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if getattr(samples_ddim, 'already_decoded', False):
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x_samples_ddim = samples_ddim
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else:
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x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
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x_samples_ddim = torch.stack(x_samples_ddim).float()
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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@ -1161,9 +1169,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
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decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
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self.is_hr_pass = False
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return samples
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return decoded_samples
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def close(self):
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super().close()
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